Artificial Intelligence: Reinforcement Learning in Python

Artificial Intelligence: Reinforcement Learning in Python

2024



























Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications































上次更新 12/2020































英语 [自动], 德语 [自动]































13 个章节 • 108 个讲座 • 总时长 12 小时 52 分钟































你将会学到的































Apply gradient-































d supervised machine learning methods to reinforcement learning































Understand reinforcement learning on a technical level































Understand the relationship between reinforcement learning and psychology































Implement 17 differ































Share FacebookTwitterGoogle+ReddIt































Artificial Intelligence Reinforcement Learning in Python































Complete guide to artificial intelligence and machine learning, prep for deep reinforcement learning































What Will I Learn?































Apply gradient-































d supervised machine learning methods to reinforcement learning































Understand reinforcement learning on a technical level































Understand the relationship between reinforcement learning and psychology































Implement 17 different reinforcement learning algorithms































Requirements































Calculus































Probability































Markov Models































The Numpy Stack































Have experience with at least a few supervised machine learning methods































Gradient descent































Good object-oriented programming skills































Description































When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning.































These tasks are pretty trivial compared to what we think of AIs doing – playing chess and Go, driving cars, and beating video games at a superhuman level.































Reinforcement learning has recently become popular for doing all of that and more.































Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn’t been until recently that we’ve been able to observe first hand the amazing results that are possible.































In 2016 we saw Google’s AlphaGo beat the world Champion in Go.































We saw AIs playing video games like Doom and Super Mario.































Self-driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance.































If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially.































Learning about supervised and unsupervised machine learning is no small feat. To date I have over SIXTEEN (16!) courses just on those topics alone.































And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other.































It’s led to new and amazing insights both in behavioral psychology and neuroscience. As you’ll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true general artificial intelligence.































What’s covered in this course?































The multi-armed bandit problem and the explore-exploit dilemma































Ways to calculate means and moving averages and their relationship to stochastic gradient descent































Markov Decision Processes (MDPs)































Dynamic Programming































Monte Carlo































Temporal Difference (TD) Learning































Approximation Methods (i.e. how to plug in a deep neural network or other differentiable model into your RL algorithm)































If you’re ready to take on a brand new challenge, and learn about AI techniques that you’ve never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.































See you in class!































NOTES:































All the code for this course can be downloaded from my github:































/lazyprogrammer/machine_learning_examples































In the directory: rl































Make sure you always “git pull” so you have the latest version!































HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:































Calculus































Probability































Object-oriented programming































Python coding: if/else, loops, lists, dicts, sets































Numpy coding: matrix and vector operations































Linear regression































Gradient descent































TIPS (for getting through the course):































Watch it at 2x.































Take handwritten notes. This will drastically increase your ability to retain the information.































Write down the equations. If you don’t, I guarantee it will just look like gibberish.































Ask lots of questions on the discussion board. The more the better!































Realize that most exercises will take you days or weeks to complete.































Write code yourself, don’t just sit there and look at my code.































USEFUL COURSE ORDERING:































(The Numpy Stack in Python)































Linear Regression in Python































Logistic Regression in Python































(Supervised Machine Learning in Python)































(Bayesian Machine Learning in Python: A/B Testing)































Deep Learning in Python































Practical Deep Learning in Theano and TensorFlow































(Supervised Machine Learning in Python 2: Ensemble Methods)































Convolutional Neural Networks in Python































(Easy NLP)































(Cluster Analysis and Unsupervised Machine Learning)































Unsupervised Deep Learning































(Hidden Markov Models)































Recurrent Neural Networks in Python































Artificial Intelligence: Reinforcement Learning in Python































Natural Language Processing with Deep Learning in Python































Who is the target audience?































Anyone who wants to learn about artificial intelligence, data science, machine learning, and deep learning































Both students and professionals