Thronebreaker: The Witcher Tales Now Available For Nintendo Switch!
Thronebreaker is a single player role-playing game that combines narrative-driven exploration with unique puzzles and turn-based battles, and spins the tale of Meve, war-veteran queen of two Northern Realms — Lyria and Rivia. Facing an imminent Nilfgaardian invasion, Meve is forced to once again enter the warpath and set out on a dark journey of destruction and revenge.
A set of free digital goodies comes with Thronebreaker, including the official soundtrack, concept art from the game, as well as an annotated map of Lyria. Details on how to claim these goodies can be found on the dedicated website.
Ported to the Nintendo Switch by Crunching Koalas, in close cooperation with CD PROJEKT RED, Thronebreaker: The Witcher Tales can be purchased right now from the Nintendo eShop. The title is also available on GOG.COM, Steam, as well as PlayStation 4 and Xbox One. For more information regarding the game, visit thewitcher.com/thronebreaker.
Granblue Fantasy Versus Free Download
Now Cygames has partnered with Arc System Works, known for such popular fighting franchises as GUILTY GEAR and BlazBlue, to bring Granblue Fantasy to the world of fighting games, complete with top-notch game design and one-of-a-kind 3D graphics.
GAMEPLAY AND SCREENSHOTS
♢ Click or choose only one button below to download this game.
♢ View detailed instructions for downloading and installing the game here.
♢ Use 7-Zip to extract RAR, ZIP and ISO files. Install PowerISO to mount ISO files.
Granblue Fantasy Versus Free Download
INSTRUCTIONS FOR THIS GAME
➤ Download the game by clicking on the button link provided above.
➤ Download the game on the host site and turn off your Antivirus or Windows Defender to avoid errors.
➤ Once the download has been finished or completed, locate or go to that file.
➤ To open .iso file, use PowerISO and run the setup as admin then install the game on your PC.
➤ Once the installation process is complete, run the game's exe as admin and you can now play the game.
➤ Congratulations! You can now play this game for free on your PC.
➤ Note: If you like this video game, please buy it and support the developers of this game.
SYSTEM REQUIREMENTS:
(Your PC must at least have the equivalent or higher specs in order to run this game.)
Minimum:
• OS: Windows 7/8/10 (64-bit OS required)
• Processor: AMD FX-4350, 4.2 GHz / Intel Core i5-3470, 3.20 GHz
• Memory: 4 GB RAM
• Graphics: Radeon HD 6870, 1 GB / GeForce GTX 650 Ti, 1 GB
• DirectX: Version 11
• Network: Broadband Internet connection
• Storage: 7 GB available space
• Sound Card: DirectX compatible soundcard or onboard chipset
Recommended:
• OS: Windows 7/8/10 (64-bit OS required)
• Processor: AMD Ryzen 5 1400, 3.2 GHz / Intel Core i7-3770, 3.40 GHz
• Memory: 8 GB RAM
• Graphics: Radeon HD 7870, 2 GB / GeForce GTX 660, 2 GB
• DirectX: Version 11
• Network: Broadband Internet connection
• Storage: 7 GB available space
• Sound Card: DirectX compatible soundcard or onboard chipset
Supported Language: English, French, Italian, German, Spanish, Polish, Czech, Russian, Hungarian, Dutch, Danish, Portuguese, Finnish, Norwegian, Swedish, Korean, and Simplified Chinese language are available.
• OS: Windows 7/8/10 (64-bit OS required)
• Processor: AMD FX-4350, 4.2 GHz / Intel Core i5-3470, 3.20 GHz
• Memory: 4 GB RAM
• Graphics: Radeon HD 6870, 1 GB / GeForce GTX 650 Ti, 1 GB
• DirectX: Version 11
• Network: Broadband Internet connection
• Storage: 7 GB available space
• Sound Card: DirectX compatible soundcard or onboard chipset
Recommended:
• OS: Windows 7/8/10 (64-bit OS required)
• Processor: AMD Ryzen 5 1400, 3.2 GHz / Intel Core i7-3770, 3.40 GHz
• Memory: 8 GB RAM
• Graphics: Radeon HD 7870, 2 GB / GeForce GTX 660, 2 GB
• DirectX: Version 11
• Network: Broadband Internet connection
• Storage: 7 GB available space
• Sound Card: DirectX compatible soundcard or onboard chipset
Games Course Alumni Shares His Top Tips On Art Projects.
A big 'Thank you' goes out to our Alumni, David Woodman, who has shared some of his top art tips on ArtStation for our students to learn from his many years of experience as a 3D artist and Art Director in Research and Development at TT-Games!
Top Tips.
You can also see examples of David's work n his ArtStation portfolio.
Top Tips.
You can also see examples of David's work n his ArtStation portfolio.
The Battle For Light Rock Valley
Where to start? This game could have made a great Newport Noodle article or made for a good long blow by blow post describing how the rules worked turn by turn and what affect they had on each player decision but that might have been as tedious to read as to write. So I'll quickly summarize the game in the picture captions while writing the post about the rules and how they worked in the game.
The scenario, which I picked as a nod to the origin of the turn sequence which came from one of the sample games in Don Featherstone's Battles with Model Soldiers, was, as identified by Cesar Paz in a comment, "Action in the Plattville Valley" from Don Featherstone's Wargames.
Each side has 3 infantry Brigades, a cavalry brigade and 2 guns. (I also threw in a field hospital on each side though these weren't really ready to be seen in public at the moment, being in the process of being renovated.) One infantry brigade on each side moves down the road on turn One. These troops can do what the player wants for turns Two and Three and at the end of turn Three all the remaining forces arrive anywhere on the baseline. The objective is to "control' the valley by the end of the gaming day without being more specific. I've gotten into the habit of playing 15 turns (thank you Mr Thomas) but with my usual initiative/chance card deck meaning that could be shortened by 1 turn for each joker which shows up, which one did today.
My brigades were each made up of 4 units plus a Brigadier, giving a total of 18 units per side with an army break point of 9 units lost. Once again it was just right. I got interrupted twice but the game took somewhere between 1.5 about 2 hours to play.
Two of the main ideas behind the rules were that they should focus on the General's decisions and the role of Brigade commanders, not the minutiae of battalion tactics and be grid friendly rather than grid dependent. The last part was easy since a few years ago I had made measuring sticks with 3" bands painted on for use with a set of rules calling for measurement in "lengths" so all measurements were made as multiples of "3" and thus 'one unit' of measurement can be 3", 3 cm., 1 grid area of any size and shape, or 1 "band" on one of my sticks.
The near abandonment of almost all unit tactical detail was harder, I don't think I'd have gotten there without having played Volley & Bayonet in the late '90's followed not long after by Morschauser and then by all sorts of new designs especially the various gridded games from Bob Cordery's The Portable Wargame to Battlecry and its descendants and having recognized the possibility of including some simple rules to allow the effect or feel of such tactics without showing them or taking up too much time and attention away from the army commander.
The orders, of course, are essentially a variation of the original DBA command system which I plucked out of a pre-publication article in Slingshot in the early '80's and have used on and off since!
Basically I'm not sure there are any original ideas in here but it feels like a different blend to me and more important, so far, it seems to be providing the kind of game I've been looking for for most of the last two decades although my Morschauser Meets MacDuff/ later renamed Hearts of Tin set of rules was close, at least until I started upping the detail!
Its pretty unusual for me to make it through a play test without wanting to make changes but, perhaps because none of the individual rules were completely new, this time there was nothing I felt an itch to improve except that I realized I hadn't really specified what a unit or Brigadier without orders could do. For units its essentially that they can mount/dismount etc and change facing but I also had intended to allow them to retreat without orders from enemy within 3". For Brigadiers, I had intended that they could rearrange their brigades without orders, bring forward reserves, retire units near the break point, refuse a flank etc.. They will also have the right to withdraw if closely engaged as above, subject to possible courtmartial if things go badly of course. But I won't allow any heroic unordered attacks except perhaps as a Chance cards event. It was tempting to bring back a control test for out of command commanders but it had to be EITHER the single command roll OR a series of individual rolls. Past experience has shown that using both in the same game provides too much overhead and the control check tends to override the orders dice making it superfluous and that all the individual command rolls slow the game too much compared to the single one and require additional rules to encourage players to maintain multi-unit formations .
