# KodeKloud: Beta Courses PyTorch

## PyTorch

### Getting Started with PyTorch

- [Course Introduction](https://notes.kodekloud.com/docs/PyTorch/Getting-Started-with-PyTorch/Course-Introduction/page.md): This course teaches PyTorch for developing AI applications, focusing on breast cancer diagnosis and covering data handling, model training, and deployment techniques.
- [Course Structure](https://notes.kodekloud.com/docs/PyTorch/Getting-Started-with-PyTorch/Course-Structure/page.md): This guide outlines the course organization and setup for a comprehensive PyTorch learning journey.
- [Introduction to PyTorch](https://notes.kodekloud.com/docs/PyTorch/Getting-Started-with-PyTorch/Introduction-to-PyTorch/page.md): This article introduces PyTorch, its applications, advantages, and ecosystem in the context of artificial intelligence and machine learning.
- [Setting up PyTorch](https://notes.kodekloud.com/docs/PyTorch/Getting-Started-with-PyTorch/Setting-up-PyTorch/page.md): This guide offers a comprehensive introduction to installing and configuring PyTorch, covering installation options, best practices, and managing Python environments.
- [Demo Setting up PyTorch](https://notes.kodekloud.com/docs/PyTorch/Getting-Started-with-PyTorch/Demo-Setting-up-PyTorch/page.md): This guide covers setting up a PyTorch development environment on Ubuntu, including installation and verification steps.
- [Introduction to PyTorch Tensors](https://notes.kodekloud.com/docs/PyTorch/Getting-Started-with-PyTorch/Introduction-to-PyTorch-Tensors/page.md): This article introduces PyTorch tensors, covering their creation, attributes, operations, and GPU utilization for efficient data processing in deep learning.
- [Demo Introduction to PyTorch Tensors](https://notes.kodekloud.com/docs/PyTorch/Getting-Started-with-PyTorch/Demo-Introduction-to-PyTorch-Tensors/page.md): This lesson introduces PyTorch Tensors, covering creation, manipulation, and visualization techniques essential for deep learning and model training.
- [PyTorch Ecosystem](https://notes.kodekloud.com/docs/PyTorch/Getting-Started-with-PyTorch/PyTorch-Ecosystem/page.md): This lesson provides an overview of the PyTorch ecosystem, including tools, community engagement, and resources to enhance learning and development.

### Working with Data

- [Data Overview](https://notes.kodekloud.com/docs/PyTorch/Working-with-Data/Data-Overview/page.md): This article explores data preparation for AI and ML models, covering preprocessing, labeling, augmentation, and privacy management to ensure effective learning and fairness.
- [Datasets and Dataloaders](https://notes.kodekloud.com/docs/PyTorch/Working-with-Data/Datasets-and-Dataloaders/page.md): This article explores how PyTorch’s Datasets and Dataloaders facilitate efficient data handling and loading for machine learning projects.
- [Demo Datasets and Dataloaders](https://notes.kodekloud.com/docs/PyTorch/Working-with-Data/Demo-Datasets-and-Dataloaders/page.md): This comprehensive guide covers loading preexisting datasets and building custom datasets using PyTorch Datasets and Dataloaders.
- [Introduction to Transformations](https://notes.kodekloud.com/docs/PyTorch/Working-with-Data/Introduction-to-Transformations/page.md): This lesson explores using transformations to prepare and enhance image data for training models in PyTorch, improving robustness and reducing overfitting.
- [Demo Introduction to Transformations](https://notes.kodekloud.com/docs/PyTorch/Working-with-Data/Demo-Introduction-to-Transformations/page.md): This article teaches PyTorch image transformations for data preprocessing and augmentation to enhance model performance and efficiency.
- [Building Data](https://notes.kodekloud.com/docs/PyTorch/Working-with-Data/Building-Data/page.md): This article explains how to build custom data for training a model using PyTorch, covering data splitting, cleaning, versioning, and creating data transforms and loaders.
- [Demo Building Data](https://notes.kodekloud.com/docs/PyTorch/Working-with-Data/Demo-Building-Data/page.md): This article guides building and preprocessing a custom dataset for training machine learning models using PyTorch, covering data cleaning, annotation, and transformations.

