Please do homework on your own and look in this repository course materials only when you've already done the assignments. Anyway, it is in your interest if you want to learn something.
- Neural Networks and Deep Learning
- Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models
- Building_your_Deep_Neural_Network_Step_by_Step
- Deep Neural Network for Image Classification: Application
- Gradient Checking
- Various regularizations
- Optimization methods
- Convolutional Neural Networks: Step by Step
- Residual Networks
- Autonomous driving - Car detection
- Face Recognition
- Deep Learning & Art: Neural Style Transfer
- Building your Recurrent Neural Network - Step by Step
- Character level language model
- Improvise a Jazz Solo with an LSTM Network
- Emojifier
- Neural Machine Translation
- Trigger Word Detection
The foundations of deep learning
- Week 1 - Introduction to deep learning
- Week 2 - Neural Networks Basics
- Week 3 - Shallow neural networks
- Week 4 - Deep Neural Networks
This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results.
- Week 1 - Practical aspects of Deep Learning
- Week 2 - Optimization algorithms
- Week 3 - Hyperparameter tuning, Batch Normalization and Programming Frameworks
You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how.
- Week 1 - Introduction to ML Strategy
- Week 2 - ML Strategy (2)
This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.
- Week 1 - Foundations of Convolutional Neural Networks
- Week 2 - Deep convolutional models: case studies
- Week 3 - Object detection
- Week4 - Special applications: Face recognition & Neural style transfer
This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.