For the rest, I really enjoyed how the game played. It was best to think at least 2 turns ahead, be prepared to use high command rolls but to not count on getting them, nor to count on combat dice. I was a little concerned early on that Blue would break Red's army by the 1/2 way mark given their hot shooting and Red's sudden inability to hit a barn door. However, with my Red hat on and a coffee break, I forced myself to give up a very promising, if rash and accidentally poorly supported attack, and pull back while I desperately tried to stay alive and think of a new one. Just at the danger point, Blue's dice failed him. All of a sudden he had trouble getting his brigades moving and while the firefight continued the losses soon started to even out. Still, Red was in a pickle, twice the number of units lost in the firefight and his artillery losing the duel, Blue's left was in cover and his right was already, if belatedly, moving forward to finish off the two already shot up brigades.
Suddenly I noticed that Red's cavalry was within 2 moves of the flank of Blue's artillery and Blue's supports had moved away, some to pursue Red's retreating left, some to help defend the town. Blue could probably get some supports back or withdraw his guns but at least it would ease the pressure. Blue went first and a rolled a 1 for command! ARGHH. None of his infantry was able to come up and enemy way their fire helped by the artillery had almost silenced Red's batteries. Most of the cavalry was too far away to mount up and get back but he had one mounted unit in reserve. He ordered it to move to support the guns and turned one gun towards the open flank. Red moved his cavalry forward. Next turn, Red got first go. What!? Yup, ample orders, it was a long shot for the cavalry to wipe out the fresh batteries but better than nothing.
The trumpets sounded and the Gentlemen Pensioners in their cuirasses and plumed helmets trotted forward. The gun decided to hold fire while the other gun finished silencing one enemy gun. The cavalry picked up speed and the cannon fired........getting 1 hit on 4 dice.....the cavalry rolled, 4 hits! The gun was over run and the cavalry pursued into the flank of the second gun and over rode it as well. Then I noticed that Red's cavalry had had to ride over Blue's general during the pursuit. My rule there is that both sides' dice off and the commander escapes if he rolls a tie or higher. He rolled 1, they rolled 6! The "old fellers" were having a good day!
Oh dear. Who said you can't surprise yourself? I didn't think the charge would work that well but if I had noticed the General there on the previous turn when I was trying to prepare for the charge, I would have moved him anyway, just to be safe! So suddenly Blue was down 3 army morale points and had a -1 to his orders dice for the rest of the game. That was to became a problem for him.
Well, balance in all things. Red's less than all out attack on the town led to near equal losses and ended with both sides hiding in 1/2 the town, sniping at the other side. The focus switched back where Blue's best chance was to pursue and break Red's worn brigades. Unfortunately for Blue, low command rolls with a minus 1 on top and the need to manage the fight in the village meant that the pursuit was slow, the more so since the Blue units were battered so took advantage of a slightly more circuitous route to avoid artillery fire on their approach. In the meantime Red's hospital finally started getting some of the early casualties back into the fight and by the time the firefight resumed, the sides were equal. Red's men were firing more steadily now though and had artillery support. Eventually Blue's units started nearing their break point and he pulled back, going for the draw. Red pursued at first but didn't dare risk a charge with his battered units. He kept getting hits but was taking them too. Soon both armies were down to 1 hit and 2 hits respectively from their Army Morale point. There was momentary jubilation in Red's ranks when they got that last hit but the brigadier rode in and risked a rally roll: 5,6 = cancel 1 hit, 1=Commander dies. Hit saved, next turn Blue took another hit and again rallied it while Red lost a unit (Apparently Brigadier Ross is less charismtic!). That was it! A Bloody draw.
So, work has begun on the four or five page version of the rules, adding explanations as well as more unit types, irregulars, boats, trains, engineers, etc etc. All the usual. It will need more testing as well but so far I'm pretty happy with how closely they resemble in play the vision that has been trying to get out of my brain for years. Time and playing will tell.
The scenario, which I picked as a nod to the origin of the turn sequence which came from one of the sample games in Don Featherstone's Battles with Model Soldiers, was, as identified by Cesar Paz in a comment, "Action in the Plattville Valley" from Don Featherstone's Wargames.
 |
| The Dominion (Red) Advance Guard under Brigadier Ross pushed quickly over the bridge while the main forces arrived at the end of turn 3 (after a joker froze the 2 advance guards for turn 3). Alas for the young volunteers in the Dominion advance guard they were facing the crack shots and stubborn veterans of the oldest brigade in the Rebel (or Origawn Freestate) army. Both armies have been tasked with taking control of the valley. I decided this meant either controlling the bridge and the town and hills or more likely, breaking the enemy's morale and forcing him to retreat. |
My brigades were each made up of 4 units plus a Brigadier, giving a total of 18 units per side with an army break point of 9 units lost. Once again it was just right. I got interrupted twice but the game took somewhere between 1.5 about 2 hours to play.
 |
| The firefight across the river raged for several turns but with Ross's Brigade down to half strength he felt compelled to order the remnants of his brigade to retreat behind the cover of the ridge. |
The near abandonment of almost all unit tactical detail was harder, I don't think I'd have gotten there without having played Volley & Bayonet in the late '90's followed not long after by Morschauser and then by all sorts of new designs especially the various gridded games from Bob Cordery's The Portable Wargame to Battlecry and its descendants and having recognized the possibility of including some simple rules to allow the effect or feel of such tactics without showing them or taking up too much time and attention away from the army commander.
The orders, of course, are essentially a variation of the original DBA command system which I plucked out of a pre-publication article in Slingshot in the early '80's and have used on and off since!
Basically I'm not sure there are any original ideas in here but it feels like a different blend to me and more important, so far, it seems to be providing the kind of game I've been looking for for most of the last two decades although my Morschauser Meets MacDuff/ later renamed Hearts of Tin set of rules was close, at least until I started upping the detail!
 |
| "There's something wrong with our bloody troops today". All along the line the Rebel guns and infantry were dishing out more than they were taking. With heavy casualties, the Dominion infantry pulled back into dead ground while MacDuff's Highlanders were ordered forward to storm the town, held largely by dismounted cavalry , while the cavalry were ordered forward to threaten the enemy's guns. It must have been the powder smoke (or possibly a low Blue command roll followed by a flip in the initiative sequence) but the Red cavalry manged to catch the enemy artillery in flank, rode over the first gun and into then into the second catching General Lannigan and his staff before they even knew they were in danger. (The Lifeguards dice were HOT! and I honestly did not see the danger coming at the start of the previous turn or I would have shifted him then, just in case! ) To make matter worse Reds's artillery and the Greandiers hit Grey's Brigade hard before the Grenadiers retired. In a flash the situation was changed. Blue's morale was now lower than Red's and they were going to have trouble with command. |
 |
| Never say die! As next senior officer, General Byrd took command, pulled back his battered cavalry to hold the center and be prepared to cover a retreat (ie don't get shot up worse and break army morale). This left the Highlanders in possession of half the village but they'd lost heavily while doing so and were in no shape to finish the job. Both armies were on the edge of breaking but Byrd wasn't one to settle for a draw if victory was even a remote possibility. He led his brigade forward to finish their attack. |
Suddenly I noticed that Red's cavalry was within 2 moves of the flank of Blue's artillery and Blue's supports had moved away, some to pursue Red's retreating left, some to help defend the town. Blue could probably get some supports back or withdraw his guns but at least it would ease the pressure. Blue went first and a rolled a 1 for command! ARGHH. None of his infantry was able to come up and enemy way their fire helped by the artillery had almost silenced Red's batteries. Most of the cavalry was too far away to mount up and get back but he had one mounted unit in reserve. He ordered it to move to support the guns and turned one gun towards the open flank. Red moved his cavalry forward. Next turn, Red got first go. What!? Yup, ample orders, it was a long shot for the cavalry to wipe out the fresh batteries but better than nothing.
