Mix doesn't support your web browser. For a better experience, we recommend using another browser.
@chrisreeser
Chris Reeser
Lover of coffee, engaging designs, intuitive interfaces, intelligent learning algorithms, entertaining mobile apps, puzzles, simple casual games.
https://genxtao.com/
1
1
Spaced Learning: An Approach to Minimize the Forgetting CurveEffective long-term learning is rarely achieved by a one-off event, but designers all too often think of one-off events when building training solutions. However, as soon as the event ends, forgetting is likely to begin. It is an unfortunate fact, first spotted by Ebbinghaus in 1885, that we have not only a “learning curve” (where people take time to learn in the first place), but also a “forgetting curve” (where we lose what we learn if we don’t use it regularly). Effective learning strategies should therefore not only help people learn as quickly and efficiently as possible, but also minimize forgetting. What can we do? Just-in-time performance support is one answer to this problem. If you learn something just before you need to use it, then there is no time to forget. However, most people can’t learn everything they need just before they need it; sometimes having to stop and look things up slows us down too much, or safety requires that we master and memorize information. Enter a Spaced Learning Approach Clearly, we need another approach to complement just–in-time learning. Something that will help us learn and that will minimize what we forget. Spacing learning over time might just be that approach. There is a body of research that suggests that spacing learning over time helps people learn more quickly and remember better. It has been found to be effective in various domains, from sales training to language learning to medicine (Caple, 1997; Castel, Logan, Haber, & Viehman, 2012; Grote, 1995; Kerfoot et al., 2007; Lambert, 2009; Landauer & Ross, 1977; Lehmann-Willenbrock & Kauffeld, 2010; Toppino & Cohen, 2010). What does this mean in practice? If you are designing a learning program with spacing in mind, you will present learners with a concept or learning objective, allow a period of time to pass (days, weeks, or months) and then present the same concept again. This might involve a few repetitions, or many, depending on how complex the content is. Similarly, the intervals between repetitions might be adjusted depending on the content and the audience. Will Thalheimer’s research (see the list of links at the end of this blog) gives some useful pointers about the effect of using longer or shorter intervals. Repeating the concept might mean simply re-introducing that concept exactly as it was presented earlier or presenting it in a slightly different way. For example, concepts might be presented using a variety of different media, stories, and so forth. They might also involve delivery of a selection of similar but distinct practice exercises, or simulations delivered over time. Designers using the spaced approach need to settle on several different ways to present the same point, rather than thinking about the single best way, as we might for a more traditional e-learning course or face-to-face session. Designers also need to get even better at tightly defining learning objectives—as each one will need a lot of work and interpretation, there may be little room for ‘nice to have’ information! When should you consider spaced learning? Sometimes a spaced approach will be appropriate as follow-up to a one-off event to minimize forgetting after that event. At other times, perhaps in conjunction with performance support, it may be possible to design an entire solution using this approach. Designing learning so that activities can be tackled in short bursts, spaced over time, may not only help learners remember over time but also reduce the need for large blocks of time away from the workplace to learn in the first place. It is likely to be particularly helpful for busy learners on the go, who can use mobile devices to access spaced learning in short bursts of ‘found time’. It is worth noting that marketing campaigns have long known the importance of spacing repetition of key messages. They