1.28 GB | 00:25:08 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
Appendix A Classification with structured data sets (27.43 MB)
Appendix A Introduction to machine learning (28.67 MB)
Appendix A Machine learning at first glance (19.21 MB)
Appendix A Machine learning with structured data sets (27.49 MB)
Appendix A Regression with structured data sets (16.82 MB)
Appendix A Training a supervised machine learning model (15.24 MB)
Chapter 1 Challenges when designing a machine learning platform (12.82 MB)
Chapter 1 Conclusions (3.54 MB)
Chapter 1 How does this book teach (3.49 MB)
Chapter 1 Introduction to serverless machine learning (16.38 MB)
Chapter 1 Public clouds for machine learning platforms (3.05 MB)
Chapter 1 Summary (2.26 MB)
Chapter 1 What is serverless machine learning (7.17 MB)
Chapter 1 When is this book not for you (12.75 MB)
Chapter 1 Who is this book for (6.99 MB)
Chapter 1 Why serverless machine learning (20.48 MB)
Chapter 10 Adopting PyTorch Lightning (41.06 MB)
Chapter 10 Summary (1.71 MB)
Chapter 11 Experimenting with the batch normalization hyperparameter (21.19 MB)
Chapter 11 Hyperparameter optimization (25.95 MB)
Chapter 11 Neural network layers configuration as a hyperparameter (6.16 MB)
Chapter 11 Summary (1.85 MB)
Chapter 12 Enabling PyTorch-distributed training support with Kaen (24.49 MB)
Chapter 12 Hyperparameter optimization with Optuna (36.01 MB)
Chapter 12 Machine learning pipeline (21.76 MB)
Chapter 12 Summary (1.06 MB)
Chapter 12 Unit testing model training in a local Kaen container (6.16 MB)
Chapter 2 Discovering the schema for the data set (27.51 MB)
Chapter 2 Getting started with the data set (34.11 MB)
Chapter 2 Migrating to columnar storage for more efficient analytics (30.15 MB)
Chapter 2 Starting with object storage for the data set (31.19 MB)
Chapter 2 Summary (4.5 MB)
Chapter 3 Applying VACUUM to the DC taxi data (53.92 MB)
Chapter 3 Exploring and preparing the data set (66.83 MB)
Chapter 3 Getting started with data quality (77.38 MB)
Chapter 3 Implementing VACUUM in a PySpark job (16.74 MB)
Chapter 3 Summary (2.74 MB)
Chapter 4 More exploratory data analysis and data preparation (60.05 MB)
Chapter 4 Summary (1.66 MB)
Chapter 5 Creating PyTorch tensors of pseudorandom and interval values (7.43 MB)
Chapter 5 Getting started with PyTorch tensor creation operations (4.31 MB)
Chapter 5 Introducing PyTorch Tensor basics (27.89 MB)
Chapter 5 PyTorch tensors vs native Python lists (16.61 MB)
Chapter 5 PyTorch tensor operations and broadcasting (13.09 MB)
Chapter 5 Summary (2.46 MB)
Chapter 6 Core PyTorch Autograd, optimizers, and utilities (41.96 MB)
Chapter 6 Dataset and DataLoader classes for gradient descent with batches (6.52 MB)
Chapter 6 Data set batches with PyTorch Dataset and DataLoader (18.2 MB)
Chapter 6 Getting started with data set batches for gradient descent (8.79 MB)
Chapter 6 Linear regression using PyTorch automatic differentiation (17.76 MB)
Chapter 6 Summary (3.11 MB)
Chapter 6 Transitioning to PyTorch optimizers for gradient descent (19.18 MB)
Chapter 7 Faster PyTorch tensor operations with GPUs (19.59 MB)
Chapter 7 Gradient descent with out-of-memory data sets (20.68 MB)
Chapter 7 Scaling up to use GPU cores (3.92 MB)
Chapter 7 Serverless machine learning at scale (15.08 MB)
Chapter 7 Summary (2.81 MB)
Chapter 7 Using IterableDataset and ObjectStorageDataset (24.96 MB)
Chapter 8 Introducing logical ring-based gradient descent (25.6 MB)
Chapter 8 Parameter server approach to gradient accumulation (9.93 MB)
Chapter 8 Phase 1 Reduce-scatter (10.11 MB)
Chapter 8 Phase 2 All-gather (9.89 MB)
Chapter 8 Scaling out with distributed training (39.23 MB)
Chapter 8 Summary (3.01 MB)
Chapter 8 Understanding ring-based distributed gradient descent (15.51 MB)
Chapter 9 Feature selection (61.94 MB)
Chapter 9 Feature selection case studies (5.81 MB)
Chapter 9 Feature selection using guiding principles (9.35 MB)
Chapter 9 Selecting features for the DC taxi data set (12.86 MB)
Chapter 9 Summary (3.02 MB)
Part 1 Mastering the data set (1.82 MB)
Part 2 PyTorch for serverless machine learning (5.34 MB)