The trumpets sounded and the Gentlemen Pensioners in their cuirasses and plumed helmets trotted forward. The gun decided to hold fire while the other gun finished silencing one enemy gun. The cavalry picked up speed and the cannon fired........getting 1 hit on 4 dice.....the cavalry rolled, 4 hits! The gun was over run and the cavalry pursued into the flank of the second gun and over rode it as well. Then I noticed that Red's cavalry had had to ride over Blue's general during the pursuit. My rule there is that both sides' dice off and the commander escapes if he rolls a tie or higher. He rolled 1, they rolled 6! The "old fellers" were having a good day!
Oh dear. Who said you can't surprise yourself? I didn't think the charge would work that well but if I had noticed the General there on the previous turn when I was trying to prepare for the charge, I would have moved him anyway, just to be safe! So suddenly Blue was down 3 army morale points and had a -1 to his orders dice for the rest of the game. That was to became a problem for him.
 |
| Red's field hospital had been working hard though and Ross's battered units and the Grenadiers were sufficiently recovered to make it an even fight. Twice Byrd had to ride amongst his men and rally them to hold together as he reluctantly led them back, fighting every step of the way but at last it was managed as the sun set. A truce was arranged for the collection of the wounded while the two armies camped on their respective sides of the valley and contemplated their next move. (In game terms, by turn 14/15, both armies were 1 unit loss away from breaking with several units only 1 hit from being broken. On each of those two last turns Red inflicted a hit on a Blue unit which would have broken the unit and the army but each time Byrd rallied it: (5,6=cancel hit, 1=commander shot). Red was also down to being able to lose 2 units, lost one on turn 14 but none of turn 15 despite having several under fire and only 1 hit away from breaking. Any victory for either side would have been Pyrrhic at best. |
Well, balance in all things. Red's less than all out attack on the town led to near equal losses and ended with both sides hiding in 1/2 the town, sniping at the other side. The focus switched back where Blue's best chance was to pursue and break Red's worn brigades. Unfortunately for Blue, low command rolls with a minus 1 on top and the need to manage the fight in the village meant that the pursuit was slow, the more so since the Blue units were battered so took advantage of a slightly more circuitous route to avoid artillery fire on their approach. In the meantime Red's hospital finally started getting some of the early casualties back into the fight and by the time the firefight resumed, the sides were equal. Red's men were firing more steadily now though and had artillery support. Eventually Blue's units started nearing their break point and he pulled back, going for the draw. Red pursued at first but didn't dare risk a charge with his battered units. He kept getting hits but was taking them too. Soon both armies were down to 1 hit and 2 hits respectively from their Army Morale point. There was momentary jubilation in Red's ranks when they got that last hit but the brigadier rode in and risked a rally roll: 5,6 = cancel 1 hit, 1=Commander dies. Hit saved, next turn Blue took another hit and again rallied it while Red lost a unit (Apparently Brigadier Ross is less charismtic!). That was it! A Bloody draw.
So, work has begun on the four or five page version of the rules, adding explanations as well as more unit types, irregulars, boats, trains, engineers, etc etc. All the usual. It will need more testing as well but so far I'm pretty happy with how closely they resemble in play the vision that has been trying to get out of my brain for years. Time and playing will tell.
Tech Book Face Off: Data Smart Vs. Python Machine Learning
After reading a few books on data science and a little bit about machine learning, I felt it was time to round out my studies in these subjects with a couple more books. I was hoping to get some more exposure to implementing different machine learning algorithms as well as diving deeper into how to effectively use the different Python tools for machine learning, and these two books seemed to fit the bill. The first book with the upside-down face, Data Smart: Using Data Science to Transform Data Into Insight by John W. Foreman, looked like it would fulfill the former goal and do it all in Excel, oddly enough. The second book with the right side-up face, Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow by Sebastian Raschka and Vahid Mirjalili, promised to address the second goal. Let's see how these two books complement each other and move the reader toward a better understanding of machine learning.
It was refreshing to read a book about data science that didn't take itself seriously and added a bit of levity to an otherwise dry (interesting, but dry) subject. Even though it was lighthearted, the book was not a joke. It had an intensity to the material that was surprising given the medium through which it was presented. Spreadsheets turned out to be a great way to show how these algorithms are built up, and you can look through the columns and rows to see how each step of each calculation is performed. Conditional formatting helps guide understanding by highlighting outliers and important contrasts in the rows of data. Excel may not be the best choice for crunching hundreds of thousands of entries in an industrial-scale model, but for learning how those models actually work, I'm convinced that it was a worthy choice.
The book starts out with a little introduction that describes what you got yourself into and justifies the choice of Excel for those of us that were a bit leery. The first chapter gives a quick tour of the important parts of Excel that are going to be used throughout the book—a skim-worthy chapter. The first real chapter jumps into explaining how to build up a k-means cluster model for the highly critical task of grouping people on a middle school dance floor. Like most of the rest of the chapters, this one starts out easy, but ramps up the difficulty so that by the end we're clustering subscribers for email marketing with a dozen or so dimensions to the data.
Chapter 3 switches gears from an unsupervised to a supervised learning model with naïve Bayes for classifying tweets about Mandrill the product vs. the animal vs. the Mega Man X character. Here we can see how irreverent, but on-point Foreman is with his explanations:
After optimization came another dive into cluster analysis, but this time using network graphs to analyze wholesale wine purchasing data. This model was new to me, and a fascinating way to use graphs to figure out closely related nodes. The next chapter moved on to regression, both linear and non-linear varieties, and this happens to be the Target-pregnancy example. It was super interesting to see how to conform the purchasing data to a linear model and then run the regression on it to analyze the data. Foreman also had some good advice tucked away in this chapter on data vs. models:
The next chapter was on detecting outliers. In this case, the outliers were exceptionally good or exceptionally bad call center employees even though the bad employees didn't fall below any individual firing thresholds on their performance ratings. It was another excellent example to cap off a whole series of very well thought out and well executed examples. There was one more chapter on how to do some of these models in R, but I skipped it. I'm not interested in R, since I would just use Python, and this chapter seemed out of place with all the spreadsheet work in the rest of the book.
What else can I say? This book was awesome. Every example of every model was deep, involved, and appropriate for learning the ins and outs of that particular model. The writing was funny and engaging, and it was clear that Foreman put a ton of thought and energy into this book. I highly recommend it to anyone wanting to learn the inner workings of some of the standard data science models.
As long as you're comfortable with linear algebraic notation, this book is a straightforward read. It's not exactly easy, but it never takes off into the stratosphere with the difficulty level. The authors also assume you already know Python, so they don't waste any time on the language, instead packing the book completely full of machine learning stuff. The shorter first chapter still does the introductory tour of what machine learning is and how to install the correct Python environment and libraries that will be used in the rest of the book. The next chapter kicks us off with our first algorithm, showing how to implement a perceptron classifier as a mathematical model, as Python code, and then using scikit-learn. This basic sequence is followed for most of the algorithms in the book, and it works well to smooth out the reader's understanding of each one. Model performance characteristics, training insights, and decisions about when to use the model are highlighted throughout the chapter.
Chapter 3 delves deeper into perceptrons by looking at different decision functions that can be used for the output of the perceptron model, and how they could be used for more things beyond just labeling each input with a specific class as described here:
The next chapter switches gears and looks at how to generate good training sets with data preprocessing, and how to train a model effectively without overfitting using regularization. Regularization is a way to systematically penalize the model for assigning large weights that would lead to memorizing the training data during training. Another way to avoid overfitting is to use ensemble learning with a model like random forests, which are introduced in this chapter as well. The following chapter looks at how to do dimensionality reduction, both unsupervised with principal component analysis (PCA) and supervised with linear discriminant analysis (LDA).
