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...
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Table Of Contents
Python Deep Learning Projects
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Python Deep Learning Projects
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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)
Preface
Free Chapter
Building Deep Learning Environments
Training NN for Prediction Using Regression
Word Representation Using word2vec
Building an NLP Pipeline for Building Chatbots
Sequence-to-Sequence Models for Building Chatbots
Generative Language Model for Content Creation
Building Speech Recognition with DeepSpeech2
Handwritten Digits Classification Using ConvNets
Object Detection Using OpenCV and TensorFlow
Building Face Recognition Using FaceNet
Automated Image Captioning
Pose Estimation on 3D models Using ConvNets
Image Translation Using GANs for Style Transfer
Develop an Autonomous Agent with Deep R Learning
Summary and Next Steps in Your Deep Learning Career
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