AI Learning

Welcome to the AI Learning repository! This hub is dedicated to anyone passionate about Artificial Intelligence, from beginners to experienced practitioners. Here, you'll find a diverse collection of resources tailored to enhance your understanding and skills in AI.

What's Inside?

• Detailed notes covering various AI topics.
• Tools and scripts to facilitate your AI projects.
• Curated links to important resources, videos, tutorials, and communities.
• Educational materials including articles, papers, and more.

Getting Started

To get started, explore the repository's sections, each designed to provide specific types of content:

• For foundational concepts, check out the Foundations.
• Dive into the Utilities for practical tools and scripts.
• Explore Additional Resources for additional learning resources and community connections.
• Browse the folders to deepen your theoretical and practical understanding.

Contribute

Your contributions are welcome! If you have resources, tools, or knowledge you'd like to share, please feel free to contribute. Let's learn and grow together in the fascinating world of AI!

My personal learning calendar, created by GPT-4

Semester 1: Foundations of Machine Learning and AI

Objective: Establish a strong foundation in machine learning, statistical methods, and basic AI concepts.

Courses:

• Introduction to Machine Learning (Coursera, edX)
• Statistical Learning (Stanford Online)
• Linear Algebra and Calculus (Khan Academy or MIT OpenCourseWare)

Projects:

• Implement basic ML algorithms from scratch (linear regression, logistic regression, decision trees).

Reading:

• “Pattern Recognition and Machine Learning” by Christopher Bishop

Semester 2: Deep Learning and Neural Networks

Objective: Dive deep into neural networks, focusing on their architecture, training, and applications.

Courses:

• Deep Learning Specialization (Coursera by Andrew Ng)
• Neural Networks and Deep Learning (MIT OpenCourseWare)

Projects:

• Build and train a convolutional neural network (CNN) on an image classification task.
• Experiment with recurrent neural networks (RNNs) for sequence modeling.

Reading: “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Semester 3: Advanced Topics in AI: Transformers and LLMs

Objective: Specialize in transformers and large language models, understanding their architecture and applications.

Courses:

• The Hugging Face Course (Hugging Face)
• Natural Language Processing with Transformers (O’Reilly)

Projects:

• Implement a transformer model from scratch.
• Fine-tune a pre-trained transformer model for a custom NLP task.

Reading:

• Relevant papers from arXiv or the ACL Anthology, such as the original transformer paper.

Semester 4: Applications and Projects in AI

Objective: Apply learned skills to real-world applications, particularly in media transcoding using AI.

Courses:

• Advanced Machine Learning with TensorFlow on Google Cloud (Coursera)
• Multimedia Processing with AI (Udemy or Coursera)

Projects:

• Develop a project to transcode media formats using AI.
• Use AI for enhancing video and audio quality.

Reading: Stay updated with the latest research and developments via journals and conferences like NeurIPS or ICML.