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

Building Deep Learning Environments

Welcome to the applied AI deep-learning team, and to our first project—Building a Common Deep Learning Environment! We're excited about the projects we've assembled in this book. The foundation of a common working environment will help us work together and learn very cool and powerful deep learning (DL) technologies, such as computer vision (CV) and natural language processing (NLP), that you will be able to use in your professional career as a data scientist.

The following topics will be covered in this chapter:

  • Components in building a common DL environment
  • Setting up a local DL environment
  • Setting up a DL environment in the cloud
  • Using the cloud for deployment for DL applications
  • Automating the setup process to reduce errors and get started quickly