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

Conclusion

This project was all about building a CNN classifier to classify handwritten digits better than we did in Chapter 2, Training NN for Prediction Using Regression, with a multilayer Perceptron.

Our deep convolution neural network classifier with max pooling and dropout hit 99.01% accuracy on a test set of 10,000 images/digits. This is good. This is almost 12% better than our multilayer Perceptron model.

However, there are some implications. What are the implications of this accuracy? It is important that we understand this. Just like we did in Chapter 2, Training NN for Prediction Using Regression, let's calculate the incidence of an error occurring that would result in a customer service issue.

Just to refresh our memory, in this hypothetical use case, we assumed that the restaurant has an average of 30 tables at each location, and that those tables turn over two...