Book Description

This book discusses the intricacies of the internal workings of a deep learning model. It addresses the techniques and methods that can not only boost the productivity of your machine learning architectural skills, but also introduces new concepts. Implemented correctly, these can set your deep learning model a league apart from all other models.

This book not only focuses on theoretical and conceptual realms of such knowledge, but also gives equal importance to putting this information to the test. We do this by including some common practical examples and demonstrations that you would normally build deep learning for, hence giving you the best of both worlds. The main features of this book include:

• Refreshing the fundamentals of a deep learning model and neural networks and connecting them with the advanced knowledge laid out in this book, reinforcing the reader’s prior knowledge and transforming it into an expert-level understanding.
• Emphasizing those tasks that are commonly demanded from deep learning models and breathing new life into them by introducing new techniques, methods, and elements that enable the model to drastically improve the performance of deep learning models on such tasks.
• Discussing experimentally tested methods that are known to have a positive impact on the model’s effectiveness.
• No usage of mathematical notations in the examples detailed in this book so that the concepts can be readily assimilated and mastered by programmers that do not have a mathematical background, hence prioritizing clarity of concepts.
• Keeping this requirement in mind, the examples use Numpy code throughout as it best represents what the code actually means and its purpose.

If you want to learn advanced strategies for Python this is the book for you.  Click the Buy Now button to get started today!