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Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems
Insightful projects to master deep learning and neural network architectures using Python and Keras
Key FeaturesExplore deep learning across computer vision, natural language processing (NLP), and image processingDiscover best practices for the training of deep neural networks and their deploymentAccess popular deep learning models as well as widely used neural network architecturesBook 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 learnSet up a deep learning development environment on Amazon Web Services (AWS)Apply GPU-powered instances as well as the deep learning AMIImplement seq-to-seq networks for modeling natural language processing (NLP)Develop an end-to-end speech recognition systemBuild a system for pixel-wise semantic labeling of an imageCreate a system that generates images and their regionsWho 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
Table of ContentsBuilding Deep Learning EnvironmentTraining Neural Network for Prediction using RegressionWord Vector representationusing Word2VEC (skip-gram) for word predictionBuild NLP pipeline for Open-Domain Question AnsweringSequence-to-sequence models for building chatbotsGenerative Language modelling using Bi-LSTM for content creationBuilding Speech Recognition with DeepSpeech2Handwritten digits classification using ConvNetsReal-time Object Detection using OpenCV and TensorFlowBuilding Face Recognition using OpenFace and ClusteringAutomated Image Captioning with NeuralTalk modelPose Estimation on 3D models using ConvNetsImage translation using GANs for style transferDevelop anautonomous Agents with Deep Reinforcement LearningSummary and Next Steps in Your Deep Learning Career
Key FeaturesExplore deep learning across computer vision, natural language processing (NLP), and image processingDiscover best practices for the training of deep neural networks and their deploymentAccess popular deep learning models as well as widely used neural network architecturesBook 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 learnSet up a deep learning development environment on Amazon Web Services (AWS)Apply GPU-powered instances as well as the deep learning AMIImplement seq-to-seq networks for modeling natural language processing (NLP)Develop an end-to-end speech recognition systemBuild a system for pixel-wise semantic labeling of an imageCreate a system that generates images and their regionsWho 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
Table of ContentsBuilding Deep Learning EnvironmentTraining Neural Network for Prediction using RegressionWord Vector representationusing Word2VEC (skip-gram) for word predictionBuild NLP pipeline for Open-Domain Question AnsweringSequence-to-sequence models for building chatbotsGenerative Language modelling using Bi-LSTM for content creationBuilding Speech Recognition with DeepSpeech2Handwritten digits classification using ConvNetsReal-time Object Detection using OpenCV and TensorFlowBuilding Face Recognition using OpenFace and ClusteringAutomated Image Captioning with NeuralTalk modelPose Estimation on 3D models using ConvNetsImage translation using GANs for style transferDevelop anautonomous Agents with Deep Reinforcement LearningSummary and Next Steps in Your Deep Learning Career
474 pages, Kindle Edition
Published October 31, 2018
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