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Deep Learning with Tensorflow 2.0
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Introduction
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Meet your instructors and why you should study machine learning
What does this course cover?
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Introduction to neural networks
12 Topics
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Introduction to neural networks
Training the model
Types of machine learning
The linear model
The linear model. Multiple inputs
The linear model. Multiple inputs and multiple outputs
Graphical representation
The objective function
L2-norm loss
Cross-entropy loss
One parameter gradient descent
N-parameter gradient descent
Setting up the environment
8 Topics
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Setting up the environment – An introduction – Do not skip, please!
Why Python and why Jupyter?
Installing Anaconda
The Jupyter dashboard – part 1
The Jupyter dashboard – part 2
Installing TensorFlow 2
Installing packages – exercise
Installing packages – solution
Minimal Example – your first machine learning algorithm
4 Topics
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Minimal example – part 1
Minimal example – part 2
Minimal example – part 3
Minimal example – part 4
TensorFlow – An Introduction
7 Topics
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TensorFlow outline
TensorFlow 2 intro
A Note on Coding in TensorFlow
Types of file formats in TensorFlow and data handling
Model layout – inputs, outputs, targets, weights, biases, optimizer and loss
Interpreting the result and extracting the weights and bias
Cutomizing your model
Going deeper : Introduction to deep neural networks
8 Topics
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Layers
What is a deep net ?
Understanding deep nets in depth
Why do we need non-linearities?
Activation functions
Softmax activation
Backpropagation
Backpropagation – visual representation
Overfitting
6 Topics
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Underfitting and overfitting
Underfitting and overfitting – classification
Training and validation
Training, validation, and test
N-fold cross validation
Early stopping
Initialization
3 Topics
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Initialization – Introduction
Types of simple initializations
Xavier initialization
Gradient descent and learning rates
7 Topics
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Stochastic gradient descent
Gradient descent pitfalls
Momentum
Learning rate schedules
Learning rate schedules. A picture
Adaptive learning rate schedules
Adaptive moment estimation
Preprocessing
5 Topics
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Preprocessing introduction
Basic preprocessing
Standardization
Dealing with categorical data
One-hot and binary encoding
The MNIST example
9 Topics
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The dataset
How to tackle the MNIST
Importing the relevant packages and load the data
Preprocess the data – create a validation dataset and scale the data
Preprocess the data – shuffle and batch the data
Outline the model
Select the loss and the optimizer
Learning
Testing the model
Business Case
8 Topics
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Exploring the dataset and identifying predictors
Outlining the business case solution
Balancing the dataset
Preprocessing the data
Load the preprocessed data
Learning and interpreting the result
Setting an early stopping mechanism
Testing the model
Appendix: Linear Algebra Fundamentals
11 Topics
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What is a Matrix?
Scalars and Vectors
Linear Algebra and Geometry
Scalars, Vectors and Matrices in Python
Tensors
Addition and Subtraction of Matrices
Errors when Adding Matrices
Transpose of a Matrix
Dot Product of Vectors
Dot Product of Matrices
Why is Linear Algebra Useful?
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