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16 Free Machine Learning Books

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The following is a list of free books on Machine Learning. A Brief Introduction To Neural Networks provides a comprehensive overview of the subject of neural networks and is divided into 4 parts –Part I: From Biology to Formalization -- Motivation, Philosophy, History and Realization of Neural Models,Part II: Supervised learning Network Paradigms, Part III: Unsupervised learning Network Paradigms and Part IV: Excursi, Appendices and Registers. A Course In Machine Learning is designed to provide a gentle and pedagogically organized introduction to the field and provide a view of machine learning that focuses on ideas and models, not on math. The audience of this book is anyone who knows differential calculus and discrete math, and can program reasonably well. An undergraduate in their fourth or fifth semester should be fully capable of understanding this material.


Top 10 Reinforcement Learning Courses & Certifications in 2020

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Reinforcement Learning is one of the most in demand research topics whose popularity is only growing day by day. An RL expert learns from experience, rather than being explicitly taught, which is essentially trial and error learning. To understand RL, Analytics Insight compiles the Top 10 Reinforcement Learning Courses and Certifications in 2020. The reinforcement learning specialization consists of four courses that explore the power of adaptive learning systems and artificial intelligence (AI). On this MOOC course, you will learn how Reinforcement Learning (RL) solutions help to solve real-world problems through trial-and-error interaction by implementing a complete RL solution.


Schooling Flappy Bird: A Reinforcement Learning Tutorial

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In classical programming, software instructions are explicitly made by programmers and nothing is learned from the data at all. In contrast, machine learning is a field of computer science which uses statistical methods to enable computers to learn and to extract knowledge from the data without being explicitly programmed. In this reinforcement learning tutorial, I'll show how we can use PyTorch to teach a reinforcement learning neural network how to play Flappy Bird. But first, we'll need to cover a number of building blocks. Machine learning algorithms can roughly be divided into two parts: Traditional learning algorithms and deep learning algorithms. Traditional learning algorithms usually have much fewer learnable parameters than deep learning algorithms and have much less learning capacity. Also, traditional learning algorithms are not able to do feature extraction: Artificial intelligence specialists need to figure out a good data representation which is then sent to the learning algorithm. Examples of traditional machine learning techniques include SVM, random forest, decision tree, and $k$-means, whereas the central algorithm in deep learning is the deep neural network.


17 Best Online Courses on Machine Learning, Deep Learning, AI and Big Data Analytics

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You will learn how to use Python to analyze data (big data analytics), create beautiful visualizations (data visualization) and use powerful machine learning algorithms. You will specifically get to learn how to use NumPy, Seaborn, Matplotlib, Pandas, Scikit-Learn, Machine Learning, Plotly, Tensorflow and more.


Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning

Neural Information Processing Systems

The combination of modern Reinforcement Learning and Deep Learning approaches holds the promise of making significant progress on challenging applications requiring both rich perception and policy-selection. The Arcade Learning Environment (ALE) provides a set of Atari games that represent a useful benchmark set of such applications. A recent breakthrough in combining model-free reinforcement learning with deep learning, called DQN, achieves the best real-time agents thus far. Planning-based approaches achieve far higher scores than the best model-free approaches, but they exploit information that is not available to human players, and they are orders of magnitude slower than needed for real-time play. Our main goal in this work is to build a better real-time Atari game playing agent than DQN. The central idea is to use the slow planning-based agents to provide training data for a deep-learning architecture capable of real-time play. We proposed new agents based on this idea and show that they outperform DQN.