Chapter 6 comes back to how to train your dragon…I mean model…by tuning the hyperparameters of the model. The hyperparameters are just the settings of the model, like what its decision function is or how fast its learning rate is. It's important during this tuning that you don't pick hyperparameters that are just best at identifying the test set, as the authors explain:
Chapter 10 covers regression analysis in more depth with single and multiple linear and nonlinear regression. Some of this stuff has been seen in previous chapters, and indeed, the cross-referencing starts to get a bit annoying at this point. Every single time a topic comes up that's covered somewhere else, it gets a reference with the full section name attached. I'm not sure how I feel about this in general. It's nice to be reminded of things that you've read about hundreds of pages back and I've read books that are more confusing for not having done enough of this linking, but it does get tedious when the immediately preceding sections are referenced repeatedly. The next chapter is similar with a deeper look at unsupervised clustering algorithms. The new k-means algorithm is introduced, but it's compared against algorithms covered in chapter 3. This chapter also covers how we can decide if the number of clusters chosen is appropriate for the data, something that's not so easy for high-dimensional data.
Now that we're two-thirds of the way through the book, we come to the elephant in the machine learning room, the multilayer artificial neural network. These networks are built up from perceptrons with various activation functions:
The last two chapters look at two more types of neural networks: the deep convolutional neural network (CNN) and the recurrent neural network (RNN). The CNN does the same hand-written digit classification as before, but of course does it better. The RNN is a network that's used for sequential and time-series data, and in this case, it was used in two examples. The first example was another implementation of the sentiment analyzer for IMDb movie reviews, and it ended up performing similarly to the regression classifier that we used back in chapter 8. The second example was for how to train an RNN with Shakespeare's Hamlet to generate similar text. It sounds cool, but frankly, it was pretty disappointing for the last example of the most complicated network in a machine learning book. It generated mostly garbage and was just a let-down at the end of the book.
Even though this book had a few issues, like tedious code duplication and explanations in places, the annoying cross-referencing, and the out-of-place chapter 9, it was a solid book on machine learning. I got a ton out of going through the implementations of each of the machine learning algorithms, and wherever the topics started to stray into more in-depth material, the authors provided references to the papers and textbooks that contained the necessary details. Python Machine Learning is a solid introductory text on the fundamental machine learning algorithms, both in how they work mathematically how they're implemented in Python, and how to use them with scikit-learn and TensorFlow.
Of these two books, Data Smart is a definite-read if you're at all interested in data science. It does a great job of showing how the basic data analysis algorithms work using the surprisingly effect method of laying out all of the calculations in spreadsheets, and doing it with good humor. Python Machine Learning is also worth a look if you want to delve into machine learning models, see how they would be implemented in Python, and learn how to use those same models effectively with scikit-learn and TensorFlow. It may not be the best book on the topic, but it's a solid entry and covers quite a lot of material thoroughly. I was happy with how it rounded out my knowledge of machine learning.
 | VS. |  |
Data Smart
I must admit; I was somewhat hesitant to get this book. I was worried that presenting everything in Excel would be a bit too simple to really learn much about data science, but I needn't have been concerned. This book was an excellent read for multiple reasons, not least of which is that Foreman is a highly entertaining writer. His witty quips about everything from middle school dances to Target predicting teen pregnancies were a great motivator to keep me reading along, and more than once I caught myself chuckling out loud at an unexpectedly absurd reference.It was refreshing to read a book about data science that didn't take itself seriously and added a bit of levity to an otherwise dry (interesting, but dry) subject. Even though it was lighthearted, the book was not a joke. It had an intensity to the material that was surprising given the medium through which it was presented. Spreadsheets turned out to be a great way to show how these algorithms are built up, and you can look through the columns and rows to see how each step of each calculation is performed. Conditional formatting helps guide understanding by highlighting outliers and important contrasts in the rows of data. Excel may not be the best choice for crunching hundreds of thousands of entries in an industrial-scale model, but for learning how those models actually work, I'm convinced that it was a worthy choice.
The book starts out with a little introduction that describes what you got yourself into and justifies the choice of Excel for those of us that were a bit leery. The first chapter gives a quick tour of the important parts of Excel that are going to be used throughout the book—a skim-worthy chapter. The first real chapter jumps into explaining how to build up a k-means cluster model for the highly critical task of grouping people on a middle school dance floor. Like most of the rest of the chapters, this one starts out easy, but ramps up the difficulty so that by the end we're clustering subscribers for email marketing with a dozen or so dimensions to the data.
Chapter 3 switches gears from an unsupervised to a supervised learning model with naïve Bayes for classifying tweets about Mandrill the product vs. the animal vs. the Mega Man X character. Here we can see how irreverent, but on-point Foreman is with his explanations:
Because naïve Bayes is often called "idiot's Bayes." As you'll see, you get to make lots of sloppy, idiotic assumptions about your data, and it still works! It's like the splatter-paint of AI models, and because it's so simple and easy to implement (it can be done in 50 lines of code), companies use it all the time for simple classification jobs.Every chapter is like this and better. You never know what Foreman's going to say next, but you quickly expect it to be entertaining. Case in point, the next chapter is on optimization modeling using an example of, what else, commercial-scale orange juice mixing. It's just wild; you can't make this stuff up. Well, Foreman can make it up, it seems. The examples weren't just whimsical and funny, they were solid examples that built up throughout the chapter to show multiple levels of complexity for each model. I was constantly impressed with the instructional value of these examples, and how working through them really helped in understanding what to look for to improve the model and how to make it work.
After optimization came another dive into cluster analysis, but this time using network graphs to analyze wholesale wine purchasing data. This model was new to me, and a fascinating way to use graphs to figure out closely related nodes. The next chapter moved on to regression, both linear and non-linear varieties, and this happens to be the Target-pregnancy example. It was super interesting to see how to conform the purchasing data to a linear model and then run the regression on it to analyze the data. Foreman also had some good advice tucked away in this chapter on data vs. models:
You get more bang for your buck spending your time on selecting good data and features than models. For example, in the problem I outlined in this chapter, you'd be better served testing out possible new features like "customer ceased to buy lunch meat for fear of listeriosis" and making sure your training data was perfect than you would be testing out a neural net on your old training data.We're into chapter 7 now with ensemble models. This technique takes a bunch of simple, crappy models and improves their performance by putting them to a vote. The same pregnancy data was used from the last chapter, but with this different modeling approach, it's a new example. The next chapter introduces forecasting models by attempting to forecast sales for a new business in sword-smithing. This example was exceptionally good at showing the build-up from a simple exponential smoothing model to a trend-corrected model and then to a seasonally-corrected cyclic model all for forecasting sword sales.
Why? Because the phrase "garbage in, garbage out" has never been more applicable to any field than AI. No AI model is a miracle worker; it can't take terrible data and magically know how to use that data. So do your AI model a favor and give it the best and most creative features you can find.
As I've learned in the other data science books, so much of data analysis is about cleaning and munging the data. Running the model(s) doesn't take much time at all.
The next chapter was on detecting outliers. In this case, the outliers were exceptionally good or exceptionally bad call center employees even though the bad employees didn't fall below any individual firing thresholds on their performance ratings. It was another excellent example to cap off a whole series of very well thought out and well executed examples. There was one more chapter on how to do some of these models in R, but I skipped it. I'm not interested in R, since I would just use Python, and this chapter seemed out of place with all the spreadsheet work in the rest of the book.
What else can I say? This book was awesome. Every example of every model was deep, involved, and appropriate for learning the ins and outs of that particular model. The writing was funny and engaging, and it was clear that Foreman put a ton of thought and energy into this book. I highly recommend it to anyone wanting to learn the inner workings of some of the standard data science models.
