Are you fascinated by the incredible world of deep learning and machine learning? Look no further! This guide will show you how to get started with an exhaustive list of resources that can boost your proficiency in PyTorch.

Table Of Contents

• Tutorials
• Large Language Models (LLMs)
• Tabular Data
• Visualization
• Explainability
• Object Detection
• Long-Tailed Out-of-Distribution Recognition
• Activation Functions
• Energy-Based Learning
• Missing Data
• Architecture Search
• Continual Learning
• Optimization
• Quantization
• Quantum Machine Learning
• Neural Network Compression
• Facial, Action, and Pose Recognition
• Super Resolution
• Synthesizing Views
• Voice
• Medical
• 3D Segmentation, Classification, and Regression
• Video Recognition
• Recurrent Neural Networks (RNNs)
• Convolutional Neural Networks (CNNs)
• Segmentation
• Geometric Deep Learning: Graph Irregular Structures
• Sorting
• Ordinary Differential Equations Networks
• Multi-task Learning
• GANs, VAEs, and AEs
• Unsupervised Learning
• Adversarial Attacks
• Style Transfer
• Image Captioning
• Transformers
• Similarity Networks and Functions
• Reasoning
• General NLP
• Question and Answering
• Speech Generation and Recognition
• Document and Text Classification
• Text Generation
• Text to Image
• Translation
• Sentiment Analysis
• Deep Reinforcement Learning
• Deep Bayesian Learning and Probabilistic Programming
• Spiking Neural Networks
• Anomaly Detection
• Regression Types
• Time Series
• Synthetic Datasets
• Neural Network General Improvements
• DNN Applications in Chemistry and Physics
• New Thinking on General Neural Network Architecture
• Linear Algebra
• API Abstraction
• Low Level Utilities
• PyTorch Utilities
• PyTorch Video Tutorials
• Community
• To be Classified
• Links to This Repository
• Contributions

Tutorials

To kick off your journey, check out the plethora of tutorials available:

• Official PyTorch Tutorials
• Official PyTorch Examples
• Dive Into Deep Learning with PyTorch
• Minicourse in Deep Learning with PyTorch (Multi-language)

Large Language Models (LLMs)

Building LLMs can be intricate, but the following resources simplify the process:

• Build a Large Language Model (From Scratch)
• Hugginface LLM Training Book

Understanding PyTorch Code: An Analogy

To truly understand PyTorch, think of it as building a house. Each part of the house, like walls, windows, and the roof, corresponds to different components in PyTorch. Just as you build a house brick by brick, in PyTorch, you build models layer by layer. Each layer is like a brick, which you stack to create a solid structure (your model) that can withstand the tests of data. The methods inside PyTorch, such as layers, optimizers, and loss functions, serve as the tools you’ll use to build and modify your house to ensure it meets your design specifications (requirements of your task).

Troubleshooting Ideas

As you explore these resources, you may encounter a few hurdles. Here are some common troubleshooting ideas:

• If you experience issues with your installations, ensure your version of Python is compatible with the version of PyTorch you are using.
• For any programming errors, thoroughly check the documentation of the specific library you are using, as example codes are often provided.
• Join the PyTorch community forums or visit fxis.ai for more insights and problem-solving support.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.