Home / Series / Deep Learning / Aired Order /
  • TheTVDB.com Season ID 779744
  • Created September 21, 2018
  • Modified September 21, 2018
When available, episode names will be translated into your preferred language. Otherwise they will be shown using the series' origin language.
Name First Aired Runtime Image
S01E01 What is Deep Learning?
September 23, 2017
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S01E02 What is a Neural Network?
September 23, 2017
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S01E03 Supervised Learning with Neural Networks
September 23, 2017
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S01E04 Drivers Behind the Rise of Deep Learning
September 23, 2017
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S01E05 Binary Classification in Deep Learning
September 23, 2017
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S01E06 Logistic Regression
September 23, 2017
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S01E07 Logistic Regression Cost Function
September 23, 2017
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S01E08 Gradient Descent
September 23, 2017
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S01E09 Derivatives
September 23, 2017
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S01E10 Derivatives Examples
September 23, 2017
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S01E11 Computation Graph
September 23, 2017
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S01E12 Derivatives with a Computation Graph
September 23, 2017
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S01E13 Logistic Regression Derivatives
September 23, 2017
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S01E14 Gradient Descent on m Training Examples
September 23, 2017
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S01E15 Vectorization
September 23, 2017
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S01E16 More Vectorization Examples
September 23, 2017
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S01E17 Vectorizing Logistic Regressio
September 23, 2017
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S01E18 Vectorizing Logistic Regression's Gradient Computation
September 23, 2017
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S01E19 Broadcasting in Python
September 23, 2017
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S01E20 Python-Numpy
September 23, 2017
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S01E21 Jupyter-iPython
September 23, 2017
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S01E22 Logistic Regression Cost Function Explanation
September 23, 2017
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S01E23 Neural Network Overview
September 23, 2017
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S01E24 Neural Network Representation
September 23, 2017
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S01E25 Computing a Neural Network's Output
September 23, 2017
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S01E26 Vectorizing Across Multiple Training Examples
September 23, 2017
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S01E27 Vectorized Implementation Explanation
September 23, 2017
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S01E28 Activation Functions
September 23, 2017
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S01E29 Why Non-Linear Activation Function?
September 23, 2017
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S01E30 Derivatives of Activation Functions
September 23, 2017
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S01E31 Gradient Descent for Neural Networks
September 23, 2017
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S01E32 BackPropagation Intuition
September 23, 2017
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S01E33 Random Initialization of Weights
September 23, 2017
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S01E34 Deep L-layer Neural Network
September 23, 2017
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S01E35 Forward Propagation in Deep Networks
September 23, 2017
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S01E36 Getting your Matrix Dimension Right
September 23, 2017
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S01E37 Why DEEP representation?
September 23, 2017
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S01E38 Building Blocks of Deep Neural Network
September 23, 2017
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S01E39 Forward Propagation for Layer L
September 23, 2017
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S01E40 Parameters vs Hyperparameters
September 23, 2017
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S01E41 Brain and Deep Learning
September 23, 2017
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S01E42 Train/Dev/Test sets
September 23, 2017
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S01E43 Bias/Variance
September 23, 2017
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S01E44 Basic "Recipe" of Machine Learning
September 23, 2017
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S01E45 Regularization
September 23, 2017
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S01E46 Why Regularization reduces Overfitting?
September 23, 2017
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S01E47 Dropout Regularization
September 23, 2017
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S01E48 Why does drop-out work?
September 23, 2017
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S01E49 Other Regularization Methods
September 23, 2017
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S01E50 Normalizing Input
September 23, 2017
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S01E51 Vanishing/Exploding Gradients
September 23, 2017
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S01E52 Weight Initialization for deep networks
September 23, 2017
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S01E53 Numerical Approximation of Gradients
September 23, 2017
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S01E54 Gradient Checking
September 23, 2017
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S01E55 Gradient Checking Implantation Notes
September 23, 2017
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S01E56 Mini Batch Gradient Descent
September 23, 2017
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S01E57 Understanding Mini-Batch Gradient Descent
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S01E58 Exponentially Weighted Averages
September 23, 2017
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S01E59 Understanding Exponentially Weighted Averages
September 23, 2017
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S01E60 Bias Correction in Exponentially Weighted Average
September 23, 2017
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S01E61 Gradient Descent with Momentum
September 23, 2017
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S01E62 RMSprop
September 23, 2017
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S01E63 Adam Optimization Algorithm
September 23, 2017
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S01E64 Learning Rate Decay
September 23, 2017
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S01E65 The Problem of Local Optima
September 23, 2017
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S01E66 Tunning Process
September 23, 2017
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S01E67 Right Scale for Hyperparameters
September 23, 2017
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S01E68 Hyperparameters tuning in Practice: Panda vs. Caviar
September 23, 2017
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S01E69 Batch Norm
September 23, 2017
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S01E70 Fitting Batch Norm into a Neural Network
September 23, 2017
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S01E71 Why Does Batch Norm Work?
September 23, 2017
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S01E72 Batch Norm at Test Time
September 23, 2017
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S01E73 Softmax Regression
September 23, 2017
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S01E74 Training a Softmax Classifier
September 23, 2017
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S01E75 Deep Learning Frameworks
September 23, 2017
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S01E76 TensorFlow
September 23, 2017
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S01E77 Why ML Strategy?
September 23, 2017
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S01E78 Orthogonalization
September 23, 2017
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S01E79 Single Number Evaluation Metric
September 23, 2017
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S01E80 Satisfying and Optimizing Metrics
September 23, 2017
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S01E81 train/dev/test distributions
September 23, 2017
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S01E82 Size of dev and test sets
September 23, 2017
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S01E83 When to change dev/test sets and metrics?
September 23, 2017
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S01E84 Why human-level performance?
September 23, 2017
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S01E85 Avoidable Bias
September 23, 2017
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S01E86 Understanding Human-Level Performance
September 23, 2017
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S01E87 Surpassing Human-Level Performance
September 23, 2017
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S01E88 Improving Your Model Performance
September 23, 2017
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S01E89 Carrying Out Error Analysis
September 23, 2017
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S01E90 Cleaning Up Incorrect Labeled Data
September 23, 2017
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S01E91 Build Your First System Quickly, Then Iterate
September 23, 2017
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S01E92 Training and Testing on Different Distributions
September 23, 2017
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S01E93 Bias and Variance with Mismatched data distributions
September 23, 2017
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S01E94 Addressing Data Mismatch
September 23, 2017
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S01E95 Transfer Learning
September 23, 2017
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S01E96 Multi-Task Learning
September 23, 2017
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S01E97 End-to-End Deep Learning
September 23, 2017
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S01E98 Whether to use End-to-End Learning
series finale
September 23, 2017
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