Provides 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. The book then covers deep learning architectures such as CNNs, RNNs,LSTMs, and auto encoders.
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 appendices for Keras, TensorFlow 2, and Pandas.
Features: - Covers an introduction to programming concepts related to AI, machine learning, and deep learning
- Includes material on Keras, TensorFlow2 and Pandas
