PyTorch Recipes : A Problem-Solution Approach /: A Problem-Solution Approach. ([2019])

• Record Type: Book
• Further Information: Note: Pradeepta Mishra.
• Authors: Mishra, Pradeepta
• Contents: Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Introduction to PyTorch, Tensors, and Tensor Operations; What Is PyTorch?; PyTorch Installation; Recipe 1-1. Using Tensors; Problem; Solution; How It Works; Conclusion; Chapter 2: Probability Distributions Using PyTorch; Recipe 2-1. Sampling Tensors; Problem; Solution; How It Works; Recipe 2-2. Variable Tensors; Problem; Solution; How It Works; Recipe 2-3. Basic Statistics; Problem; Solution; How It Works; Recipe 2-4. Gradient Computation; Problem; Solution; How It Works Recipe 2-5. Tensor OperationsProblem; Solution; How It Works; Recipe 2-6. Tensor Operations; Problem; Solution; How It Works; Recipe 2-7. Distributions; Problem; Solution; How It Works; Conclusion; Chapter 3: CNN and RNN Using PyTorch; Recipe 3-1. Setting Up a Loss Function; Problem; Solution; How It Works; Recipe 3-2. Estimating the Derivative of the Loss Function; Problem; Solution; How It Works; Recipe 3-3. Fine-Tuning a Model; Problem; Solution; How It Works; Recipe 3-4. Selecting an Optimization Function; Problem; Solution; How It Works; Recipe 3-5. Further ­Optimizing the Function ProblemSolution; How It Works; Recipe 3-6. Implementing a Convolutional Neural Network (CNN); Problem; Solution; How It Works; Recipe 3-7. Reloading a Model; Problem; Solution; How It Works; Recipe 3-8. Implementing a Recurrent Neural Network (RNN); Problem; Solution; How It Works; Recipe 3-9. Implementing a RNN for Regression Problems; Problem; Solution; How It Works; Recipe 3-10. Using PyTorch Built-in Functions; Problem; Solution; How It Works; Recipe 3-11. Working with Autoencoders; Problem; Solution; How It Works; Recipe 3-12. Fine-Tuning Results Using Autoencoder; Problem; Solution How It WorksRecipe 3-13. Visualizing the Encoded Data in a 3D Plot; Problem; Solution; How It Works; Recipe 3-14. Restricting Model Overfitting; Problem; Solution; How It Works; Recipe 3-15. Visualizing the Model Overfit; Problem; Solution; How It Works; Recipe 3-16. Initializing Weights in the Dropout Rate; Problem; Solution; How It Works; Recipe 3-17. Adding Math Operations; Problem; Solution; How It Works; Recipe 3-18. Embedding Layers in RNN; Problem; Solution; How It Works; Conclusion; Chapter 4: Introduction to Neural Networks Using PyTorch; Recipe 4-1. Working with Activation Functions ProblemSolution; How It Works; Linear Function; Bilinear Function; Sigmoid Function; Hyperbolic Tangent Function; Log Sigmoid Transfer Function; ReLU Function; Leaky ReLU; Recipe 4-2. Visualizing the Shape of Activation Functions; Problem; Solution; How It Works; Recipe 4-3. Basic Neural Network Model; Problem; Solution; How It Works; Recipe 4-4. Tensor Differentiation; Problem; Solution; How It Works; Conclusion; Chapter 5: Supervised Learning Using PyTorch; Introduction to Linear Regression; Recipe 5-1. Data Preparation for the Supervised Model; Problem; Solution; How It Works
• Publisher Details: California : Apress
• Publication Date: 2019
• Copyright Date: 2019
• Extent: 1 online resource
• Subjects: 006.3/2; Neural networks (Computer science); Machine learning; Python (Computer program language); COMPUTERS / General; Electronic books; Electronic books
• Languages: English
• ISBNs: 9781484242582; 1484242580
• Related ISBNs: 9781484242575
• Notes: Note: Online resource; title from PDF title page (EBSCO, viewed January 31, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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