01_machine_learning_and_deep_learning

Machine Learning and Deep Learning Fundamentals

Introduction

Machine Learning (ML) and Deep Learning (DL) form the foundation of modern AI engineering. This guide covers essential concepts, algorithms, and resources to build a solid understanding of these fundamental areas.

Topics Covered

1. Machine Learning Basics

   • Core concepts and algorithms
   • Training and evaluation
   • Model selection and optimization

2. Deep Learning Fundamentals

   • Neural networks architecture
   • Backpropagation
   • Activation functions
   • Loss functions

3. Advanced Deep Learning Concepts

   • Convolutional Neural Networks (CNNs)
   • Recurrent Neural Networks (RNNs)
   • Transformers
   • Generative Models

Learning Resources

YouTube Playlists

• Stanford YouTube playlist — Machine learning
• MIT YouTube playlist — Introduction to Deep Learning
• DeepLearning.AI YouTube playlist — Deep Learning Specialization

Blogs

• Deep Learning, by Ian Goodfellow, Yoshua Bengio and Aaron Courville
• An Introduction to PyTorch — A Simple yet Powerful Deep Learning Library

Research Papers

• Information Theory of Deep Learning

Online Courses

• Deep Learning Summer School Talks
• CloudyML AI for all course