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Deep Learning with PyTorch : a practical approach to building neural network models using PyTorch.

Author: Vishnu Subramanian
Publisher: Birmingham : Packt Publishing, 2018.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This book provides the intuition behind the state of the art Deep Learning architectures such as ResNet, DenseNet, Inception, and encoder-decoder without diving deep into the math of it. It shows how you can implement and use various architectures to solve problems in the area of image classification, language translation and NLP using PyTorch.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Subramanian, Vishnu.
Deep Learning with PyTorch : A practical approach to building neural network models using PyTorch.
Birmingham : Packt Publishing, ©2018
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Vishnu Subramanian
ISBN: 9781788626071 1788626079 9781788624336 1788624335
OCLC Number: 1028224970
Notes: Understanding what a CNN model learnsÂ
Description: 1 online resource (255 pages)
Contents: Cover; Copyright and Credits; Dedication; Packt Upsell; Foreword; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Deep Learning Using PyTorch; Artificial intelligence; The history of AI; Machine learning; Examples of machine learning in real life; Deep learning; Applications of deep learning; Hype associated with deep learning ; The history of deep learning ; Why now?; Hardware availability; Data and algorithms; Deep learning frameworks; PyTorch; Summary; Chapter 2: Building Blocks of Neural Networks; Installing PyTorch; Our first neural network; Data preparation. Scalar (0-D tensors) Vectors (1-D tensors); Matrix (2-D tensors); 3-D tensors; Slicing tensors ; 4-D tensors; 5-D tensors; Tensors on GPU; Variables; Creating data for our neural network; Creating learnable parameters; Neural network model; Network implementation ; Loss function; Optimize the neural network ; Loading data ; Dataset class; DataLoader class; Summary ; Chapter 3: Diving Deep into Neural Networks; Deep dive into the building blocks of neural networks; Layers â#x80;#x93; fundamental blocks of neural networks; Non-linear activations; Sigmoid; Tanh; ReLU; Leaky ReLU. PyTorch non-linear activationsThe PyTorch way of building deep learning algorithms; Model architecture for different machine learning problems; Loss functions; Optimizing network architecture; Image classification using deep learning; Loading data into PyTorch tensors; Loading PyTorch tensors as batches; Building the network architecture; Training the model ; Summary; Chapter 4: Fundamentals of Machine Learning; Three kinds of machine learning problems; Supervised learning; Unsupervised learning; Reinforcement learning; Machine learning glossary; Evaluating machine learning models. Training, validation, and test splitSimple holdout validation; K-fold validation; K-fold validation with shuffling ; Data representativeness ; Time sensitivity; Data redundancy; Data preprocessing and feature engineering; Vectorization; Value normalization; Handling missing values; Feature engineering; Overfitting and underfitting; Getting more data; Reducing the size of the network; Applying weight regularization; Dropout; Underfitting; Workflow of a machine learning project; Problem definition and dataset creation; Measure of success ; Evaluation protocol; Prepare your data. Baseline modelLarge model enough to overfit; Applying regularization; Learning rate picking strategies ; Summary; Chapter 5: Deep Learning for Computer Vision; Introduction to neural networks; MNIST â#x80;#x93; getting data; Building a CNN model from scratch; Conv2d; Pooling; Nonlinear activation â#x80;#x93; ReLU; View; Linear layer; Training the model; Classifying dogs and cats â#x80;#x93; CNN from scratch; Classifying dogs and cats using transfer learning; Creating and exploring a VGG16 model ; Freezing the layers; Fine-tuning VGG16; Training the VGG16 model ; Calculating pre-convoluted features.

Abstract:

This book provides the intuition behind the state of the art Deep Learning architectures such as ResNet, DenseNet, Inception, and encoder-decoder without diving deep into the math of it. It shows how  Read more...

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