Machine Learning Math: A Complete Guide to Machine Learning for Beginners with Tensorflow. This Book Explains How to Build Artificial Intelli

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In this book, you will learn about the OpenAI Gym, used in reinforcement learning projects with several examples of the training platform provided out of the box. Machine Learning Math is the book most readers will want to have when starting to learn machine learning. This book is a reference, something you can keep coming back to hence suitable for newbies.

The book is perfect for all people who have a desire to study data science. Have you heard of machine learning being everywhere, and you intend to understand what it can do? Or are you familiar with applying the tools of machine learning, but you want to make sure you aren't missing any?

Having a little knowledge about mathematics, statistics, and probability would be helpful, but this book has been written in such a way that you will get most of this knowledge as you continue reading. You should not shy away from reading the book if you have no background in machine learning.

You will learn how to use reinforcement learning algorithms in other tasks, for example, the board game Go, and generating deep image classifiers. This will help you to get a comprehensive understanding of reinforcement learning and help you solve real-world problems. The most interesting part of this book is the asynchronous reinforcement learning framework. You will learn what the shortcomings of DQN are, and why DQN is challenging to apply in complex tasks. Then, you will learn how to apply the asynchronous reinforcement learning framework in the actor-critic method REINFORCE, which led us to the A3C algorithm.

You will learn four important things. The first one is how to implement games using gym and how to play games for relaxation and having fun. The second one is that you will learn how to preprocess data in reinforcement learning tasks such as in computer games. For practical machine learning applications, you will spend a great deal of time understanding and refining data, which affects the performance of an AI system a lot. The third one is the deep Q-learning algorithm. You will learn the intuition behind it, for example, why the replay memory is necessary, why the target network is needed, where the update rule comes from, and so on. The final one is that you will learn how to implement DQN using TensorFlow and how to visualize the training process.

The following is a glimpse of what you will find inside the book:

• Introduction to machine learning
• The best machine learning algorithms
• Regression (a problem of predicting a real-valued label) and classification( a problem of automatically assigning a label to unlabeled example-for example spam detection)
• Reinforcement learning
• Robotics
• Supervised and Unsupervised learning
• How to implement a convolutional neural network(usually used for images) in TensorFlow
• Deep Learning
• Data preparation and processing
• TensorFlow machine learning frameworks
• Neural Networks (a combination of linear and non-linear functions)
• Clustering(aims to group similar samples together)

Even if you have never studied Machine Learning before, you can learn it quickly.

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