Python Deep Learning Projects
9 projects demystifying neural network and deep learning models for building intelligent systems
Published on 31. October 2018
Book
Paperback/Softback
472 pages
978-1-78899-709-6 (ISBN)
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
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
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 26 mm
Weight
874 gr
ISBN-13
978-1-78899-709-6 (9781788997096)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Matthew Lamons | Rahul Kumar | Abhishek Nagaraja
Python Deep Learning Projects
9 projects demystifying neural network and deep learning models for building intelligent systems
E-Book
06/2024
Packt Publishing
€41.49
Available for download
Persons
Matthew Lamons's background is in experimental psychology and deep learning. Founder and CEO of Skejulthe AI platform to help people manage their activities. Named by Gartner, Inc. as a "Cool Vendor" in the "Cool Vendors in Unified Communication, 2017" report. He founded The Intelligence Factory to build AI strategy, solutions, insights, and talent for enterprise clients and incubate AI tech startups based on the success of his Applied AI MasterMinds group. Matthew's global community of more than 85 K are leaders in AI, forecasting, robotics, autonomous vehicles, marketing tech, NLP, computer vision, reinforcement, and deep learning. Matthew invites you to join him on his mission to simplify the future and to build AI for good. Rahul Kumar is an AI scientist, deep learning practitioner, and independent researcher. His expertise in building multilingual NLU systems and large-scale AI infrastructures has brought him to Copenhagen, where he leads a large team of AI engineers as Chief AI Scientist at Jatana. Often invited to speak at AI conferences, he frequently travels between India, Europe, and the US where, among other research initiatives, he collaborates with The Intelligence Factory as NLP data science fellow. Keen to explore the ramifications of emerging technologies for his next book, he's currently involved in various research projects on Quantum Computing (QC), high-performance computing (HPC), and the brain-computer interaction (BCI). Abhishek Nagaraja was born and raised in India. Graduated Magna Cum Laude from the University of Illinois at Chicago, United States, with a Masters Degree in Mechanical Engineering with a concentration in Mechatronics and Data Science. Abhishek specializes in Keras and TensorFlow for building and evaluation of custom architectures in deep learning recommendation models. His deep learning skills and interest span computational linguistics and NLP to build chatbots to computer vision and reinforcement learning. He has been working as a Data Scientist for Skejul Inc. building an AI-powered activity forecast engine and engaged as a Deep Learning Data Scientist with The Intelligence Factory building solutions for enterprise clients.
Content
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
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