This is my personal projects for the course. The course covers deep learning from begginer level to advanced. Highly recommend anyone wanting to break into AI.

Instructor: Andrew Ng, DeepLearning.ai

1. Week1 - Introduction to deep learning
2. Week2 - Neural Networks Basics
3. Week3 - Shallow neural networks
4. Week4 - Deep Neural Networks

1. Week1 - Practical aspects of Deep Learning - Setting up your Machine Learning Application - Regularizing your neural network - Setting up your optimization problem
2. Week2 - Optimization algorithms
3. Week3 - Hyperparameter tuning, Batch Normalization and Programming Frameworks

1. Week1 - Introduction to ML Strategy - Setting up your goal - Comparing to human-level performance
2. Week2 - ML Strategy (2) - Error Analysis - Mismatched training and dev/test set - Learning from multiple tasks - End-to-end deep learning

1. Week1 - Foundations of Convolutional Neural Networks
2. Week2 - Deep convolutional models: case studies - Papers for read: ImageNet Classification with Deep Convolutional Neural Networks, Very Deep Convolutional Networks For Large-Scale Image Recognition
3. Week3 - Object detection - Papers for read: You Only Look Once: Unified, Real-Time Object Detection, YOLO
4. Week4 - Special applications: Face recognition & Neural style transfer - Papers for read: DeepFace, FaceNet

1. Week1 - Recurrent Neural Networks
2. Week2 - Natural Language Processing & Word Embeddings
3. Week3 - Sequence models & Attention mechanism

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