This repository documents a personal 300-day journey into machine learning and deep learning, providing a structured learning path with code implementations and resource recommendations. It's aimed at individuals seeking to build a foundational understanding of ML/DL concepts, from core algorithms to advanced neural network architectures.

How It Works

The project follows a day-by-day log of learning activities, referencing specific books and online courses. Each entry details the topics covered, often including Python code snippets and explanations for implementing concepts like gradient descent, linear regression, neural networks, CNNs, RNNs, and transformers. The approach is to systematically work through established ML/DL literature, translating theoretical knowledge into practical code.

Quick Start & Requirements

• Install: Typically involves installing Python libraries like TensorFlow, PyTorch, scikit-learn, NumPy, and Fastai.
• Requirements: Python 3.x, standard ML/DL libraries. No specific hardware (e.g., GPU) is mandated by the repository structure, though performance for deep learning tasks would benefit from one.
• Resources: Links to specific books and courses are embedded within the daily logs.

Highlighted Details

• Comprehensive coverage of foundational ML algorithms (regression, classification, clustering).
• In-depth exploration of deep learning architectures (CNNs, RNNs, LSTMs, Transformers, GANs).
• Practical implementation examples using both TensorFlow and PyTorch.
• Integration of the Fastai library for streamlined deep learning workflows.
• Inclusion of computer vision (OpenCV) and natural language processing (NLP) topics.

Maintenance & Community

This appears to be a personal learning log, with contributions solely from the repository owner. There are no explicit community channels or roadmap details provided.

Licensing & Compatibility

The repository does not specify a license. Code snippets are generally compatible with standard Python environments.

Limitations & Caveats

The repository is a personal learning journal, not a production-ready library. Code examples may require adaptation for specific use cases, and the depth of coverage for each topic varies based on the author's learning progress.