Book Image

Python Deep Learning Projects

By : Matthew Lamons, Rahul Kumar, Abhishek Nagaraja
Book Image

Python Deep Learning Projects

By: Matthew Lamons, Rahul Kumar, Abhishek Nagaraja

Overview of this book

Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way
Table of Contents (17 chapters)
8
Handwritten Digits Classification Using ConvNets

Summary and Next Steps in Your Deep Learning Career

This has been a fantastic journey and you've been quite productive as a member of the team! We hope that you've enjoyed our practical approach to teaching Python Deep Learning Projects. Furthermore, it was our intention to provide you with thought-provoking and exciting experiences that will further your intuition and form the technical foundation for your career in deep learning engineering.

Each chapter was structured similarly to participating as a member of our Intelligence Factory team, where, by going through the material, we achieved the following:

  • Saw the big picture of the real-world use case and identified the success criteria
  • Got focused and into the code, loaded dependencies and data, and built, trained, and evaluated our models
  • Expanded back out to the big picture to confirm that we achieved our goal
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