Practical Deep Learning: A Python-Based Introduction

Practical Deep Learning: A Python-Based Introduction
  • eBook:
    Practical Deep Learning: A Python-Based Introduction
  • Author:
    Ron Kneusel
  • Edition:
  • Categories:
  • Data:
    February 23, 2021
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    464 pages
  • Format:

Book Description
Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.

If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further.
All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance.
You’ll also learn:

   • How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines
   • How neural networks work and how they’re trained
   • How to use convolutional neural networks
   • How to develop a successful deep learning model from scratch 
You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. 
The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.


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 Keras and MNIST
Chapter 14: Experiments with CIFAR-10
Chapter 15: A Case Study: Classifying Audio Samples
Chapter 16: Going Further

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