Coursera Deep Learning Course by deeplearning.ai projects

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

3. Week3 - Object detection - Papers for read: [You Only Look Once:

Unified, Real-Time Object Detection](https://arxiv.org/pdf/1506.02640.pdf), [YOLO](https://arxiv.org/pdf/1612.08242.pdf)

4. Week4 - Special applications: Face recognition & Neural style transfer - Papers for read: [DeepFace](https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf), [FaceNet](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schroff_FaceNet_A_Unified_2015_CVPR_paper.pdf)