This link of course : Neural Networks and Deep Learning
1. Week1: Analyze the major trends driving the rise of deep learning, and give examples of where and how it is applied today.
- We learn about basic of Neural Network
2. Week 2: Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models.
- We learn about Logistic Regression as a Neural Networks
- How to calculate : Cost function, Gradient Descent, Derivatives and so on
3. Week 3: Build a neural network with one hidden layer, using forward propagation and backpropagation.
- Describe hidden units and hidden layers
- Use units with a non-linear activation function, such as tanh
- Implement forward and backward propagation
- Apply random initialization to your neural network
- Increase fluency in Deep Learning notations and Neural Network Representations
- Implement a 2-class classification neural network with a single hidden layer
- Compute the cross entropy loss
4. Week 4: Analyze the key computations underlying deep learning, then use them to build and train deep neural networks for computer vision tasks.
- Describe the successive block structure of a deep neural network
- Build a deep L-layer neural network
- Analyze matrix and vector dimensions to check neural network implementations
- Use a cache to pass information from forward to back propagation
- Explain the role of hyperparameters in deep learning
- Build a 2-layer neural network
Course 2 - C2 - Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
This link of course : Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Week 1: Discover and experiment with a variety of different initialization methods, apply L2 regularization and dropout to avoid model overfitting, then apply gradient checking to identify errors in a fraud detection model.
- Give examples of how different types of initializations can lead to different results
- Examine the importance of initialization in complex neural networks
- Explain the difference between train/dev/test sets
- Diagnose the bias and variance issues in your model
- Assess the right time and place for using regularization methods such as dropout or L2 regularization
- Explain Vanishing and Exploding gradients and how to deal with them
- Use gradient checking to verify the accuracy of your backpropagation implementation
- Apply zeros initialization, random initialization, and He initialization
- Apply regularization to a deep learning model