Deep Learning with PyTorch, Second Edition
portes grátis
Deep Learning with PyTorch, Second Edition
Huang, Howard
Manning Publications
03/2026
600
Dura
Inglês
9781633438859
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
PART 1: CORE PYTORCH
1 INTRODUCING DEEP LEARNING AND THE PYTORCH LIBRARY
2 PRETRAINED NETWORKS
3 IT STARTS WITH A TENSOR
4 REAL-WORLD DATA REPRESENTATION USING TENSORS
5 THE MECHANICS OF LEARNING
6 USING A NEURAL NETWORK TO FIT THE DATA
7 TELLING BIRDS FROM AIRPLANES: LEARNING FROM IMAGES
8 USING CONVOLUTIONS TO GENERALIZE
PART 2: PRACTICAL APPLICATIONS
9 HOW TRANSFORMERS WORK
10 DIFFUSION MODELS FOR IMAGES
11 USING PYTORCH TO FIGHT CANCER
12 COMBINING DATA SOURCES INTO A UNIFIED DATASET
13 TRAINING A CLASSIFICATION MODEL TO DETECT SUSPECTED TUMORS
14 IMPROVING TRAINING WITH METRICS AND AUGMENTATION
15 USING SEGMENTATION TO FIND SUSPECTED NODULES
16 TRAINING MODELS ON MULTIPLE GPUS
17 DEPLOYING TO PRODUCTION
1 INTRODUCING DEEP LEARNING AND THE PYTORCH LIBRARY
2 PRETRAINED NETWORKS
3 IT STARTS WITH A TENSOR
4 REAL-WORLD DATA REPRESENTATION USING TENSORS
5 THE MECHANICS OF LEARNING
6 USING A NEURAL NETWORK TO FIT THE DATA
7 TELLING BIRDS FROM AIRPLANES: LEARNING FROM IMAGES
8 USING CONVOLUTIONS TO GENERALIZE
PART 2: PRACTICAL APPLICATIONS
9 HOW TRANSFORMERS WORK
10 DIFFUSION MODELS FOR IMAGES
11 USING PYTORCH TO FIGHT CANCER
12 COMBINING DATA SOURCES INTO A UNIFIED DATASET
13 TRAINING A CLASSIFICATION MODEL TO DETECT SUSPECTED TUMORS
14 IMPROVING TRAINING WITH METRICS AND AUGMENTATION
15 USING SEGMENTATION TO FIND SUSPECTED NODULES
16 TRAINING MODELS ON MULTIPLE GPUS
17 DEPLOYING TO PRODUCTION
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
deep learning; PyTorch; neural networks; machine learning; transformers; generative AI; large language models; diffusion models; convolutional neural networks; recurrent neural networks; model optimization; deployment; PyTorch Tensor API; data loading
PART 1: CORE PYTORCH
1 INTRODUCING DEEP LEARNING AND THE PYTORCH LIBRARY
2 PRETRAINED NETWORKS
3 IT STARTS WITH A TENSOR
4 REAL-WORLD DATA REPRESENTATION USING TENSORS
5 THE MECHANICS OF LEARNING
6 USING A NEURAL NETWORK TO FIT THE DATA
7 TELLING BIRDS FROM AIRPLANES: LEARNING FROM IMAGES
8 USING CONVOLUTIONS TO GENERALIZE
PART 2: PRACTICAL APPLICATIONS
9 HOW TRANSFORMERS WORK
10 DIFFUSION MODELS FOR IMAGES
11 USING PYTORCH TO FIGHT CANCER
12 COMBINING DATA SOURCES INTO A UNIFIED DATASET
13 TRAINING A CLASSIFICATION MODEL TO DETECT SUSPECTED TUMORS
14 IMPROVING TRAINING WITH METRICS AND AUGMENTATION
15 USING SEGMENTATION TO FIND SUSPECTED NODULES
16 TRAINING MODELS ON MULTIPLE GPUS
17 DEPLOYING TO PRODUCTION
1 INTRODUCING DEEP LEARNING AND THE PYTORCH LIBRARY
2 PRETRAINED NETWORKS
3 IT STARTS WITH A TENSOR
4 REAL-WORLD DATA REPRESENTATION USING TENSORS
5 THE MECHANICS OF LEARNING
6 USING A NEURAL NETWORK TO FIT THE DATA
7 TELLING BIRDS FROM AIRPLANES: LEARNING FROM IMAGES
8 USING CONVOLUTIONS TO GENERALIZE
PART 2: PRACTICAL APPLICATIONS
9 HOW TRANSFORMERS WORK
10 DIFFUSION MODELS FOR IMAGES
11 USING PYTORCH TO FIGHT CANCER
12 COMBINING DATA SOURCES INTO A UNIFIED DATASET
13 TRAINING A CLASSIFICATION MODEL TO DETECT SUSPECTED TUMORS
14 IMPROVING TRAINING WITH METRICS AND AUGMENTATION
15 USING SEGMENTATION TO FIND SUSPECTED NODULES
16 TRAINING MODELS ON MULTIPLE GPUS
17 DEPLOYING TO PRODUCTION
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
