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Full Description
Insightful projects to master deep learning and neural network architectures using Python and Keras
Key Features
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
Book DescriptionDeep 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
Who this book is forPython Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects.
It is assumed that you have sound knowledge of Python programming
Contents
Table of Contents
Building Deep Learning Environment
Training Neural Network for Prediction using Regression
Word Vector representationusing Word2VEC (skip-gram) for word prediction
Build NLP pipeline for Open-Domain Question Answering
Sequence-to-sequence models for building chatbots
Generative Language modelling using Bi-LSTM for content creation
Building Speech Recognition with DeepSpeech2
Handwritten digits classification using ConvNets
Real-time Object Detection using OpenCV and TensorFlow
Building Face Recognition using OpenFace and Clustering
Automated Image Captioning with NeuralTalk model
Pose Estimation on 3D models using ConvNets
Image translation using GANs for style transfer
Develop anautonomous Agents with Deep Reinforcement Learning
Summary and Next Steps in Your Deep Learning Career
