Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems

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Training NN for Prediction Using Regression

Welcome to our first proper project in Python deep learning! What we'll be doing today is building a classifier to solve the problem of identifying specific handwriting samples from a dataset of images. We've been asked (in this hypothetical use case) to do this by a restaurant chain that has the need to accurately classify handwritten numbers into digits. What they have their customers do is write their phone numbers in a simple iPad application. At the time when they can be seated, the guest will get a text prompting them to come and see the restaurant's host. We need to accurately classify the handwritten numbers, so that the output from the app will be accurately predicted labels for the digits of a phone number. This can then be sent to their (hypothetical) auto dialer service for text messages, and the notice gets...

Key benefits

Explore deep learning across computer vision, natural language processing (NLP), and image processing

Discover best practices for the training of deep neural networks and their deployment

Access popular deep learning models as well as widely used neural network architectures

Description

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

What you will learn

Set up a deep learning development environment on Amazon Web Services (AWS)

Apply GPU-powered instances as well as the deep learning AMI

Implement seq-to-seq networks for modeling natural language processing (NLP)

Develop an end-to-end speech recognition system

Build a system for pixel-wise semantic labeling of an image

Create a system that generates images and their regions

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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beryl sirmacek Jan 08, 2019

Great book for strengthening deep learning skills following the well described examples. You can benefit from this book for your education, teaching activities, as well as for your company and business development in this field.

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bptsj Jan 06, 2019

I plan to spend the next 4mo studying this material and executing the 9 projects. I've had the book for two weeks and so far am not impressed. The first project uses a MLP NN for solving the MNIST handwritten digit problem and concludes the MLP model is not accurate enough (85%). I would have skipped the MLP and just started with the CNN NN. I have done this based on online tutorials and the accuracy was greater than 97%. The book is light on content. On each subject it has a few introductory words then a link to other sources for content and a walk-through on the project code. I think I would have been better off searching the web for the best tutorials on the topics of the 9 projects.To be fair I will update my review when I finish the first 3 projects.

ABHIJIT Jan 20, 2019

Bad print . Very difficult to read.

FutureDoc Aug 31, 2023

I had such high hopes for this book. It is 5 years old, but I assumed the code on GitHub would have been kept updated, like it implies in the book. But the GitHub files haven't been updated in 5 years. And when I try to run these on my local machine as well as Google Colab, I keep getting version errors. I'm not willing to do all the behind the scenes work to get these to run before I can even use them.

Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems

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Python Deep Learning Projects

Training NN for Prediction Using Regression

Building a regression model for prediction using an MLP deep neural network

Exploring the MNIST dataset

Intuition and preparation

Defining regression

Let's code the implementation!

Defining hyperparameters

Concluding the project

Summary

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Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems

What do you get with Print?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

Packt Subscriptions

Frequently bought together

Table of Contents

Recommendations for you

Customer reviews

People who bought this also bought

About the 3 authors

FAQs