Neural Networks and Deep Learning (Complete Course)

INTRODUCTION TO DEEP LEARNING
0:00:00 Welcome
0:05:31 What is a Neural Network
0:12:48 Supervised Learning with Neural Network
0:21:17 Why is Deep Learing taking off
0:31:38 About this course
0:34:06 Geoffrey Hinton Interview

NEURAL NETWORKS BASICS
1:14:28 Binary Classification
1:22:52 Logistic Regression
1:28:51 Logistic Regression Cost Function
1:37:03 Gradient Descent
1:48:26 Derivatives
1:55:37 More Derivative Example
2:06:04 Computation Graph
2:09:38 Derivatives With a Computation Graph
2:24:12 Logistic Regression Gradient Descent
2:30:54 Gradient Descent on m Examples
2:38:55 Vectorization
2:46:59 More Vectorization Examples
2:53:18 Vectorization Logistic Regression
3:00:50 Vectorization Logistic Regression Gradient Output
3:10:28 Broadcasting in PYthon
3:21:34 A Note on Python Numpy Vectors
3:28:23 Quick tour of jupyter iPython Notebooks
3:32:06 Explanation of Logistics Regression Cost Function (optional)
3:39:21 Pieter Abbeel Interview

SHALLOW NEURAL NETWORKS
3:55:25 Neural Network Overview
3:59:51 Neural Network Representation
4:05:05 computing a Neural Network's output
4:15:03 Vectorizing Across Multiple Examples
4:24:09 Explantion for Vectorized Implementation
4:31:46 Activation Functions
4:42:43 Why do you need Non-Linear Activation Functions
4:48:19 Derivatives of Activation Functions
4:56:16 Gradient Descent for Neural Networks
5:06:14 Backpropagation Intuition (Optional)
5:22:02 Random Initialization
5:30:00 Ian Goodfellow Interview

DEEP NEURAL NETWORKS
5:44:56 Deep L-layer Neural Network
5:50:47 Forward Propagation in a Deep Network
5:58:02 Getting your Matrix Dimentions Right
6:09:12 Why Deep Representations
6:19:46 Building Blocks of Deep Neural Networks
6:28:19 Forward and Backward propagation
6:38:49 Parameters vs Hyperparameters
6:46:06 What does this have to do with brain

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.
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