Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Python Deep Learning
  • Table Of Contents Toc
  • Feedback & Rating feedback
Python Deep Learning

Python Deep Learning

By : Ivan Vasilev
4.9 (15)
close
close
Python Deep Learning

Python Deep Learning

4.9 (15)
By: Ivan Vasilev

Overview of this book

The field of deep learning has developed rapidly recently and today covers a broad range of applications. This makes it challenging to navigate and hard to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today. The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning. The second part of the book introduces convolutional networks for computer vision. We’ll learn how to solve image classification, object detection, instance segmentation, and image generation tasks. The third part focuses on the attention mechanism and transformers – the core network architecture of large language models. We’ll discuss new types of advanced tasks they can solve, such as chatbots and text-to-image generation. By the end of this book, you’ll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models and adapt existing ones to solve your tasks. You’ll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.
Table of Contents (17 chapters)
close
close
1
Part 1:Introduction to Neural Networks
5
Part 2: Deep Neural Networks for Computer Vision
8
Part 3: Natural Language Processing and Transformers
13
Part 4: Developing and Deploying Deep Neural Networks

Introduction to DL

In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton published a milestone paper titled ImageNet Classification with Deep Convolutional Neural Networks (https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf). The paper describes their use of NNs to win the ImageNet competition of the same year, which we mentioned in Chapter 2. At the end of their paper, they noted that the network’s performance degrades even if a single layer is removed. Their experiments demonstrated that removing any of the middle layers resulted in an about 2% top-1 accuracy loss of the model. They concluded that network depth is important for the performance of the network. The basic question is: what makes the network’s depth so important?

A typical English saying is a picture is worth a thousand words. Let’s use this approach to understand what DL is. We’ll use images from the highly cited paper Convolutional...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY