Mastering TensorFlow 2.x

Аннотация

This book covers many parts of deep learning concepts with an implementation based on TensorFlow. We will start with the basics and then delve into advanced topics and model tuning as well. The key focus of the book is to introduce a concept, the math behind it, and how the concepts can be used in the TensorFlow library. Most of the samples are based on Jupyter with some of them also based on the Google Colab environment. Book is a healthy combination of theory as well as samples to get you started. Some chapters have taken real-world examples from Kaggle – for example using the PCAM dataset for image classification as well as transfer learning. In some cases, we have also shown how to generate a synthetic dataset for a problem, for example for a time series dataset. While sufficient effort has been made to explain the topics it is almost impossible to do deep dive on each topic. Considering the trade-off between the vast majority of topics to be covered in the book and keeping the volume manageable, we have focused on applications in some cases.