Introduction to Deep Learning

Welcome to my repository where I document my learnings in several files.

📚 Chapters

1. Neural Networks and Deep Learning

In this foundational chapter, I delved into the basics of neural networks and deep learning, which include:

• Building, training, and applying fully connected deep neural networks.

2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

This course opened up the "black box" of deep learning, focusing on:

• Techniques like initialization, L2 and dropout regularization, batch normalization, and gradient checking.
• Optimization algorithms including mini-batch gradient descent, Momentum, RMSprop, and Adam.

3. Convolutional Neural Networks

Focusing on computer vision applications, this course covered:

• Building and understanding convolutional neural networks.
• Applying these networks to visual detection and recognition tasks.