Python Machine Learning
This is a fairly long book, certainly longer than most books I've read recently, and a pretty thorough and detailed introduction to machine learning with Python. It's a melding of a couple other good books I've read, containing quite a few machine learning algorithms that are built up from scratch in Python a la Data Science from Scratch, and showing how to use the same algorithms with scikit-learn and TensorFlow a la the Python Data Science Handbook. The text is methodical and deliberate, describing each algorithm clearly and carefully, and giving precise explanations for how each algorithm is designed and what their trade-offs and shortcomings are.As long as you're comfortable with linear algebraic notation, this book is a straightforward read. It's not exactly easy, but it never takes off into the stratosphere with the difficulty level. The authors also assume you already know Python, so they don't waste any time on the language, instead packing the book completely full of machine learning stuff. The shorter first chapter still does the introductory tour of what machine learning is and how to install the correct Python environment and libraries that will be used in the rest of the book. The next chapter kicks us off with our first algorithm, showing how to implement a perceptron classifier as a mathematical model, as Python code, and then using scikit-learn. This basic sequence is followed for most of the algorithms in the book, and it works well to smooth out the reader's understanding of each one. Model performance characteristics, training insights, and decisions about when to use the model are highlighted throughout the chapter.
Chapter 3 delves deeper into perceptrons by looking at different decision functions that can be used for the output of the perceptron model, and how they could be used for more things beyond just labeling each input with a specific class as described here:
In fact, there are many applications where we are not only interested in the predicted class labels, but where the estimation of the class-membership probability is particularly useful (the output of the sigmoid function prior to applying the threshold function). Logistic regression is used in weather forecasting, for example, not only to predict if it will rain on a particular day but also to report the chance of rain. Similarly, logistic regression can be used to predict the chance that a patient has a particular disease given certain symptoms, which is why logistic regression enjoys great popularity in the field of medicine.The sigmoid function is a fundamental tool in machine learning, and it comes up again and again in the book. Midway through the chapter, they introduce three new algorithms: support vector machines (SVM), decision trees, and K-nearest neighbors. This is the first chapter where we see an odd organization of topics. It seems like the first part of the chapter really belonged with chapter 2, but including it here instead probably balanced chapter length better. Chapter length was quite even throughout the book, and there were several cases like this where topics were spliced and diced between chapters. It didn't hurt the flow much on a complete read-through, but it would likely make going back and finding things more difficult.
The next chapter switches gears and looks at how to generate good training sets with data preprocessing, and how to train a model effectively without overfitting using regularization. Regularization is a way to systematically penalize the model for assigning large weights that would lead to memorizing the training data during training. Another way to avoid overfitting is to use ensemble learning with a model like random forests, which are introduced in this chapter as well. The following chapter looks at how to do dimensionality reduction, both unsupervised with principal component analysis (PCA) and supervised with linear discriminant analysis (LDA).
Chapter 6 comes back to how to train your dragon…I mean model…by tuning the hyperparameters of the model. The hyperparameters are just the settings of the model, like what its decision function is or how fast its learning rate is. It's important during this tuning that you don't pick hyperparameters that are just best at identifying the test set, as the authors explain:
A better way of using the holdout method for model selection is to separate the data into three parts: a training set, a validation set, and a test set. The training set is used to fit the different models, and the performance on the validation set is then used for the model selection. The advantage of having a test set that the model hasn't seen before during the training and model selection steps is that we can obtain a less biased estimate of its ability to generalize to new data.It seems odd that a separate test set isn't enough, but it's true. Training a machine isn't as simple as it looks. Anyway, the next chapter circles back to ensemble learning with a more detailed look at bagging and boosting. (Machine learning has such creative names for things, doesn't it?) I'll leave the explanations to the book and get on with the review, so the next chapter works through an extended example application to do sentiment analysis of IMDb movie reviews. It's kind of a neat trick, and it uses everything we've learned so far together in one model instead of piecemeal with little stub examples. Chapter 9 continues the example with a little web application for submitting new reviews to the model we trained in the previous chapter. The trained model will predict whether the submitted review is positive or negative. This chapter felt a bit out of place, but it was fine for showing how to use a model in a (semi-)real application.
Chapter 10 covers regression analysis in more depth with single and multiple linear and nonlinear regression. Some of this stuff has been seen in previous chapters, and indeed, the cross-referencing starts to get a bit annoying at this point. Every single time a topic comes up that's covered somewhere else, it gets a reference with the full section name attached. I'm not sure how I feel about this in general. It's nice to be reminded of things that you've read about hundreds of pages back and I've read books that are more confusing for not having done enough of this linking, but it does get tedious when the immediately preceding sections are referenced repeatedly. The next chapter is similar with a deeper look at unsupervised clustering algorithms. The new k-means algorithm is introduced, but it's compared against algorithms covered in chapter 3. This chapter also covers how we can decide if the number of clusters chosen is appropriate for the data, something that's not so easy for high-dimensional data.
Now that we're two-thirds of the way through the book, we come to the elephant in the machine learning room, the multilayer artificial neural network. These networks are built up from perceptrons with various activation functions:
However, logistic activation functions can be problematic if we have highly negative input since the output of the sigmoid function would be close to zero in this case. If the sigmoid function returns output that are close to zero, the neural network would learn very slowly and it becomes more likely that it gets trapped in the local minima during training. This is why people often prefer a hyperbolic tangent as an activation function in hidden layers.And they're trained with various types of back-propagation. Chapter 12 shows how to implement neural networks from scratch, and chapter 13 shows how to do it with TensorFlow, where the network can end up running on the graphics card supercomputer inside your PC. Since TensorFlow is a complex beast, chapter 14 gets into the nitty gritty details of what all the pieces of code do for implementation of the handwritten digit identifier we saw in the last chapter. This is all very cool stuff, and after learning a bit about how to do the CUDA programming that's behind this library with CUDA by Example, I have a decent appreciation for what Google has done with making it as flexible, performant, and user-friendly as they can. It's not simple by any means, but it's as complex as it needs to be. Probably.
The last two chapters look at two more types of neural networks: the deep convolutional neural network (CNN) and the recurrent neural network (RNN). The CNN does the same hand-written digit classification as before, but of course does it better. The RNN is a network that's used for sequential and time-series data, and in this case, it was used in two examples. The first example was another implementation of the sentiment analyzer for IMDb movie reviews, and it ended up performing similarly to the regression classifier that we used back in chapter 8. The second example was for how to train an RNN with Shakespeare's Hamlet to generate similar text. It sounds cool, but frankly, it was pretty disappointing for the last example of the most complicated network in a machine learning book. It generated mostly garbage and was just a let-down at the end of the book.
Even though this book had a few issues, like tedious code duplication and explanations in places, the annoying cross-referencing, and the out-of-place chapter 9, it was a solid book on machine learning. I got a ton out of going through the implementations of each of the machine learning algorithms, and wherever the topics started to stray into more in-depth material, the authors provided references to the papers and textbooks that contained the necessary details. Python Machine Learning is a solid introductory text on the fundamental machine learning algorithms, both in how they work mathematically how they're implemented in Python, and how to use them with scikit-learn and TensorFlow.
Of these two books, Data Smart is a definite-read if you're at all interested in data science. It does a great job of showing how the basic data analysis algorithms work using the surprisingly effect method of laying out all of the calculations in spreadsheets, and doing it with good humor. Python Machine Learning is also worth a look if you want to delve into machine learning models, see how they would be implemented in Python, and learn how to use those same models effectively with scikit-learn and TensorFlow. It may not be the best book on the topic, but it's a solid entry and covers quite a lot of material thoroughly. I was happy with how it rounded out my knowledge of machine learning.
A Eulogy For Saturday Morning TV
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| Image by the autowitch. Some rights reserved. Source: Flickr |
So, Saturday morning cartoons are dead.
Last year, The Washington Post reported,
"This past Saturday, the CW became the last broadcast television network to cut Saturday morning cartoons. The CW is replacing its Saturday cartoon programming, called "The Vortexx," with "One Magnificent Morning," a five-hour bloc of non-animated TV geared towards teens and their families.
From the 1960s through the 1980s, Saturday morning time slots were synonymous with cartoons. Broadcast networks and advertisers battled for underage viewers. But that started to change in the 1990s.
In 1992, NBC was the first broadcast network to swap Saturday morning cartoons for teen comedies such as "Saved by the Bell" and a weekend edition of the "Today" show. Soon, CBS and ABC followed suit. In 2008, Fox finally replaced Saturday morning cartoons with infomercials.