set out to create a ‘persuasive’ effect through a cultivation of familiarity which is achieved through repetition. Perhaps it is time for learning to take a lesson from marketing in spacing out material. Additional Resources References Caple, C. (1997). The effects of spaced practice and spaced review on recall and retention using computer-assisted instruction. Dissertation Abstracts International: Section B: The Sciences & Engineering, 57, 6603. Castel, A. D., Logan, J. M., Haber, S., & Viehman, E. J. (2012). Metacognition and the spacing effect: the role of repetition, feedback, and instruction on judgments of learning for massed and spaced rehearsal. Metacognition and Learning. doi:10.1007/s11409-012-9090-3 Grote, M. (1995). The Effect of Massed Versus Spaced Practice on Retention and Problem-Solving in High School Physics. he Ohio Journal of Science, 95, 243–247. Retrieved from papers://3a07567f-5013-4eec-86cd-a5ef539fd065/Paper/p4355 Kerfoot, B. P., Baker, H. E., Koch, M. O., Connelly, D., Joseph, D. B., & Ritchey, M. L. (2007). Randomized, controlled trial of spaced education to urology residents in the United States and Canada. The Journal of Urology, 177, 1481–1487. doi:10.1016/j.juro.2006.11.074 Lambert, C. (2009). “Spaced education” improves learning. Harvard Magazine. Retrieved from http://harvardmagazine.com/2009/11/spaced-education-boosts-learning Landauer, T. K., & Ross, B. H. (1977). Can simple instructions to use spaced practice improve ability to remember a fact? An experimental test using telephone numbers. Bulletin of the Psychonomic Society, 10, 215–218. Lehmann-Willenbrock, N., & Kauffeld, S. (2010). Sales training: effects of spaced practice on training transfer. Journal of European Industrial Training. doi:10.1108/03090591011010299 Toppino, T. C., & Cohen, M. S. (2010). Metacognitive control and spaced practice: clarifying what people do and why. Journal of Experimental Psychology. Learning, Memory, and Cognition, 36, 1480–1491. doi:10.1037/a0020949
5
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-playComputers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game, exploiting its properties, such as the symmetries of the board on which it is played. Silver et al. developed a program called AlphaZero, which taught itself to play Go, chess, and shogi (a Japanese version of chess) (see the Editorial, and the Perspective by Campbell). AlphaZero managed to beat state-of-the-art programs specializing in these three games. The ability of AlphaZero to adapt to various game rules is a notable step toward achieving a general game-playing system. Science , this issue p. [1140][1]; see also pp. [1087][2] and [1118][3] The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go. [1]: /lookup/doi/10.1126/science.aar6404 [2]: /lookup/doi/10.1126/science.aaw2221 [3]: /lookup/doi/10.1126/science.aav1175
4
Python Machine Learning & AI Mega Course - Learn 4 Different Areas of ML & AIReady to explore machine learning and artificial intelligence in python? This python machine learning and AI mega course contains 4 different series designed to teach you the ins and outs of ML and AI. It talks about fundamental ML algorithms, neural networks, creating AI chat bots and finally developing an AI that can play the game of Flappy Bird. Download Kite for FREE to get the best python autocomplete engine! https://kite.com/download/?utm_medium=referral&utm_source=youtube&utm_campaign=techwithtim&utm_content=python-ml-ai-mega-course ⭐ RESOURCES ⭐ IMPORTANT: The text-based guides will have download links for files or datasets needed. 