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 ...

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