In the 1970s and 1980s, a Saturday morning cartoon viewership could grab more than 20 million viewers. In 2003, some top performers got a mere 2 million, according to Animation World Network," (Sullivan).
Well, I suppose it was only a matter of time before this occurred. Saturday morning cartoons have left the public television stations for good. Of course, this isn't a bad thing. Kids can get their shows on demand from a variety of venues, be it Hulu, Netflix, and the wonders of cable. No need to wake up early in the morning with a bowl of sugary cereal, while your eyes sink in the flashing screens. I think this change is for the best, children should be doing more productive things with their weekends, but nevertheless, a eulogy is necessary.
I can't remember when I first started watching Saturday morning TV, but I do know that the earliest I'd get up at would be 7:00. A feat that'd be unthinkable for my more jaded self to do on a day off. 7:00, I'm sure, was when they'd play the classic cartoons, like Popeye. Then there were the principal shows that I followed every week, Pokemon, Digimon, Power Rangers, Teenage Mutant Ninja Turtles: The Next Mutation, and Transformers: The Beast Wars. I may have watched more, but I don't remember them. Of course, many of these shows, along with others like X-Men, Beetleborgs, and Spiderman, often played on weekday afternoons. Yet those were reruns. On Saturday morning, you saw things fresh.
Of course, none of these shows was anything particularly intelligent or profound, this was children's entertainment, after all. They just hit on all the right points, reaching those base, animal desires that most children wish to see. Namely, colorful, lively worlds with fantastical characters, be they transforming monsters, super-powered teenagers, or shape-shifting robots. Many of these shows, I imagine, probably introduced a generation of children to science-fiction, fantasy, martial arts, and most importantly for me, anime. That said, reading Calvin and Hobbes has made me reflect and question the wisdom of consuming so much silly television at a young age. While I don't believe television to be quite the scourge of civilization that some Luddites may make it out to be, to say it has no effect on us at all (if even a fleeting one), after habitual viewings, just sounds dishonest.
It's a bit regrettable that Digimon and Pokemon were released around the same time. No doubt, Digimon banked somewhat on the popularity of Pokemon, but it would always be under Pokemon's shadow. The reason I say this, is because Digimon was a smarter show, well, "smarter" by the standards of children's entertainment, but you get the idea.
Pokemon came out in 1998 and Digimon came out in 1999. While I can't speak for the developments of these shows in Japan, I suspect that Fox Kids licensed Digimon to capitalize on Pokemon's success and have an easy cash cow to compete with WB. I mean, as far as they saw it, Pokemon had monsters and that made money. Digimon also had monsters, therefore, it too will make money. While Digimon certainly had its peak, it never became quite the phenomenon that Pokemon was. Not where I lived, anyhow.
If you're too young to remember the Pokemon craze, then you'd best watch the "Chinpokomon" episode of South Park. While being in its own right an entertaining episode, it's a fairly accurate satire of how most children and adults reacted to the fad. So much so, that I'm a little embarrassed of my behavior then. In a nutshell, children became consumerist zombies, begging their parents to buy as much Pokemon-related merchandise as possible. While the adults were gravely confused as to why children found this cartoon so attractive. I recall one adult asking me why the Pokemon only say their own names and nothing else. Although unlike South Park, the Japanese weren't interested in using this franchise to cause another Pearl Harbor (or complement our comparative penis sizes).
Pokemon was based on a series of Nintendo video games, which are far more enjoyable than the television show. The point of the game was the capture 'pocket monsters' or 'Pokemon', and use them to fight other Pokemon. So yes, the premise of the franchise is essentially glorified cock-fighting (another South Park episode comes to mind), but electric Pikachu and fire-breathing Charizard are a far-cry from actual animals. I'm not aware of anyone who has said that they were drawn to cock-fighting, or even animal cruelty in general, because of Pokemon. So PETA's grotesque claims that Pokemon encourages such behavior, and the degrees of absurdity with which they attack the series, diminishes, if not destroys any credibility they have as an honest animal rights organization. Try the Humane Society instead.
Digimon, on the other hand, is set in real-life Japan, with Japanese children who fall into the digital world. The digital world is inhabited by digital monsters, or "Digimon". These children, dubbed the "Digi-destined" (because it has been prophesied) partner up with Digimon to fight off the threats to both of their realities. Much like the Pokemon, the Digimon can also evolve. Agumon can turn into WarGreymon and Patamon can turn into Angemon, the difference being that Digimon evolutions aren't permanent and didn't always work in a pinch. Digimon also dealt with more mature themes than Pokemon, like divorce, romance, and death. Yes, much of Digimon devolved to monster-of-the-week plots and very cliched characters, but some clever people were able to put their mark on it. One was Mamoru Hosoda, who would later gain fame for the films Summer Wars and The Girl Who Leapt Through Time. He got his debut directing the "Four Years Later" or "Our War Game" section of Digimon: The Movie. Even if you don't like Digimon, you have to appreciate the physical realism that Hosoda brought to the series, and surreal, hypnotic design of the World Wide Web that were a clear influence on Summer Wars. In the English dub, this is all dubbed over with a pop soundtrack that includes The Barenaked Ladies and The Mighty Mighty BossTones. It actually kind of fit, somehow. The other talent to touch Digimon was writer Chiaki J. Konaka, who wrote mind-bending screenplays for Texhnolyze, Rahxephon, and Serial Experiments Lain. His pen went behind the third season, Digimon Tamers, which was also the darkest. The season is rather meta, with the past two seasons being a television show in this universe. The main character creates his own Digimon and has to own up to the responsibilities of that. I can't say I remember much from this season, except that it was pretty gloomy in comparison to the other two. So, to summarize, Pokemon was about fighting for fun, Digimon was about fighting for glory.
As dumb as Pokemon and Digimon were, they're probably the best examples in recent memory of anime becoming mainstream entertainment in the United States. I mean hell, I sang the Pokemon theme song in music class, and not the TV-edited version, either. Yes, Dragonball and Sailor Moon ran close behind, but they were aimed at a slightly older demographic, so they didn't get quite as much accessibility as those whose cerebrums were still wet. That isn't to say that Dragonball and Sailor Moon weren't accessible, or even all that unpopular, but again, I didn't sing the Sailor Moon theme song in music class.
Probably the most significant anime I saw on Saturday morning was The Vision of Escaflowne. It didn't get a long run, I only recall seeing two episodes. Anyone who's seen Escaflowne knows that it's not for kids, so the editors went to work on Disneyfying it. Yet as defanged and bastardized as this version was, those two episodes still left an impact on me. One so strong, in fact, that long after I had forgotten the title of the show, the image of Prince Vaughn sprouting his glowing, white wings haunted the dark corners of my brain. Escaflowne was really weird in comparison to all the Pokemons running around. The characters had detailed and mature designs, while the atmosphere was enigmatic and quiet. Even though I didn't rediscover Escaflowne until over a decade later, it was my first glimpse into the world of adult anime.
There's not much I can say about Teenage Mutant Ninja Turtles: The Next Mutation because I can barely, and I mean barely recall it. I can't even reproduce a full episode in my mind. All I know is that they had a female turtle, Venus de Milo, and that's about it. The show has aged terribly, and I doubt if I could stomach a full half hour of the stuff nowadays. Yet, nevertheless, this was the series that introduced my generation to the Turtles. (I think that's a good thing.) I know that the only episode of Ninja Turtles that left something of an impression on me, was their crossover episode with the Power Rangers, who were then, "lost in space." Again, details are fuzzy, but at the time, it was a pretty cool event.
Now Power Rangers was a show. To see young people like myself fight monsters in colored spandex and ride in giant robots inspired by prehistorical creatures, was all my hyper-active brain needed. Much like Pokemon, Power Rangers was also very repetitive in form, but unlike Pokemon, Power Rangers is still plenty of fun to watch. The campy aesthetic coupled with MTV style editing, a slapstick Saved By The Bell background, and hard rock soundtrack are all too much to resist. If you don't take it too seriously, which you shouldn't, the Power Rangers is entertainment for entertainment's sake. Kitsch, yes, but if you know what you're going in for, then you might as well have fun with it.
