Python Deep Learning Projects

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 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

Who this book is for

Python 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

Table of Contents

1. Building Deep Learning Environment
2. Training Neural Network for Prediction using Regression
3. Word Vector representationusing Word2VEC (skip-gram) for word prediction
4. Build NLP pipeline for Open-Domain Question Answering
5. Sequence-to-sequence models for building chatbots
6. Generative Language modelling using Bi-LSTM for content creation
7. Building Speech Recognition with DeepSpeech2
8. Handwritten digits classification using ConvNets
9. Real-time Object Detection using OpenCV and TensorFlow
10. Building Face Recognition using OpenFace and Clustering
11. Automated Image Captioning with NeuralTalk model
12. Pose Estimation on 3D models using ConvNets
13. Image translation using GANs for style transfer
14. Develop anautonomous Agents with Deep Reinforcement Learning
15. Summary and Next Steps in Your Deep Learning Career

Rahul Kumar has more than 9 years of experience in Machine Learning and more than 2 years in Deep Learning. He is currently working as Data Scientist at Mercedes-Benz Research and Development in Bangalore. He has an experience of working in diverse fields such as banking, financial services, telecom and automotive. His expertise lies in Machine learning, Deep learning, Business Analytics, Data Analytics. Rahul has acquired coursera certifications for machine learning, deep learning, CNNs, neural networks.

Matthew Lamons founded the Applied AI Masterminds as well as built a data science community in excess of 55,000 made up of University Researchers exploring Artificial Intelligence and Applied Data Scientists, and machine learning engineers creating cutting edge applications in financial forecasting, Robotics, Autonomous Vehicles, Marketing Tech, Healthcare, with the most sophisticated AI technologies such as NLP, Computer Vision, Reinforcement Learning, and Deep Learning.