Expectations

This book is best suited for the reader who:

• Has intermediate proficiency in Python
• Possesses a basic understanding of machine learning and deep learning, especially for the following topics:
  • Neural networks
  • Backpropagation
  • Convolution
  • Techniques for better generalization and reduced overfitting
• Enjoys a hands-on, practical approach toward learning

Since this book serves as a practical introduction to the field, we try to keep theoretical content to a minimum. However, it is advisable for the reader to have basic knowledge of some of the fundamental mathematical and statistical concepts on which the field of machine learning depends. These include the following:

• Calculus (single and multivariate)
• Linear algebra
• Probability theory
• Graph theory

Having some experience with these subjects would greatly assist the reader in understanding the concepts and algorithms we will cover throughout this book.