1⃣ Machine Learning for Beginners Text-Based Guide: https://techwithtim.net/tutorials/machine-learning-python/introduction/ UCI Student Data Set: https://archive.ics.uci.edu/ml/datasets/Student+Performance UCI Car Evaluation Data Set: http://techwithtim.net/wp-content/uploads/2019/01/Car-Data-Set.zip 2⃣ Neural Networks Text-Based Guide: https://techwithtim.net/tutorials/python-neural-networks/what-is-a-nn/ 3⃣ Simple AI Chat Bot Text-Based Guide: https://techwithtim.net/tutorials/ai-chatbot/ JSON-File Download: https://techwithtim.net/wp-content/uploads/2019/05/json-file.zip 4⃣ Flappy Bird AI GitHub/Code: https://github.com/techwithtim/NEAT-Flappy-Bird Images: https://techwithtim.net/wp-content/uploads/2019/08/imgs.zip ⭐ SOFTWARE DOWNLOADS ⭐ Anaconda Download: https://www.anaconda.com/download/ Pycharm Download: https://www.jetbrains.com/pycharm/download/#section=windows ⭐ TIMESTAMPS ⭐ Course 1: Machine Learning for Beginners ⌨️ (00:02:30) Introduction to Machine Learning & Environment Setup ⌨️ (00:12:24) Linear Regression Part 1 – Data Loading and Analysis ⌨️ (00:26:28) Linear Regression Part 2 – Implementation and Algorithm Explanation ⌨️ (00:42:50) Saving Models and Visualizing Data ⌨️(00:56:05) K-Nearest Neighbors Part 1 – Irregular Data ⌨️ (01:08:16) K-Nearest Neighbors Part 2 – Algorithm Explanation ⌨️ (01:21:33) K-Nearest Neighbors Part 3 – Implementation ⌨️ (01:31:54) Support Vector Machines Part 1 - SkLearn Datasets and Analysis ⌨️ (01:38:34) Support Vector Machines Part 2 – Algorithm Explanation ⌨️ (01:52:21) Support Vector Machines Part 3 – Implementation ⌨️(02:01:57) K-Means Clustering – Algorithm Explanation ⌨️ (02:15:11) K-Means Clustering - Implementation Course 2: Neural Networks ⌨️ (02:27:07) Introduction to Neural Networks ⌨️ (02:53:47) Loading & Looking at Data ⌨️ (03:06:50) Creating a Model ⌨️ (03:24:05) Using and Testing Our Model ⌨️ (03:33:56) Text Classification Part 1 – Data Analysis and Model Architecture ⌨️ (03:55:23) Text Classification Part 2 – Embedding Layers ⌨️ (04:09:43) Text Classification Part 3 – Training the Model ⌨️ (04:19:49) Text Classification Part 4 – Saving and Loading Models Course 3: AI Chat Bot ⌨️ (04:34:35) Part 1 ⌨️ (04:50:28) Part 2 ⌨️ (05:02:39) Part 3 ⌨️ (05:14:32) Part 4 ⌨️ (05:30:34) Part 5 Course 4: Neuroevolutionary Algorithm Plays Flappy Bird ⌨️ (05:39:16) Creating the Bird ⌨️ (05:51:36) Moving the Bird ⌨️ (06:10:04) Pixel Perfect Collision ⌨️ (06:29:22) Finishing the Graphics ⌨️ (06:41:16) NEAT Introduction and Configuration File ⌨️ (07:01:36) Implementing NEAT and Fitness Functions ⌨️ (07:16:32) Testing and Saving Models ***** Enroll in The Fundamentals of Programming w/ Python https://tech-with-tim.teachable.com/p/the-fundamentals-of-programming-with-python Instagram: https://www.instagram.com/tech_with_tim Website https://techwithtim.net Twitter: https://twitter.com/TechWithTimm Discord: https://discord.gg/pr2k55t GitHub: https://github.com/techwithtim Podcast: https://anchor.fm/tech-with-tim One-Time Donations: https://www.paypal.com/donate/?token=m_JfrPK7DsK4PLk0CxNnv4VPutjqSldorAmgQIQnMozUwwQw93vdul-yhU06IwAuig15uG&country.x=CA&locale.x= Patreon: https://www.patreon.com/techwithtim ***** Please leave a LIKE and SUBSCRIBE for more content! Tags: - Tech With Tim - Python Tutorials - Machine Learning Course - AI Course Python - Python Machine Learning - Python Machine Learning Course - Machine Learning Python #Python #MachineLearning #AI
9
Alpha Zero Style Stoofvlees Chess Opening Novelty of Year Candidate! || vs Scorpion | TCEC 16FIDE CM Kingscrusher goes over Alpha Zero Style Stoofvlees Chess Opening Novelty of Year Candidate! || vs Scorpion | TCEC 16 League 1 ♚ Play turn style chess at http://bit.ly/chessworld ♚ Play Chess vs. Kingscrusher and others: https://www.chessworld.net/chessclubs/asplogin.asp?from=1053 FIDE CM Kingscrusher goes over amazing games of Chess every day, with a focus recently on chess champions such as Magnus Carlsen or even games of Neural Networks which are opening up new concepts for how chess could be played more effectively. The Game qualities that kingscrusher looks for are generally amazing games with some awesome or astonishing features to them. Many brilliant games are being played every year in Chess and this channel helps to find and explain them in a clear way. There are classic games, crushing and dynamic games. There are exceptionally elegant games. Or games which are excellent in other respects which make them exciting to check out. Some games are fabulous, some are famous. Some are simply fantastic. This channel tries to find basically the finest chess games going. There are also flashy, important, impressive games. Sometimes games can also be exceptionally instructive and interesting at the same time. Info about Leela Zero: https://en.wikipedia.org/wiki/Leela_Zero ... Leela Zero is a free and open-source computer Go software released on 25 October 2017. It is developed by Belgian programmer Gian-Carlo Pascutto,[1][2][3] the author of chess engine Sjeng and Go engine Leela.