Welcome! Course introduction
  • Meet your instructors and why you should study machine learning?
  • What does the course cover?
  • What does the course cover? - Quiz
  • Download All Resources and Important FAQ
Introduction to neural networks
  • Introduction to neural networks
  • Introduction to neural networks - Quiz
  • Training the model
  • Training the model - Quiz
  • Types of machine learning
  • Types of machine learning - Quiz
  • The linear model
  • The linear model - Quiz
  • Need Help with Linear Algebra?
  • The linear model. Multiple inputs
  • The linear model. Multiple inputs - Quiz
  • The linear model. Multiple inputs and multiple outputs
  • The linear model. Multiple inputs and multiple outputs - Quiz
  • Graphical representation
  • Graphical representation - Quiz
  • The objective function
  • The objective function - Quiz
  • L2-norm loss
  • L2-norm loss - Quiz
  • Cross-entropy loss
  • Cross-entropy loss - Quiz
  • One parameter gradient descent
  • One parameter gradient descent - Quiz
  • N-parameter gradient descent
  • N-parameter gradient descent - Quiz
Setting up the working environment
  • Setting up the environment - An introduction - Do not skip, please!
  • Why Python and why Jupyter?
  • Why Python and why Jupyter? - Quiz
  • Installing Anaconda
  • The Jupyter dashboard - part 1
  • The Jupyter dashboard - part 2
  • Jupyter Shortcuts
  • The Jupyter dashboard - Quiz
  • Installing TensorFlow 2
  • Installing packages - exercise
  • Installing packages - solution
Minimal example - your first machine learning algorithm
  • Minimal example - part 1
  • Minimal example - part 2
  • Minimal example - part 3
  • Minimal example - part 4
  • Minimal example - Exercises
TensorFlow - An introduction
  • 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
  • Minimal example with TensorFlow - Exercises
Going deeper: Introduction to deep neural networks
  • 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
Backpropagation. A peek into the Mathematics of Optimization
  • Backpropagation. A peek into the Mathematics of Optimization
Overfitting
  • Underfitting and overfitting
  • Underfitting and overfitting - classification
  • Training and validation
  • Training, validation, and test
  • N-fold cross validation
  • Early stopping
Initialization
  • Initialization - Introduction
  • Types of simple initializations
  • Xavier initialization
Gradient descent and learning rates
  • Stochastic gradient descent
  • Gradient descent pitfalls
  • Momentum
  • Learning rate schedules
  • Learning rate schedules. A picture
  • Adaptive learning rate schedules
  • Adaptive moment estimation
Preprocessing
  • Preprocessing introduction
  • Basic preprocessing
  • Standardization
  • Dealing with categorical data
  • One-hot and binary encoding
The MNIST example
  • 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 - scale the test data