Practical Deep Learning with Python Cover

Practical Deep Learning with Python

A Hands-On Introduction
by Ronald T. Kneusel
January 2021, 450 pp.
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Download Chapter 4: WORKING WITH DATA

The deep-learning revolution is upon us, and it’s never been easier to use AI algorithms and neural networks for large-scale problem solving. You don’t need any experience in machine learning to get started; all that’s required is a standard computer, some popular open-source software tools, and this book.

Expert author Ron Kneusel starts you at the beginning with an introduction to Python, the language ubiquitous in machine learning. He then shows you how to create optimal datasets for training successful deep-learning models, before guiding you through more granular concepts like classic machine-learning algorithms, neural networks, and modern convolutional neural networks (CNNs). Practical knowledge builds on intuition as you work with standard toolkits to gain hands-on experience with various models, conduct experiments based on case studies and, ultimately, apply the book’s examples toward designing your own projects.

Along the way, you’ll learn:

  • Fundamental DL concepts such as classes and labels, constructing datasets, and what a model does
  • How to use standard open-source toolkits (sklearn and Keras/TensorFlow) along with standard datasets (e.g., MNIST and CIFAR-10)
  • How to tune and evaluate the performance of a model via current standards of practice
  • How to train models for classifying handwritten digits, images, audio sounds, and more
  • How CNNs work, and how their outputs are affected by parameter choices

While no one book could possibly capture the entirety of this dynamic, ever-expanding field, Practical Deep Learning with Python will help you get your foot in the pool so you can feel the water – then give you the skills and confidence to dive all in.

Author Bio 

Ronald T. Kneusel earned a PhD in machine learning from the University of Colorado, Boulder, has nearly 20 years of machine learning experience in industry, and is presently pursuing deep-learning projects with L3Harris Technologies, Inc. Kneusel is also the author of Numbers and Computers (2nd ed., Springer 2017) and Random Numbers and Computers (Springer 2018).

Table of contents 

Foreword by Michael C. Mozer, PhD

Chapter 1: Getting Started
Chapter 2: Using Python
Chapter 3: Using NumPy
Chapter 4: Working With Data
Chapter 5: Building Datasets
Chapter 6: Classical Machine Learning
Chapter 7: Experiments with Classical Models
Chapter 8: Introduction to Neural Networks
Chapter 9: Training A Neural Network
Chapter 10: Experiments with Neural Networks
Chapter 11: Evaluating Models
Chapter 12: Introduction to Convolutional Neural Networks
Chapter 13: Experiments with MNIST
Chapter 14: Experiments with CIFAR-10
Chapter 15: A Case Study
Chapter 16: Going Further