I was introduced to the Transformers through the Beast Wars series. So my understanding of Optimus Prime was not of a semi-truck that could transform into a robot, but of a gorilla that could transform into a robot. Beast Wars tried to do something different with the premise of alien robots who disguise themselves as vehicles, being alien robots who could disguise themselves as giant animals. There were also no annoying humans on the planet, just aliens on an alien planet, so the plot was not restricted by the red tape that previous and later Transformers installments dealt with. Not only was Optimus Prime a gorilla and Megatron a T-Rex, but new characters were also thrown into the mix. My favorite being Cheetor, who, if you couldn't already guess is a cheetah. His personality was very much like Johnny Storm from the Fantastic Four, arrogant, quick-tempered, and fun-loving. Beast Wars was so popular that it got a sequel series, Beast Machines. Things turned darker, with the Autobots on the lam in a futuristic city, and their designs changed to reflect their more robotic predecessors. It was awesome. At my babysitter's house, where I watched much of these shows, we played with Beast Wars toys, and let me tell you, they were as frustrating to transform as all hell. In the commercials, they made it look so easy. I mean, does Hasbro really expect children to be able to successfully transform the Cheetor into assault mode in between commercial breaks?
For what it's worth, I did try watching the original 80's cartoon, but I was older, and so, didn't care for it. I liked the theme song, though. Then there was that movie which had talents like Leonard Nimoy and Orson Welles. An irony that Welles's debut was Charles Foster Kane, and his final performance was Omnicron. The movie is very much a zeitgeist of what was being marketed to boys of the 80's, over-the-top action and loud rock music. How much has changed? While I'm at it, I may as well address the elephant in room, Michael Bay. Yes, his Transformers films are all very bad, but the first one, at least, was watchable. It was a decent action film with neat effects, but held many of the problems that were multiplied over the next couple of movies. What I find more offensive than the bad scripts, however, is the fact that Bay thinks it's appropriate to market towards kids, or any human being, a franchise littered with excessive violence, racial insensitivity, and crude, blatant misogyny. In fact, I'd argue that these terribly unpleasant and immoral films do far more harm to the minds of children than the cheap shows I'm discussing here.
Here's a sidewinder, Spongebob Squarepants. Yes, I distinctly remember watching the series premiere of "Bubblestand", in my mother's bedroom, on a Saturday morning. Now, Spongebob didn't always play new episodes on Saturday mornings, but I watched the series religiously since that first viewing, so I felt the need to reference it. It's hard to defend the ungodly receptacle of garbage that holds the banner of Spongebob today. Ever since Stephen Hillenberg left, the show produced some of the worst writing to ever grace the televised screen, it's real nauseating stuff. I blame Nickelodeon's producers more than I do Spongebob's writers, because a premise can only work for so long before it grows stale. Point of reference, The Simpsons. Though at least Homer still has some dignity left on him and after two decades, no less. Spongebob, on the other hand, is no longer the quirky, nervous, and hopelessly naive character that endeared him to audiences on his first appearance. Now, he's a blubbering twit, a moronic and deranged man-child, whose every action is designed to irritate the living hell of you. The masturbatory excess of Mr. Squarepants, along with his now depraved and unsightly "friends" will not recover from this milking from a long deceased cow.
Believe it or not, my interest in Saturday morning cartoons extended into middle school. Why? Perhaps it was out of a desire to relive the nostalgia of my former years, even though I knew what I watched was garbage. At the time, I was very much addicted to television. I watched it because I was bored, and terribly lazy. I not only lament the fact that I wasted much of my youth consuming television, but that it was bad television. Surely, I could've benefited from some Star Trek or The Twilight Zone episodes. That said, there was one show I watched religiously every Saturday morning with great fondness, about as much as Pokemon, Spongebob, or Beast Wars, and that was Yu-Gi-Oh!
Yu-Gi-Oh! was more than just an anime to me, it was also a trading card game, and a very fun one, might I add. A game in which one could summon monsters, cast spells, or spring traps against your opponent. Some monsters had special abilities, while others could fuse to create greater monsters. It was a lot of fun.
However, Yu-Gi-Oh! initially began as a tribute to tabletop games in general. The protagonist, Yugi Moto, is a shy high-schooler with multicolored spiky hair (it's an anime, remember?). He solves an Egyptian artifact known as the Millennium Puzzle. Inside of this puzzle is trapped the soul of a 2000 year old pharaoh known as "The King of Games." Whenever Yugi finds himself in life-threatening trouble, the spirit of the pharaoh possesses him, and challenges his opponent to a deadly game. A variety of different ones were played, like one inspired by Dungeons and Dragons. The card game, was one among many, but it stuck, being the most popular. So the anime focused on this aspect for the story.
That said, the anime is about as corny as most Saturday morning television, and the 4Kids chop-up didn't help. Yu-Gi-Oh! was very formulaic, featuring Yugi dueling an opponent in a game of cards and almost always winning (unless blackmailed by threats of suicide). Yet, we didn't watch to Yu-Gi-Oh! to see who would win, we watched the show to see the different strategies employed by the cards. Be it the destructive blowback from Mirror Force, or the dreaded one turn kill of Exodia. The simplicity of the game when it first began is now enviable, a time when summoning a high powered Dark Magician or Blue Eyes White Dragon could win you the game. The game has since mutated into a convoluted speed contest, with nonsense terminology, conflicting rules, embarrassingly high prices, and a rapidly growing roster of cards that may very well lead to an implosion. If there was one good thing to come out of Yu-Gi-Oh!, it's Yu-Gi-Oh!: The Abridged Series by Martin Billany (aka LittleKuriboh). An abridged series is when someone makes an edited version of a show and overdubs it with humorous and often meta voiceovers. Some of the best moments are when Billany constantly notes the borderline hyperbole of seriousness with which people take a children's card game (who's rules are often broken for plot convenience). This isn't even touching the many lines that are popular amongst the otaku fandom, like "Screw the rules, I have money!"
On a side note, don't you find it a bit bizarre that we define our fading childhood memories by the films, television, and music that we consumed then? Nostalgia has never been so openly fetishized in America as it has now. The culprit behind this is, of course, the Internet. Music critic Simon Reynolds, who wrote Retromania: Pop Culture's Addiction To Its Own Past, has said,
"It was gradual, but with the arrival of the Internet, and broadband access, and the rise of this kind of strange collective archiving thing, [looking backward] became irresistible. Now people put stuff on YouTube because it feels like they're doing something worthwhile and this enormous archive has developed. You're young, but I try to remember what it was like when it was actually really hard to get hold of information. If you wanted to look at old magazines, you had to go to the library and look at microfilms. Now all the records in the known universe are basically accessible at the click of a mouse. Don't you think that's weird? I think it's weird — but I have something to compare it to. I remember living in a culture of cultural scarcity," (Salon).
I agree with Reynolds here. Nostalgia is popular because it's so accessible. I probably wouldn't have been able to find Escaflowne were it not for the Internet. I also think that this nostalgia hunt comes from the effects that 9/11 had, and still does have on the American psyche. The War on Terror, and all that came after it, in the context of the Information Age, no less, made the world a complex and ambiguous place. The truth, however, is that it was always like this, we just want to believe that there was a magical, Reaganesque America where the mornings never ended. It's worse yet when one was a child, and could've hardly comprehended events grander than the events on your television screen. Now, a sort of cult has developed that puts the cartoons of the past on a pedestal, with entitled fans claiming that newer versions can never be as good as the older ones. The worst of it comes when Hollywood taps into this nostalgia for money, and is answered with cries that Hollywood "ruined my childhood." Yet this nostalgia that people hopelessly flee to is only fueling the film industries to make more adaptations. A Catch-22. Reynolds articulated some of these issues,
"This endless regurgitation of the familiar is dulling and vaguely depressing. It's nice to think there's a future for music, for example, and that people will do things that later generations can work with and take somewhere. I think if the preponderance of the music scene is based around recycling and revivalism, then it's like bad farming. Basic common sense in farming is that you sow as well as reap. If you're just reaping from the past, you're not really giving anything back. Of course, music and culture don't necessarily work in the way farming does, and ideas don't get exhausted in the same way natural resources do, but I think it's important for the ongoing project of music to at least try to come up with things that have never been done before. Young musicians, in particular, seem to be way more fascinated by the past than the future. That's my main worry: Where is it going? Is this a practice that is infinitely sustainable? At this point, we're well into the '90s revival, and then it will be time for the naughties revival. It just seems a bit boring that that's just how it's going to proceed," (Salon).
