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