Deep Learning - Aired Order - All Seasons

Season 1

S01E01 What is Deep Learning?
- September 23, 2017
- YouTube
S01E02 What is a Neural Network?
- September 23, 2017
- YouTube
S01E03 Supervised Learning with Neural Networks
- September 23, 2017
- YouTube
S01E04 Drivers Behind the Rise of Deep Learning
- September 23, 2017
- YouTube
S01E05 Binary Classification in Deep Learning
- September 23, 2017
- YouTube
S01E06 Logistic Regression
- September 23, 2017
- YouTube
S01E07 Logistic Regression Cost Function
- September 23, 2017
- YouTube
S01E08 Gradient Descent
- September 23, 2017
- YouTube
S01E09 Derivatives
- September 23, 2017
- YouTube
S01E10 Derivatives Examples
- September 23, 2017
- YouTube
S01E11 Computation Graph
- September 23, 2017
- YouTube
S01E12 Derivatives with a Computation Graph
- September 23, 2017
- YouTube
S01E13 Logistic Regression Derivatives
- September 23, 2017
- YouTube
S01E14 Gradient Descent on m Training Examples
- September 23, 2017
- YouTube
S01E15 Vectorization
- September 23, 2017
- YouTube
S01E16 More Vectorization Examples
- September 23, 2017
- YouTube
S01E17 Vectorizing Logistic Regressio
- September 23, 2017
- YouTube
S01E18 Vectorizing Logistic Regression's Gradient Computation
- September 23, 2017
- YouTube
S01E19 Broadcasting in Python
- September 23, 2017
- YouTube
S01E20 Python-Numpy
- September 23, 2017
- YouTube
S01E21 Jupyter-iPython
- September 23, 2017
- YouTube
S01E22 Logistic Regression Cost Function Explanation
- September 23, 2017
- YouTube
S01E23 Neural Network Overview
- September 23, 2017
- YouTube
S01E24 Neural Network Representation
- September 23, 2017
- YouTube
S01E25 Computing a Neural Network's Output
- September 23, 2017
- YouTube
S01E26 Vectorizing Across Multiple Training Examples
- September 23, 2017
- YouTube
S01E27 Vectorized Implementation Explanation
- September 23, 2017
- YouTube
S01E28 Activation Functions
- September 23, 2017
- YouTube
S01E29 Why Non-Linear Activation Function?
- September 23, 2017
- YouTube
S01E30 Derivatives of Activation Functions
- September 23, 2017
- YouTube
S01E31 Gradient Descent for Neural Networks
- September 23, 2017
- YouTube
S01E32 BackPropagation Intuition
- September 23, 2017
- YouTube
S01E33 Random Initialization of Weights
- September 23, 2017
- YouTube
S01E34 Deep L-layer Neural Network
- September 23, 2017
- YouTube
S01E35 Forward Propagation in Deep Networks
- September 23, 2017
- YouTube
S01E36 Getting your Matrix Dimension Right
- September 23, 2017
- YouTube
S01E37 Why DEEP representation?
- September 23, 2017
- YouTube
S01E38 Building Blocks of Deep Neural Network
- September 23, 2017
- YouTube
S01E39 Forward Propagation for Layer L
- September 23, 2017
- YouTube
S01E40 Parameters vs Hyperparameters
- September 23, 2017
- YouTube
S01E41 Brain and Deep Learning
- September 23, 2017
- YouTube
S01E42 Train/Dev/Test sets
- September 23, 2017
- YouTube
S01E43 Bias/Variance
- September 23, 2017
- YouTube
S01E44 Basic "Recipe" of Machine Learning
- September 23, 2017
- YouTube
S01E45 Regularization
- September 23, 2017
- YouTube
S01E46 Why Regularization reduces Overfitting?
- September 23, 2017
- YouTube
S01E47 Dropout Regularization
- September 23, 2017
- YouTube
S01E48 Why does drop-out work?
- September 23, 2017
- YouTube
S01E49 Other Regularization Methods
- September 23, 2017
- YouTube
S01E50 Normalizing Input
- September 23, 2017
- YouTube
S01E51 Vanishing/Exploding Gradients
- September 23, 2017
- YouTube
S01E52 Weight Initialization for deep networks
- September 23, 2017
- YouTube
S01E53 Numerical Approximation of Gradients
- September 23, 2017
- YouTube
S01E54 Gradient Checking
- September 23, 2017
- YouTube
S01E55 Gradient Checking Implantation Notes
- September 23, 2017
- YouTube
S01E56 Mini Batch Gradient Descent
- September 23, 2017
- YouTube
S01E57 Understanding Mini-Batch Gradient Descent
- YouTube
S01E58 Exponentially Weighted Averages
- September 23, 2017
- YouTube
S01E59 Understanding Exponentially Weighted Averages
- September 23, 2017
- YouTube
S01E60 Bias Correction in Exponentially Weighted Average
- September 23, 2017
- YouTube
S01E61 Gradient Descent with Momentum
- September 23, 2017
- YouTube
S01E62 RMSprop
- September 23, 2017
- YouTube
S01E63 Adam Optimization Algorithm
- September 23, 2017
- YouTube
S01E64 Learning Rate Decay
- September 23, 2017
- YouTube
S01E65 The Problem of Local Optima
- September 23, 2017
- YouTube
S01E66 Tunning Process
- September 23, 2017
- YouTube
S01E67 Right Scale for Hyperparameters
- September 23, 2017
- YouTube
S01E68 Hyperparameters tuning in Practice: Panda vs. Caviar
- September 23, 2017
- YouTube
S01E69 Batch Norm
- September 23, 2017
- YouTube
S01E70 Fitting Batch Norm into a Neural Network
- September 23, 2017
- YouTube
S01E71 Why Does Batch Norm Work?
- September 23, 2017
- YouTube
S01E72 Batch Norm at Test Time
- September 23, 2017
- YouTube
S01E73 Softmax Regression
- September 23, 2017
- YouTube
S01E74 Training a Softmax Classifier
- September 23, 2017
- YouTube
S01E75 Deep Learning Frameworks
- September 23, 2017
- YouTube
S01E76 TensorFlow
- September 23, 2017
- YouTube
S01E77 Why ML Strategy?
- September 23, 2017
- YouTube
S01E78 Orthogonalization
- September 23, 2017
- YouTube
S01E79 Single Number Evaluation Metric
- September 23, 2017
- YouTube
S01E80 Satisfying and Optimizing Metrics
- September 23, 2017
- YouTube
S01E81 train/dev/test distributions
- September 23, 2017
- YouTube
S01E82 Size of dev and test sets
- September 23, 2017
- YouTube
S01E83 When to change dev/test sets and metrics?
- September 23, 2017
- YouTube
S01E84 Why human-level performance?
- September 23, 2017
- YouTube
S01E85 Avoidable Bias
- September 23, 2017
- YouTube
S01E86 Understanding Human-Level Performance
- September 23, 2017
- YouTube
S01E87 Surpassing Human-Level Performance
- September 23, 2017
- YouTube
S01E88 Improving Your Model Performance
- September 23, 2017
- YouTube
S01E89 Carrying Out Error Analysis
- September 23, 2017
- YouTube
S01E90 Cleaning Up Incorrect Labeled Data
- September 23, 2017
- YouTube
S01E91 Build Your First System Quickly, Then Iterate
- September 23, 2017
- YouTube
S01E92 Training and Testing on Different Distributions
- September 23, 2017
- YouTube
S01E93 Bias and Variance with Mismatched data distributions
- September 23, 2017
- YouTube
S01E94 Addressing Data Mismatch
- September 23, 2017
- YouTube
S01E95 Transfer Learning
- September 23, 2017
- YouTube
S01E96 Multi-Task Learning
- September 23, 2017
- YouTube
S01E97 End-to-End Deep Learning
- September 23, 2017
- YouTube
S01E98 Whether to use End-to-End Learning
- September 23, 2017
- YouTube

