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Deep Learning with TensorFlow
Introduction
Welcome to Machine Learning (0:30)
Welcome to the Course
What does the course cover (3:07)
Neural networks Intro
Introduction to neural networks (4:09)
Training the model (2:54)
Types of machine learning (3:43)
The linear model (3:08)
The linear model. Multiple inputs. (2:25)
The linear model. Multiple inputs and multiple outputs (4:25)
Graphical representation (1:47)
The objective function (1:27)
L2-norm loss (2:04)
Cross-entropy loss (3:55)
One-parameter gradient descent (6:33)
N-parameter gradient descent (6:08)
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Setting up the environment
Setting up the environment - Do not skip, please! (0:49)
Why Python and why Jupyter (4:43)
Installing Anaconda (3:18)
Jupyter Dashboard - Part 1 (2:53)
Jupyter Dashboard - Part 2 (5:36)
Installing the TensorFlow package (2:20)
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Minimal example
Outline (3:06)
Generating the data (optional) (4:58)
Initializing the variables (3:25)
Training the model (8:15)
Exercises
Introduction to TensorFlow
TensorFlow outline (3:46)
TensorFlow introduction (1:40)
Types of file formats used in TensorFlow (2:38)
Laying down the model (6:05)
Laying down the optimizers (3:41)
Output (6:05)
Exercises
Deep nets overview
The layer (1:52)
What is a deep net (2:18)
Really understand deep nets (4:58)
Why do we need non-linearities (2:59)
Activation functions (3:37)
Softmax activation (3:23)
Backpropagation (3:12)
Backpropagation - intuition (3:02)
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Backpropagation Mathematics (optional)
Backpropagation mathematics
Overfitting
Underfitting and overfitting (3:51)
Underfitting and overfitting. A classification example (1:52)
Train vs validation (3:22)
Train vs validation vs test (2:30)
N-fold cross validation (3:07)
Early stopping - motivation and types (4:54)
Initialization
Initializaiton (2:32)
Types of simple initializations (2:47)
Xavier's initialization (2:45)
Optimizers
SGD&Batching (3:24)
Local minima pitfalls (2:02)
Momentum (2:30)
Learning rate schedules (4:25)
Learning rate schedules. A picture (1:32)
Adaptive learning schedules (4:08)
Adaptive moment estimation (2:39)
Preprocessing
Preprocessing (2:51)
Basic preprocessing (1:17)
Standardization (4:31)
Dealing with categorical data (2:15)
One-hot vs binary (3:39)
Deeper example
MNIST dataset (2:25)
How to tackle the MNIST dataset (2:48)
MNIST - Importing libraries and data (1:34)
MNIST - Outlining the model (6:51)
MNIST - Declaring the loss (2:39)
Accuracy of a model (4:18)
Early stopping and batching preparation (2:08)
Optimization (7:35)
Commenting on the results (6:11)
Exercises
Business case
The dataset (7:55)
Outlining the solution (1:57)
Balancing a dataset (3:39)
Preprocessing the data (11:35)
Creating the batching class (6:37)
Outlining the model (5:34)
Optimizing the algorithm (5:10)
Running the code (2:05)
Test (2:04)
Homework (3:51)
Conclusion
Summary (3:41)
Whats more out there (1:47)
An overview of CNNs (4:55)
An overview of RNNs (2:50)
Non-NN approaches (3:52)
Balancing a dataset
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