Название: Machine Learning, Animated
Автор: Mark Liu
Издательство: CRC Press
Год: 2024
Страниц: 465
Язык: английский
Формат: pdf (true)
Размер: 17.4 MB

The release of ChatGPT has kicked off an arms race in Machine Learning (ML), however ML has also been described as a black box and very hard to understand. Machine Learning, Animated eases you into basic ML concepts and summarizes the learning process in three words: initialize, adjust and repeat. This is illustrated step by step with animation to show how machines learn: from initial parameter values to adjusting each step, to the final converged parameters and predictions.

This book teaches readers to create their own neural networks with dense and convolutional layers, and use them to make binary and multi-category classifications. Readers will learn how to build Deep Learning game strategies and combine this with reinforcement learning, witnessing AI achieve super-human performance in Atari games such as Breakout, Space Invaders, Seaquest and Beam Rider.

Machine Learning (ML) is redefining the way we live nowadays: it’s integrated into an increasing number of products and services in the economy, from recommender systems to language translations, from voice assistants, medical imaging, to self-driving cars... ML, especially deep learning, has made great strides in the last couple of decades, largely due to the advancements in computing power (such as graphics processing unit (GPU) training and distributed computing) and the exploding amount of data available to train deep neural networks.

The recent release of ChatGPT by OpenAI has upped the ante in the game, forcing Google and other competitors to release large language models of their own. Different organizations and institutions have realized that an arms race in the field of ML and Artificial Intelligence (AI) is on. Everyone in every profession must adapt or face the risk of becoming a dinosaur and getting left behind.

As such, the book takes a practical rather than technical approach to ML. The book provides an intuitive explanation of concepts such as Deep Learning, Q-learning, or the policy-gradient algorithm. You’ll learn how to implement these algorithms by following the examples and how to apply them to your own field, be that business, biology, medicine, or something else entirely. While most models are built by using the TensorFlow Keras API, you also learn to create ML models from scratch on your own, without resorting to any API. Along the way, you’ll know how ML models are constructed, how the parameter values are initialized and then gradually adjusted during the training process, how parameters converge, and how the trained models make accurate predictions.

This book is divided into six parts. Part I discusses how to install Python and how to create animations with Python libraries. Part II introduces you to ML basics such as the gradient descent optimization algorithm, the learning rate, loss functions, and activation functions. Part III covers binary and multi-category classifications and introduces you to neural networks. In Part IV, we build deep learning game strategies in OpenAI Gym games as well as in multi-player games such as Tic Tac Toe and Connect Four. Part V introduces you to the basics of reinforcement learning. In Part VI, we combine Deep Learning with reinforcement learning to create deep reinforcement learning game strategies, so you can create a double deep Q-network to train all Atari games (Breakout, Space Invaders, Seaquest, and so on).

Written in a clear and concise style, illustrated with animations and images, this book is particularly appealing to readers with no background in computer science, mathematics or statistics.

Access the book's repository at:https://github.com/markhliu/MLA

"Artificial Intelligence, Deep Learning, Machine Learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years." - Mark Cuban




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