All Seasons

Season 1

S01E01 What is Deep Learning?

S01E02 What is a Neural Network?

S01E03 Supervised Learning with Neural Networks

S01E04 Drivers Behind the Rise of Deep Learning

S01E05 Binary Classification in Deep Learning

S01E06 Logistic Regression

S01E07 Logistic Regression Cost Function

S01E08 Gradient Descent

S01E09 Derivatives

S01E10 Derivatives Examples

S01E11 Computation Graph

S01E12 Derivatives with a Computation Graph

S01E13 Logistic Regression Derivatives

S01E14 Gradient Descent on m Training Examples

S01E15 Vectorization

S01E16 More Vectorization Examples

S01E17 Vectorizing Logistic Regressio

S01E18 Vectorizing Logistic Regression's Gradient Computation

S01E19 Broadcasting in Python

S01E20 Python-Numpy

S01E21 Jupyter-iPython

S01E22 Logistic Regression Cost Function Explanation

S01E23 Neural Network Overview

S01E24 Neural Network Representation

S01E25 Computing a Neural Network's Output

S01E26 Vectorizing Across Multiple Training Examples

S01E27 Vectorized Implementation Explanation

S01E28 Activation Functions

S01E29 Why Non-Linear Activation Function?

S01E30 Derivatives of Activation Functions

S01E31 Gradient Descent for Neural Networks

S01E32 BackPropagation Intuition

S01E33 Random Initialization of Weights

S01E34 Deep L-layer Neural Network

S01E35 Forward Propagation in Deep Networks

S01E36 Getting your Matrix Dimension Right

S01E37 Why DEEP representation?

S01E38 Building Blocks of Deep Neural Network

S01E39 Forward Propagation for Layer L

S01E40 Parameters vs Hyperparameters

S01E41 Brain and Deep Learning

S01E42 Train/Dev/Test sets

S01E43 Bias/Variance

S01E44 Basic "Recipe" of Machine Learning

S01E45 Regularization

S01E46 Why Regularization reduces Overfitting?

S01E47 Dropout Regularization

S01E48 Why does drop-out work?

S01E49 Other Regularization Methods

S01E50 Normalizing Input

S01E51 Vanishing/Exploding Gradients

S01E52 Weight Initialization for deep networks

S01E53 Numerical Approximation of Gradients

S01E54 Gradient Checking

S01E55 Gradient Checking Implantation Notes

S01E56 Mini Batch Gradient Descent

S01E57 Understanding Mini-Batch Gradient Descent

S01E58 Exponentially Weighted Averages

S01E59 Understanding Exponentially Weighted Averages

S01E60 Bias Correction in Exponentially Weighted Average

S01E61 Gradient Descent with Momentum

S01E62 RMSprop

S01E63 Adam Optimization Algorithm

S01E64 Learning Rate Decay

S01E65 The Problem of Local Optima

S01E66 Tunning Process

S01E67 Right Scale for Hyperparameters

S01E68 Hyperparameters tuning in Practice: Panda vs. Caviar

S01E69 Batch Norm

S01E70 Fitting Batch Norm into a Neural Network

S01E71 Why Does Batch Norm Work?

S01E72 Batch Norm at Test Time

S01E73 Softmax Regression

S01E74 Training a Softmax Classifier

S01E75 Deep Learning Frameworks

S01E76 TensorFlow

S01E77 Why ML Strategy?

S01E78 Orthogonalization