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 ...
