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Deep Learning Basics

A practical introduction to neural networks and deep learning - no PhD required!

What We'll Cover

This course takes you from zero to building your first neural network. We'll keep it practical and focus on understanding concepts through hands-on examples.

Course Outline

  1. What is Deep Learning?
  2. Neural networks explained simply
  3. When to use deep learning vs traditional ML

  4. Real-world applications

  5. Building Your First Neural Network

  6. Setting up your environment
  7. Creating a simple perceptron

  8. Training your first model

  9. Understanding Backpropagation

  10. How neural networks learn
  11. Gradient descent made simple

  12. Common pitfalls and solutions

  13. Improving Your Models

  14. Activation functions
  15. Loss functions

  16. Optimization techniques

  17. Going Deeper

  18. Convolutional Neural Networks (CNNs)
  19. Recurrent Neural Networks (RNNs)
  20. Transfer learning

Prerequisites

  • Basic Python knowledge
  • High school math (we'll explain the rest)
  • Curiosity and patience!

Tools We'll Use

  • Python 3.8+
  • TensorFlow/Keras
  • Jupyter Notebooks
  • Google Colab (free GPU access)

Coming soon! This course is currently being developed based on my experience building ML systems in production.