Détail de l'auteur
Auteur V Kishore AYYADEVARA |
Documents disponibles écrits par cet auteur (2)
Titre : Modern Computer Vision with PyTorch Type de document : e-book Auteurs : V Kishore AYYADEVARA Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781839213472 Note générale : copyrighted Langues : Anglais (eng) Résumé : Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questionsKey FeaturesImplement solutions to 50 real-world computer vision applications using PyTorchUnderstand the theory and working mechanisms of neural network architectures and their implementationDiscover best practices using a custom library created especially for this bookBook DescriptionDeep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets. You'll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You'll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you'll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You'll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud. By the end of this book, you'll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.What you will learnTrain a NN from scratch with NumPy and PyTorchImplement 2D and 3D multi-object detection and segmentationGenerate digits and DeepFakes with autoencoders and advanced GANsManipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGANCombine CV with NLP to perform OCR, image captioning, and object detectionCombine CV with reinforcement learning to build agents that play pong and self-drive a carDeploy a deep learning model on the AWS server using FastAPI and DockerImplement over 35 NN architectures and common OpenCV utilitiesWho this book is forThis book is for beginners to PyTorch and intermediate-level machine learning practitioners who are looking to get well-versed with computer vision techniques using deep learning and PyTorch. If you are just getting started with neural networks, you'll find the use cases accompanied by notebooks in GitHub present in this book useful. Basic knowledge of the Python programming language and machine learning is all you need to get started with this book. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88906162 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=525332
Titre : Neural Networks with Keras Cookbook Type de document : e-book Auteurs : V Kishore AYYADEVARA Editeur : PACKT PUBLISHING Année de publication : 2019 ISBN/ISSN/EAN : 9781789346640 Note générale : copyrighted Langues : Anglais (eng) Résumé :
Implement neural network architectures by building them from scratch for multiple real-world applications.
Key Features
From scratch, build multiple neural network architectures such as CNN, RNN, LSTM in Keras
Discover tips and tricks for designing a robust neural network to solve real-world problems
Graduate from understanding the working details of neural networks and master the art of fine-tuning them
Book Description
This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach.
We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data.
Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks.
We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems.
Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game.
By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.
What you will learn
Build multiple advanced neural network architectures from scratch
Explore transfer learning to perform object detection and classification
Build self-driving car applications using instance and semantic segmentation
Understand data encoding for image, text and recommender systems
Implement text analysis using sequence-to-sequence learning
Leverage a combination of CNN and RNN to perform end-to-end learning
Build agents to play games using deep Q-learning
Who this book is for
This intermediate-level book targets beginners and intermediate-level machine learning practitioners and data scientists who have just started their journey with neural networks. This book is for those who are looking for resources to help them navigate through the various neural network architectures; you'll build multiple architectures, with concomitant case studies ordered by the complexity of the problem. A basic understanding of Python programming and a familiarity with basic machine learning are all you need to get started with this book.Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88866861 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=484609
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