Neural Networks and Deep Learning Complete Course

Don't Forget To Subscribe, Like & Share Subscribe, Like & Share
If you want me to upload some courses please tell me in the comments. Don't Forget To Subscribe, Like & Share
If you want me to upload some courses please tell me in the comments.

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.

⭐ Important Notes ⭐
⌨️ The creator of this course is Deeplearning.ai (Andrew Ng)

Note: If you have any copyright issue with the content used in our channel or you find something that belongs to you, before you claim it to Youtube, SEND US A MESSAGE and the respective content will be DELETED right away. Thanks for understanding.

neural network explained,
neural network tutorial,
#neuralnetworksanddeeplearning,
neural network
#andrewng,
neural network deep learning
Coursera,
#neuralnetwork
#deeplearning
neural network full course
neural network tutorial
neural network tutorial for beginners
neural network in artificial intelligence
neural network architecture
neural network in machine learning
neural network explained
what is neural network
what is a neural network and how does it work
neural networks for machine learning
neural networks tutorial
neural networks and deep learning crash course ai
neural network

#deep learning
#neural networks
#neural network
#machine learning
#deep learning tutorial
#deep learning full course
#neural networks and deep learning crash course ai
#deep learning complete tutorial,
#deep learning course,
#neural networks for machine learning,
#neural networks and deep learning coursera,
#neural network explained,
#neural networks and deep learning pdf,
#deep learning and neural networks,
#what is deep learning,
#what is deep learning and neural networks