A.I. & Optimization

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.

Neural networks are at the core of recent artificial intelligence (AI) advances, providing some of the best solutions to many real-world problems, such as image recognition, medical diagnosis, and text analysis. This book takes you through the fundamental neural network and deep learning concepts, as well as popular Python libraries for implementing them. You will discover practical demonstrations of neural networks in domains such as fare prediction, image classification, and sentiment analysis. In each case, the book will provide a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and even the associated Python code to implement the solution from scratch.