[4][5] Leela Zero's algorithm is based on DeepMind's 2017 paper about AlphaGo Zero.[3][6] Unlike the original Leela, which has a lot of human knowledge and heuristics programmed into it, Leela Zero only knows the basic rules and nothing more.[7] Leela Zero is trained by a distributed effort, which is coordinated at the Leela Zero website. Members of the community provide computing resources by running the client, which generates self-play games and submits them to the server. The self-play games are used to train newer networks. Generally, over 500 clients have connected to the server to contribute resources.[7] The community has provided high quality code contributions as well.[7] Leela Zero finished third at the BerryGenomics Cup World AI Go Tournament in Fuzhou, Fujian, China on 28 April 2018.[8] Additionally, in early 2018 the same team branched Leela Chess Zero from the same code base, also to verify the methods in the AlphaZero paper as applied to the game of chess. AlphaZero's use of Google TPUs was replaced by a crowd-sourcing infrastructure and the ability to use graphics card GPUs via the OpenCL library. Even so, it is expected to take a year of crowd-sourced training to make up for the dozen hours that AlphaZero was allowed to train for its chess match in the paper.[9] Info about Alphazero: https://en.wikipedia.org/wiki/AlphaZero AlphaZero is a computer program developed by the Alphabet-owned AI research company DeepMind, which uses an approach similar to AlphaGo Zero's to master not just Go, but also chess and shogi. On December 5, 2017 the DeepMind team released a preprint introducing AlphaZero, which, within 24 hours, achieved a superhuman level of play in these three games by defeating world-champion programs, Stockfish, elmo, and the 3-day version of AlphaGo Zero, in each case making use of custom tensor processing units (TPUs) that the Google programs were optimized to make use of.[1] AlphaZero was trained solely via "self-play" using 5,000 first-generation TPUs to generate the games and 64 second-generation TPUs to train the neural networks, all in parallel, with no access to opening books or endgame tables. After just four hours of training, DeepMind estimated AlphaZero was playing at a higher Elo rating than Stockfish; after 9 hours of training, the algorithm decisively defeated Stockfish 8 in a time-controlled 100-game tournament (28 wins, 0 losses, and 72 draws).[1][2][3] The trained algorithm played on a single machine with four TPUs. ... Relation to AlphaGo Zero Further information: AlphaGo Zero AlphaZero (AZ) is a more generalized variant of the AlphaGo Zero (AGZ) algorithm, and is able to play shogi and chess as well as Go. Differences between AZ and AGZ include:[1] AZ has hard-coded rules for setting search hyperparameters. The neural network is now updated continually. Go (unlike Chess) is symmetric under certain reflections and rotations; AlphaGo Zero was programmed to take advantage of these symmetries. AlphaZero is not. Chess can end in a draw unlike Go; therefore AlphaZero can take into account the possibility of a drawn game. AlphaZero vs. Stockfish and elmo ... ►If you like my chess videos please consider become a full member here at my Chessworld Site. http://www.chessworld.net/chessclubs/asplogin.asp?from=1053 #KCChess #LeelaChess #LeelaZero #NeuralNetwork #AI #chess #chessgame #chesstactics
10
