Deep Learning with Python: Mathematical building blocks of neural networks (Chapter 2)

Deep Learning with Python Study Series based on the new edition of the popular book “Deep Learning with Python” by François Chollet In this study series, we go through the chapters of the book, explain the theoretical part, run the code, answer questions and discuss various topics related to Deep Learning. The book covers a vast range of Deep Learning topics. Starting with Machine Learning and Deep Learning basics and using simple examples and easy-to-follow code, it gets us to an advanced level of DL applications. It covers many different fields of DL, from tabular data, image classification, segmentation, and generation to the use of Transformers in NLP. Due to its unique structure, one can become familiar with the code of such projects even without prior knowledge of DL. 👉 SESSION DESCRIPTION During the session, we went through the 2nd chapter of the book, (chapter 2- Mathematical building blocks of neural networks). We saw our first example of a neural network, learn what are the tensors and tensor opera