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

In this chapter, we worked to get the team set up in a common environment with a standardized toolset. We are looking to deploy our project applications by utilizing Gunicorn and CUDA. Those projects will rely on highly advanced and effective DL libraries, such as TensorFlow and Keras running in Python 2.x/3.x. We'll write our code using the resources in the Anaconda package, and all of this will be running on Ubuntu 16.04 or greater.

Now we are all set to perform experiments and deploy our DL models in production!