DL with Python: Fundamentals of machine learning (Chapter 5)

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 5th chapter of the book.
We explain the tension between generalization and optimization, the fundamental issue in machine learning. We saw various valuation methods for machine learning models as well best practices to improve model fitting and achieve better generalization.
The code that we read and run is based on:

👉 SESSION STRUCTURE

At each session, we go through the notebook(s) of one chapter of the book. The notebooks can be found here:
We explain the theoretical background needed to understand the code, read and run the code.
We strongly recommend reading this book since it’s truly well written and it is worth your time. You can also read the book chapter-wise and join specific sessions since the presentations are meant to be stand-alone. Join us on this 3-month journey of weekly study sessions, from zero to advanced Deep Learning!

👉 PRESENTER BIO
Dimitris Katsios is a Machine Learning Engineer at LPIXEL where he contributes to the development of Deep Learning assisted medical diagnosis tools. Before that, he carried out his internship at Fujitsu Labs where he worked on Computer Vision applications.
Dimitris studied Primary Education and worked as a primary school teacher for several years. During that period he studied Industrial Engineering, received an MSc degree in Information and Communication Systems Engineering, and an MSc in Intelligent Information Systems among others. He has been an instructor and speaker at various Deep Learning-related workshops and talks in Tokyo where he currently lives. He’s one of the Directors at Machine Learning Tokyo - MLT, a nonprofit organization and one of the biggest communities of ML engineers and practitioners in Japan.

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MLT (Machine Learning Tokyo)