Artificial Intelligence, Machine Learning, and Deep Learning

Paperback
February 2020
9781683924678
More details
  • Publisher
    Mercury Learning and Information
  • Published
    13th February 2020
  • ISBN 9781683924678
  • Language English
  • Pages 300 pp.
  • Size 7" x 9"
$59.95
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January 2020
9781683924654
More details
  • Publisher
    Mercury Learning and Information
  • Published
    23rd January 2020
  • ISBN 9781683924654
  • Language English
  • Pages 300 pp.
  • Size 7" x 9"
$179.95
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January 2020
9781683924661
More details
  • Publisher
    Mercury Learning and Information
  • Published
    23rd January 2020
  • ISBN 9781683924661
  • Language English
  • Pages 300 pp.
  • Size 7" x 9"
$41.95

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

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 TF2
C: Introduction to Pandas
Index

Oswald Campesato

Oswald Campesato (San Francisco, CA) is an adjunct instructor at UC-Santa Clara and specializes in Deep Learning, Java, Android, TensorFlow, and NLP. He is the author/co-author of over twenty-five books including TensorFlow 2 Pocket Primer, Python 3 for Machine Learning, and the NLP Using R Pocket Primer (all Mercury Learning and Information).

computer science