Artificial Intelligence, Machine Learning, and Deep Learning

This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learning classifiers such as logistic regression, k-NN, decision trees, random forests, and SVMs. Next, the book covers deep learning architectures such as CNNs, RNNs, LSTMs, and auto encoders. Keras-based code samples are included to supplement the theoretical discussion. In addition, this book contains separate appendices for Keras, TensorFlow 2, and Pandas. Companion files with code examples available for downloading from the publisher(with Amazon proof of purchase) by writing to info@merclearning.com. Features +Covers an introduction to programming concepts related to AI, machine learning, and deep learning +Includes material on Keras, TensorFlow 2 and Pandas +Companion files with code examples available for downloading from the publisher (with Amazon proof of purchase) by writing to info@merclearning.com Brief Table of Contents 1: Introduction to AI. 2: Introduction to Machine Learning. 3: Classifiers in Machine Learning. 4: Deep Learning Introduction. 5: Deep Learning: RNNs and LSTMs. 6: NLP and Reinforcement Learning. Appendices. A: Introduction to Keras. B: Introduction to TF 2. C: Introduction to Pandas. Index. About the Author Oswald Campesato (San Francisco, CA) specializes in Deep Learning, Java, Android, and TensorFlow. He is the author/co-author of over twenty-five books including TensorFlow 2 Pocket Primer, Angular Pocket Primer, and the Python Pocket Primer (all Mercury Learning). Read more