### Building and Training Models

- [Introduction to Neural Networks](https://notes.kodekloud.com/docs/PyTorch/Building-and-Training-Models/Introduction-to-Neural-Networks/page.md): Understanding neural networks is essential for model training with PyTorch, focusing on their structure, function, and applications in artificial intelligence.
- [Building and Training a model](https://notes.kodekloud.com/docs/PyTorch/Building-and-Training-Models/Building-and-Training-a-model/page.md): This article guides on building and training a neural network model using PyTorch, covering model definition, parameters, loss functions, optimizers, and effective training techniques.
- [Demo Building and Training a model](https://notes.kodekloud.com/docs/PyTorch/Building-and-Training-Models/Demo-Building-and-Training-a-model/page.md): This guide explores building and training a machine learning model using a simple neural network with PyTorch.
- [Saving and loading models](https://notes.kodekloud.com/docs/PyTorch/Building-and-Training-Models/Saving-and-loading-models/page.md): This article covers saving and loading models in PyTorch, including core functions, techniques for model management, and handling device differences.
- [Demo Saving and loading models](https://notes.kodekloud.com/docs/PyTorch/Building-and-Training-Models/Demo-Saving-and-loading-models/page.md): This article explores techniques for saving and loading models in PyTorch, covering methods like state dictionaries, full model storage, checkpoints, and device-specific loading.
- [Additional Training Methods](https://notes.kodekloud.com/docs/PyTorch/Building-and-Training-Models/Additional-Training-Methods/page.md): This article explores advanced techniques to enhance and optimize model training in PyTorch, including transfer learning, PyTorch Hub, and learning rate schedulers.
- [Demo Additional Training Methods](https://notes.kodekloud.com/docs/PyTorch/Building-and-Training-Models/Demo-Additional-Training-Methods/page.md): This article explores advanced training techniques in PyTorch, including transfer learning, learning rate schedulers, and model sharing with step-by-step examples.
- [Model Evaluation](https://notes.kodekloud.com/docs/PyTorch/Building-and-Training-Models/Model-Evaluation/page.md): This article discusses the process and importance of model evaluation in machine learning, including metrics, overfitting, and practical implementation techniques.
- [Demo Model Evaluation](https://notes.kodekloud.com/docs/PyTorch/Building-and-Training-Models/Demo-Model-Evaluation/page.md): This guide evaluates trained models for deployment, covering data preprocessing, inference techniques, and performance metrics computation.

### Model Deployment and Inference

- [Deployment Options](https://notes.kodekloud.com/docs/PyTorch/Model-Deployment-and-Inference/Deployment-Options/page.md): Overview of model deployment options for PyTorch, including formats, serving frameworks, and infrastructure components.
- [Demo Deployment Options](https://notes.kodekloud.com/docs/PyTorch/Model-Deployment-and-Inference/Demo-Deployment-Options/page.md): This demonstration covers the process of deploying ONNX models, including installation, model modification, export, inference, and output mapping.
- [Introduction to Flask](https://notes.kodekloud.com/docs/PyTorch/Model-Deployment-and-Inference/Introduction-to-Flask/page.md): This guide explores deploying PyTorch models using Flask, covering setup, integration, and best practices for creating an inference API.
- [Demo Introduction to Flask](https://notes.kodekloud.com/docs/PyTorch/Model-Deployment-and-Inference/Demo-Introduction-to-Flask/page.md): This lesson explores Flask basics and demonstrates serving a machine learning model using Flask for model deployment.
- [Introduction to Docker](https://notes.kodekloud.com/docs/PyTorch/Model-Deployment-and-Inference/Introduction-to-Docker/page.md): This article explores leveraging Docker for model deployment, covering its fundamentals, architecture, workflow, and best practices for efficient application management.
- [Demo Introduction to Docker](https://notes.kodekloud.com/docs/PyTorch/Model-Deployment-and-Inference/Demo-Introduction-to-Docker/page.md): This lesson guides you in using Docker to deploy a Flask application, covering installation, image building, container running, and pushing to Docker Hub.
- [Deploying to Kubernetes](https://notes.kodekloud.com/docs/PyTorch/Model-Deployment-and-Inference/Deploying-to-Kubernetes/page.md): This guide explores the advantages and best practices of deploying machine learning models using Kubernetes.
- [Demo Deploying to Kubernetes](https://notes.kodekloud.com/docs/PyTorch/Model-Deployment-and-Inference/Demo-Deploying-to-Kubernetes/page.md): This article explores advanced approaches for deploying machine learning models on Kubernetes, including deployments, Horizontal Pod Autoscalers, and node management techniques.