This repository is a curated collection of links, lessons learned, and best practices for individuals studying machine learning and deep learning. It aims to provide a structured learning path, covering foundational concepts, various models, deep learning architectures, and practical advice for ML projects, targeting students and practitioners seeking to deepen their understanding of the field.

How It Works

The repository organizes learning resources into logical sections, starting with high-level introductions to ML and AI, then progressing to specific models (Linear Regression, KNN, Decision Trees), and finally delving into deep learning concepts like Neural Networks, CNNs, and RNNs. It emphasizes a structured learning approach: understanding the high-level goal, exploring model specifics, coding implementations, and applying them in practical examples.

Quick Start & Requirements

This repository is a collection of links and does not require installation or execution. It serves as a guide to external resources.

Highlighted Details

• Comprehensive coverage of ML/DL topics, including specific algorithms, influential papers, and practical project advice.
• Curated links to university courses (Stanford CS231N, CS224D), tech talks, blogs, and tools like Kaggle and Jupyter Notebooks.
• Detailed explanations of common pitfalls, debugging strategies, and best practices for model development and deployment.
• Discussion on crucial aspects like data preprocessing, hyperparameter tuning, regularization, and the interpretability of ML models.

Maintenance & Community

The repository is maintained by adeshpande3. No specific community channels or active development signals are mentioned in the README.

Licensing & Compatibility

The repository itself, being a collection of links, does not have a specific license. The linked resources may have their own licenses.

Limitations & Caveats

The content is a personal curation and may reflect the author's specific learning journey and biases. The effectiveness of the linked resources can vary, and some external links may become outdated.