What do you think?
Rate this book
Introduction to PyTorch
Unlock the power of PyTorch and dive deep into the world of deep learning with "Introduction to PyTorch." This beginner-friendly book is designed to help you grasp the essentials of PyTorch and build a solid foundation in deep learning, even if you're new to the field.
Key Concepts and Skills
Tensor Begin with the basics as you explore what tensors are and how to perform fundamental operations on them.
Common PyTorch Learn essential torch functions like empty, zeros, ones, and randn to manipulate tensors with ease.
Automatic Differentiation with Dive into PyTorch's automatic differentiation mechanism provided by Autograd, making gradient computations a breeze.
Linear Gain a clear understanding of linear layers in neural networks, setting the stage for more advanced concepts.
A Simple Neural Put your knowledge to the test by building a simple linear regression model using PyTorch tensors and Autograd.
Training and Discover the art of training a model. Define loss functions, explore optimization techniques, and grasp the essentials of backpropagation.
Batch Data Learn the importance of processing data in batches for efficient training.
Activation Unlock the secrets behind activation functions and how they impact neural network performance.
Saving and Loading Understand the basics of saving and loading PyTorch models, a crucial skill for model preservation.
Transfer Get introduced to transfer learning and harness pre-trained models for quicker progress.
Fine-Tuning Pre-trained Learn how to adapt pre-trained models to new tasks, making your projects more efficient.
Data Handling with Master the fundamentals of working with different datasets and applying transformations using torchvision.transforms.
Model Discover how to evaluate your model's performance by making predictions on test data.
Simplifying Networks with Simplify complex neural networks using PyTorch's nn.Sequential for a more beginner-friendly approach.
Visualizing Neural Visualize the inner workings of neural networks and explore PyTorch model visualization and TensorBoard.
Key Concepts and Skills
Tensor Begin with the basics as you explore what tensors are and how to perform fundamental operations on them.
Common PyTorch Learn essential torch functions like empty, zeros, ones, and randn to manipulate tensors with ease.
Automatic Differentiation with Dive into PyTorch's automatic differentiation mechanism provided by Autograd, making gradient computations a breeze.
Linear Gain a clear understanding of linear layers in neural networks, setting the stage for more advanced concepts.
A Simple Neural Put your knowledge to the test by building a simple linear regression model using PyTorch tensors and Autograd.
Training and Discover the art of training a model. Define loss functions, explore optimization techniques, and grasp the essentials of backpropagation.
Batch Data Learn the importance of processing data in batches for efficient training.
Activation Unlock the secrets behind activation functions and how they impact neural network performance.
Saving and Loading Understand the basics of saving and loading PyTorch models, a crucial skill for model preservation.
Transfer Get introduced to transfer learning and harness pre-trained models for quicker progress.
Fine-Tuning Pre-trained Learn how to adapt pre-trained models to new tasks, making your projects more efficient.
Data Handling with Master the fundamentals of working with different datasets and applying transformations using torchvision.transforms.
Model Discover how to evaluate your model's performance by making predictions on test data.
Simplifying Networks with Simplify complex neural networks using PyTorch's nn.Sequential for a more beginner-friendly approach.
Visualizing Neural Visualize the inner workings of neural networks and explore PyTorch model visualization and TensorBoard.
36 pages, Kindle Edition
Published December 31, 2023
1 person is currently reading
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
No one has reviewed this book yet.