C1 - Neural Networks and Deep Learning

https://www.coursera.org/learn/neural-networks-deep-learning

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.

By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications

SKILLS YOU WILL GAIN

• DeepLearning
• Artificial Neural Network
• Backpropagation
• Python Programming
• Neural Network Architecture

Week1 - Introduction to deep leatning

Analyze the major trends driving the rise of deep learning, and give examples of where and how it is applied today.

Learning Objectives

• Discuss the major trends driving the rise of deep learning.
• Explain how deep learning is applied to supervised learning
• List the major categories of models (CNNs, RNNs, etc.), and when they should be applied
• Assess appropriate use cases for deep learning

Week 2 - Neural networks Basics

Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models.

Learning Objectives

• Build a logistic regression model structured as a shallow neural network
• Build the general architecture of a learning algorithm, including parameter initialization, cost function and gradient calculation, and optimization implemetation (gradient descent) Implement computationally efficient and highly vectorized versions of models
• Compute derivatives for logistic regression, using a backpropagation mindset
• Use Numpy functions and Numpy matrix/vector operations
• Work with iPython Notebooks
• Implement vectorization across multiple training examples
• Explain the concept of broadcasting

Week 3 - Shallow Neural Networks

Build a neural network with one hidden layer, using forward propagation and backpropagation.

Learning Objectives

• 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

Week 4 - Deep L-layer Neural Network

Analyze the key computations underlying deep learning, then use them to build and train deep neural networks for computer vision tasks.

Learning Objectives

• 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