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0 (hodnocen0 x )

EB

EB

ONLINE

1st ed.

Birmingham : Packt Publishing, Limited, 2022

1 online zdroj (366 stran)

ISBN 9781800569270 (e-kniha)

Print version: Sawarkar, Kunal Deep Learning with Pytorch Lightning Birmingham : Packt Publishing, Limited,c2022

002001764

Preface -- Section 1: Kickstarting with PyTorch Lightning -- Chapter 1: PyTorch Lightning Adventure -- What makes PyTorch Lightning so special? -- The first one…. -- So many frameworks? -- PyTorch versus TensorFlow -- A golden mean - PyTorch Lightning -- -- - My Lightning adventure -- Understanding the key components of PyTorch Lightning -- DL pipeline -- PyTorch Lightning abstraction layers -- Crafting AI applications using PyTorch Lightning -- Image recognition models -- Transfer learning -- NLP Transformer models -- Lightning Flash -- Time series models with LSTM -- Generative Adversarial Networks with Autoencoders -- Self-Supervised models combining CNN and RNN -- Self-Supervised models for contrastive learning -- Deploying and scoring models -- Scaling models and productivity tips -- Further reading -- Summary -- Chapter 2: Getting off the Ground with the First Deep Learning Model -- Technical requirements -- Getting started with Neural Networks -- Why Neural Networks? -- About the XOR operator -- MLP architecture -- Building a Hello World MLP model -- Importing libraries -- Preparing the data -- Configuring the model -- Training the model -- Loading the model -- Making predictions -- Building our first Deep Learning model -- So, what makes it deep? -- CNN architecture -- Building a CNN model for image recognition -- Importing the packages -- Collecting the data -- Preparing the data -- Building the model -- Training the model -- Evaluating the accuracy of the model -- Model improvement exercises -- Summary -- Chapter 3: Transfer Learning Using Pre-Trained Models -- Technical requirements -- Getting started with transfer learning -- An image classifier using a pre-trained ResNet-50 architecture -- Preparing the data --

Extracting the dataset -- Pre-processing the dataset -- Loading the dataset -- Building the model -- Training the model -- Evaluating the accuracy of the model -- Text classification using BERT transformers -- Collecting the data -- Preparing the dataset -- Setting up the DataLoader instances -- Building the model -- Setting up model training and testing -- Training the model -- Evaluating the model -- Summary -- Chapter 4: Ready-to-Cook Models from Lightning Flash -- Technical requirements -- Getting started with Lightning Flash -- Flash is as simple as 1-2-3 -- Video classification using Flash -- Slow and SlowFast architecture -- Importing libraries -- Loading the dataset -- Configuring the backbone -- Fine-tuning the model -- Making predictions using the model -- Automatic speech recognition using Flash -- Installing Libraries -- Importing libraries -- Loading the dataset -- Configuring the backbone -- Fine-tuning the model -- Speech prediction using the model -- Further learning -- Summary -- Section 2: Solving using PyTorch Lightning -- Chapter 5: Time Series Models -- Technical requirements -- Introduction to time series -- Time series forecasting using Deep Learning -- Getting started with time series models -- Traffic volume forecasting using the LSTM time series model -- Dataset analysis -- Feature engineering -- Creating a custom dataset -- Configuring the LSTM model using PyTorch Lightning -- Setting up the optimizer -- Training the model -- Measuring the training loss -- Loading the model -- A prediction on the test dataset -- The next steps -- Summary -- Chapter 6: Deep Generative Models -- Technical requirements -- Getting started with GAN models -- What is a GAN? -- Creating new food items using a GAN -- Loading the dataset -- Feature engineering utility functions -- The discriminator model -- The generator model --

The generative adversarial model -- Training the GAN model -- The model output showing fake images -- Creating new butterfly species using a GAN -- GAN training challenges -- Creating images using DCGAN -- Summary -- Chapter 7: Semi-Supervised Learning -- Technical requirements -- Getting started with semi-supervised learning -- Going through the CNN-RNN architecture -- Generating captions for images -- Downloading the dataset -- Assembling the data -- Training the model -- Generating the caption -- Next steps -- Summary -- Chapter 8: Self-Supervised Learning -- Technical requirements -- Getting started with Self-Supervised Learning -- So, what does it mean to be Self-Supervised? -- What is Contrastive Learning? -- SimCLR architecture -- How does SimCLR work? -- SimCLR model for image recognition -- Collecting the dataset -- Setting up data augmentation -- Loading the dataset -- Training configuration -- Model training -- Model evaluation -- Next steps -- Summary -- Section 3: Advanced Topics -- Chapter 9: Deploying and Scoring Models -- Technical requirements -- Deploying and scoring a Deep Learning model natively -- The pickle (.PKL) model file format -- Deploying our Deep Learning model -- Saving and loading model checkpoints -- Deploying and scoring a model using Flask -- Deploying and scoring inter-portable models -- What is the ONNX format? Why does it matter? -- Saving and loading the ONNX model -- Deploying and scoring the ONNX model using Flask -- Next steps -- Further reading -- Summary -- Chapter 10: Scaling and Managing Training -- Technical Requirements -- Managing training -- Saving model hyperparameters -- Efficient debugging -- Monitoring the training loss using TensorBoard -- Scaling up training -- Speeding up model training using a number of workers -- GPU/TPU training -- Mixed precision training/16-bit training --

Controlling training -- Saving model checkpoints when using the cloud -- Changing the default behavior of the checkpointing feature -- Resuming training from a saved checkpoint -- Saving downloaded and assembled data when using the cloud -- Further reading -- Summary -- Index -- Other Books You May Enjoy.

(MiAaPQ)EBC6845748

(Au-PeEL)EBL6845748

(OCoLC)1293248881