Our culture is in a feedback loop, stuck in the 80's and 90's, where twenty-somethings complain about how old they've gotten and indulge in listicles on the Internet that seem to confirm this bias. It's time that we stopped defining ourselves and our memories solely on the basis of the crappy shows that we were too dumb to turn off. Yes, some of them were fun, but let's not kid ourselves here, these programs weren't masterpieces. I had a good childhood, not because I had the privilege of eating soggy marshmallow cereals too close to a television screen, but because I had loving friends, teachers, and family. In any case, childhood is overrated. Some of us had terrible ones. I, for one, am glad to be older. Isn't it grand to be able to tell the difference between pearls and swine? It's easier to look back than it is to look forward. So unless you want Hollywood to reboot Spiderman every three years, I suggest we admit that the 80's and 90's were just as mundane as any other decade, and start looking ahead.
I wrote this eulogy happily.
Bibliography
Reynolds, Simon. Interviewed by Thomas Rogers. "Will nostalgia destroy pop culture." Salon, August 5, 2011. Web. http://www.salon.com/2011/08/05/retromania_simon_reynolds_interview/
Sullivan, Gail. "Saturday morning cartoons are no more." The Washington Post, September 30, 2014. Web. http://www.washingtonpost.com/news/morning-mix/wp/2014/09/30/saturday-morning-cartoons-are-no-more/
Hiring: Tools Programmer
Title: Tools programmer
Focus: Engine
Type: Full-time, permanent
Last day to apply: 30th of October 2018
Tired of the constraints of Unity, Unreal and other big engines? Want to be in control and get down into the nitty gritty of engine coding? Come join us at Frictional Games, one of the few companies that still makes their own tech, and get all up in our HPL engine!
We are now expanding our tech team and looking for a tools programmer who will help make the HPL engine better, prettier, and more intuitive. Your work on the engine will be crucial to the rest of the team, but it will also be seen by our modding community.
The position is full-time and permanent. Ideally we would help you relocate to Malmö, Sweden to be close to our core team, but this is not a necessity.
What will you work on?
As a tools programmer, you will be working together with a small tech team that is mainly responsible for our HPL engine, but also tech support for the games.
Here are some of the things you will find yourself working on:
- Creating and maintaining the level editor for our proprietary engine
- Making intuitive user interfaces
- Creating small specialised tools
- Working with low-level systems such as IO, AI, rendering, sound, and physics
- Working with Xbox and PlayStation versions, as well as possible future platforms
- Internal support for a team of developers
- Post-launch support.
If you want to know more about Frictional work practices, you can check out the introduction posts of Peter and Luis, who will be your closest teammates.
What are we looking for?
You have to be a EU/EEA resident to apply.
The person we're looking for is creative, driven and self-sufficient.
Here are some essential skills we require:
- Well-versed in C++, C#, Java, or similar
- Knowledge in AngelScript, Python, Lua, or similar
- You have created an engine or tools for development for at least one game
- Strong low-level programming skills
- Familiar with linear algebra
- Knowledge in working with Widgets / Custom GUI
- Fluency in English
- Skills in team communication and support
- A Windows PC that runs recent games (such as SOMA) that you can use for work (unless you live in Malmö and will work from the office)
- A fast and stable internet connection.
These will be considered a plus:
- Experience in engine development
- Skills in 3D modelling or texture applications
- Knowledge in UX design
- Lover for tech and messing with the low level parts of the engine
- Excitement for creating fast pipelines and making it easy to create awesome art
- You live in Sweden.
What do we offer?
We are a small team, which means you will be able to work on a wide variety of things and contribute to our future games in a meaningful way.
We also believe a healthy balance between work and life reflects positively on your work. We offer a variety of perks for our full-time employees, especially who live in or relocate to Sweden. We also don't encourage crunch.
Here's what we offer:
- Flexible working hours
- Opportunities to influence your workflow
- Variety in your work tasks, and ability to influence your workload
- Participation in our internal game Show & Tell sessions, so you'll have input into all aspects of the game
- Social security and holidays that are up to the Swedish standards
- An inclusive and respectful work environment
- An office in central Malmö you can use as much as you please
- Fun workmates, game and movie nights, and other outings!
How to apply?
Did the position pique your interest? Are you the person we're looking for? Then we would love you hear from you!
We will be looking at applications until 30th of October 2018.
Please send us your:
- Cover letter (why you should work with us, what do you bring to the table)
- CV
- Portfolio (or links to your works)
Privacy Policy
By sending us your application, you give us permission to store your personal information and attachments.
We store all applications in a secure system. The applications are stored for two years, after which they are deleted. If you want your your information removed earlier, please contact us through our Contact form. Read more in our Privacy Policy.
Captain Tsubasa Rise Of New Champions Announced
BANDAI NAMCO Entertainment Asia today announced that Captain Tsubasa RISE OF NEW CHAMPIONS will be available on the PlayStation 4, Nintendo Switch and PC (Steam) in 2020.
Captain Tsubasa game released after a decade!
Captain Tsubasa RISE OF NEW CHAMPIONS is a soccer action game, inspired by the wildly popular anime series, to be released on PlayStation 4/ Nintendo Switch/PC (Steam).
Expect high-quality visuals and experience exhilarating speeds in this one-of-a-kind game!
Catch the newly released trailer and take a trip down memory lane as you catch the appearance of the players from Japan Junior Youth Generation team, including the beloved main character Tsubasa Ozoro materializing this dream through the latest generation of home console.
Results Of MSSA's 10Th Annual Online Championships For High Schools
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| Northcliff High School grabbed the 'lion's share' of titles at MSSA's 10th Annual Online Championships for High Schools. |
Teams from aroundd the country gathered at schools, or at previously approved locations, to fight for glory and honour.
Judging by the winners of each gamee title, it is becoming clear that certain schools are beginning to take ownership of the title. The schools are blessed with hard-working and dedicated educators who give up their own time to ensure the success of the school and the learner.
It is interesting that with the educator moving from SAHETI to Redhill, success has followed the educator, which further re-inforces the belief that without proper management, the team has even greater odds to overcome.
Another encouraging feature is that more provinces saw teams being awarded High School Provincial Colours. With five (5) provinces seeing colours being awarded, it is clear that Gauteng and Western Cape provinces no longer totally dominate esports in South Africa and that there is a more general standard of excellence than ever before.
It should be noted that Provincial High School Colours are awarded immediately to the members of the team who win each and every game that they play in such 10th Annual Online Championships for High Schools.
The members of the teams that have finished in the top three also immediatel qualify for 2020 National Team Trials. It is at such National Team Trials that MSSA shall select its team to attend International Esports Federation's (IESF) 12th World Championships - Eilat.
The winners of MSSA's 10th Annual Online Championships for High Schools are:
Also read:
- Ensuring gender equity in esports.
- MSSA celebrates its women who have earned Protea Colours.
- Why schools and universities should invest in esports.
- Entry fees for Provincial and National Championships
- How school clubs affiliate for the 2020 season.
- 2020 Calendar of Events
- The various events held by Mind Sports South Africa.
- Why schools and universities should invest in esports
- New National Team Selection Criteria
- Selecting the national Protea teams for Mind Sports South Africa.
- The road to Eilat
- MSSA to nominate candidates for IESF's International Referee Workshop.
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