Amazing Pawn structure adaptation || Stockfish 11 vs Leela ID 62076 || Sicilian DefenceAmazing Pawn structure adaptation || Stockfish 11 vs Leela ID 62076 ♚ Free anti-sicilian course: https://kingscrusher.tv/free-anti-sicilian ♚ Play turn style chess at http://bit.ly/chessworld FIDE CM Kingscrusher goes over amazing games of Chess every day, with a focus recently on chess champions such as Magnus Carlsen or even games of Neural Networks which are opening up new concepts for how chess could be played more effectively. The Game qualities that kingscrusher looks for are generally amazing games with some awesome or astonishing features to them. Many brilliant games are being played every year in Chess and this channel helps to find and explain them in a clear way. There are classic games, crushing and dynamic games. There are exceptionally elegant games. Or games which are excellent in other respects which make them exciting to check out. Some games are fabulous, some are famous. Some are simply fantastic. This channel tries to find basically the finest chess games going. There are also flashy, important, impressive games. Sometimes games can also be exceptionally instructive and interesting at the same time. Info about Leela Zero: https://en.wikipedia.org/wiki/Leela_Zero ... Leela Zero is a free and open-source computer Go software released on 25 October 2017. It is developed by Belgian programmer Gian-Carlo Pascutto,[1][2][3] the author of chess engine Sjeng and Go engine Leela.[4][5] Leela Zero's algorithm is based on DeepMind's 2017 paper about AlphaGo Zero.[3][6] Unlike the original Leela, which has a lot of human knowledge and heuristics programmed into it, Leela Zero only knows the basic rules and nothing more.[7] Leela Zero is trained by a distributed effort, which is coordinated at the Leela Zero website. Members of the community provide computing resources by running the client, which generates self-play games and submits them to the server. The self-play games are used to train newer networks. Generally, over 500 clients have connected to the server to contribute resources.[7] The community has provided high quality code contributions as well.[7] Leela Zero finished third at the BerryGenomics Cup World AI Go Tournament in Fuzhou, Fujian, China on 28 April 2018.[8] Additionally, in early 2018 the same team branched Leela Chess Zero from the same code base, also to verify the methods in the AlphaZero paper as applied to the game of chess. AlphaZero's use of Google TPUs was replaced by a crowd-sourcing infrastructure and the ability to use graphics card GPUs via the OpenCL library. Even so, it is expected to take a year of crowd-sourced training to make up for the dozen hours that AlphaZero was allowed to train for its chess match in the paper.[9] Info about Alphazero: https://en.wikipedia.org/wiki/AlphaZero AlphaZero is a computer program developed by the Alphabet-owned AI research company DeepMind, which uses an approach similar to AlphaGo Zero's to master not just Go, but also chess and shogi. On December 5, 2017 the DeepMind team released a preprint introducing AlphaZero, which, within 24 hours, achieved a superhuman level of play in these three games by defeating world-champion programs, Stockfish, elmo, and the 3-day version of AlphaGo Zero, in each case making use of custom tensor processing units (TPUs) that the Google programs were optimized to make use of.[1] AlphaZero was trained solely via "self-play" using 5,000 first-generation TPUs to generate the games and 64 second-generation TPUs to train the neural networks, all in parallel, with no access to opening books or endgame tables. After just four hours of training, DeepMind estimated AlphaZero was playing at a higher Elo rating than Stockfish; after 9 hours of training, the algorithm decisively defeated Stockfish 8 in a time-controlled 100-game tournament (28 wins, 0 losses, and 72 draws).[1][2][3] The trained algorithm played on a single machine with four TPUs. ... Relation to AlphaGo Zero Further information: AlphaGo Zero AlphaZero (AZ) is a more generalized variant of the AlphaGo Zero (AGZ) algorithm, and is able to play shogi and chess as well as Go. Differences between AZ and AGZ include:[1] AZ has hard-coded rules for setting search hyperparameters. The neural network is now updated continually. Go (unlike Chess) is symmetric under certain reflections and rotations; AlphaGo Zero was programmed to take advantage of these symmetries. AlphaZero is not. Chess can end in a draw unlike Go; therefore AlphaZero can take into account the possibility of a drawn game. AlphaZero vs. Stockfish and elmo ... ♚ Play turn style chess at http://bit.ly/chessworld #KCChess #LeelaChess #LeelaZero #NeuralNetwork #AI #chess #chessgame #chesstactics
1