Part 3 Serverless machine learning pipeline (4.63 MB)
Appendix A Introduction to machine learning (28.67 MB)
Appendix A Machine learning at first glance (19.21 MB)
Appendix A Machine learning with structured data sets (27.49 MB)
Appendix A Regression with structured data sets (16.82 MB)
Appendix A Training a supervised machine learning model (15.24 MB)
Chapter 1 Challenges when designing a machine learning platform (12.82 MB)
Chapter 1 Conclusions (3.54 MB)
Chapter 1 How does this book teach (3.49 MB)
Chapter 1 Introduction to serverless machine learning (16.38 MB)
Chapter 1 Public clouds for machine learning platforms (3.05 MB)
Chapter 1 Summary (2.26 MB)
Chapter 1 What is serverless machine learning (7.17 MB)
Chapter 1 When is this book not for you (12.75 MB)
Chapter 1 Who is this book for (6.99 MB)
Chapter 1 Why serverless machine learning (20.48 MB)
Chapter 10 Adopting PyTorch Lightning (41.06 MB)
Chapter 10 Summary (1.71 MB)
Chapter 11 Experimenting with the batch normalization hyperparameter (21.19 MB)
Chapter 11 Hyperparameter optimization (25.95 MB)
Chapter 11 Neural network layers configuration as a hyperparameter (6.16 MB)
Chapter 11 Summary (1.85 MB)
Chapter 12 Enabling PyTorch-distributed training support with Kaen (24.49 MB)
Chapter 12 Hyperparameter optimization with Optuna (36.01 MB)
Chapter 12 Machine learning pipeline (21.76 MB)
Chapter 12 Summary (1.06 MB)
Chapter 12 Unit testing model training in a local Kaen container (6.16 MB)
Chapter 2 Discovering the schema for the data set (27.51 MB)
Chapter 2 Getting started with the data set (34.11 MB)
Chapter 2 Migrating to columnar storage for more efficient analytics (30.15 MB)
Chapter 2 Starting with object storage for the data set (31.19 MB)
Chapter 2 Summary (4.5 MB)
Chapter 3 Applying VACUUM to the DC taxi data (53.92 MB)
Chapter 3 Exploring and preparing the data set (66.83 MB)
Chapter 3 Getting started with data quality (77.38 MB)
Chapter 3 Implementing VACUUM in a PySpark job (16.74 MB)
Chapter 3 Summary (2.74 MB)
Chapter 4 More exploratory data analysis and data preparation (60.05 MB)
Chapter 4 Summary (1.66 MB)
Chapter 5 Creating PyTorch tensors of pseudorandom and interval values (7.43 MB)
Chapter 5 Getting started with PyTorch tensor creation operations (4.31 MB)
Chapter 5 Introducing PyTorch Tensor basics (27.89 MB)
Chapter 5 PyTorch tensors vs native Python lists (16.61 MB)
Chapter 5 PyTorch tensor operations and broadcasting (13.09 MB)
Chapter 5 Summary (2.46 MB)
Chapter 6 Core PyTorch Autograd, optimizers, and utilities (41.96 MB)
Chapter 6 Dataset and DataLoader classes for gradient descent with batches (6.52 MB)
Chapter 6 Data set batches with PyTorch Dataset and DataLoader (18.2 MB)
Chapter 6 Getting started with data set batches for gradient descent (8.79 MB)
Chapter 6 Linear regression using PyTorch automatic differentiation (17.76 MB)
Chapter 6 Summary (3.11 MB)
Chapter 6 Transitioning to PyTorch optimizers for gradient descent (19.18 MB)
Chapter 7 Faster PyTorch tensor operations with GPUs (19.59 MB)
Chapter 7 Gradient descent with out-of-memory data sets (20.68 MB)
Chapter 7 Scaling up to use GPU cores (3.92 MB)
Chapter 7 Serverless machine learning at scale (15.08 MB)
Chapter 7 Summary (2.81 MB)
Chapter 7 Using IterableDataset and ObjectStorageDataset (24.96 MB)
Chapter 8 Introducing logical ring-based gradient descent (25.6 MB)
Chapter 8 Parameter server approach to gradient accumulation (9.93 MB)
Chapter 8 Phase 1 Reduce-scatter (10.11 MB)
Chapter 8 Phase 2 All-gather (9.89 MB)
Chapter 8 Scaling out with distributed training (39.23 MB)
Chapter 8 Summary (3.01 MB)
Chapter 8 Understanding ring-based distributed gradient descent (15.51 MB)
Chapter 9 Feature selection (61.94 MB)
Chapter 9 Feature selection case studies (5.81 MB)
Chapter 9 Feature selection using guiding principles (9.35 MB)
Chapter 9 Selecting features for the DC taxi data set (12.86 MB)
Chapter 9 Summary (3.02 MB)
Part 1 Mastering the data set (1.82 MB)
Part 2 PyTorch for serverless machine learning (5.34 MB)
Part 3 Serverless machine learning pipeline (4.63 MB)
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