Developing Artificial Intelligence Applications (online) 

offered by Oxford University

Introduction to machine learning, deep learning and AI

Introduction to neural networks, TensorFlow and Keras

Important changes in TensorFlow 2.0

Introduction to neural networks

Introduction to Artificial Intelligence applications

Multi-layer perceptron (MLP) -our first example of a network

Problems in training the perceptron and their solutions

Activation functions -sigmoid, tanh, ReLU and others

Establishing a baseline

Regularization to avoid overfitting

What is regression?

Prediction using linear regression

Simple linear regression, multiple linear regression and multivariate linear regression

Predicting house price using linear regression

Logistic regression

Logistic regression on the MNIST (Modified National Institute of Standards and Technology) dataset

Deep Convolutional Neural Network (DCNN)

Local receptive fields

Shared weights and bias

Pooling layers, max pooling, average pooling

LeNet code in TensorFlow 2.0

Word embedding

The basic RNN cell

Backpropagation through time (BPTT)

Vanishing and exploding gradients

Long short-term memory (LSTM)

Gated recurrent unit (GRU)

Introduction to autoencoders

Vanilla autoencoders

Sparse autoencoder

Denoising autoencoders

Clearing images using a denoising autoencoder

Stacked autoencoder

Convolutional autoencoder for removing noise from images

Principal component analysis

PCA on the MNIST dataset

K-means clustering

Restricted Boltzmann machines

Reconstructing images using RBM

Deep belief networks

Variational Autoencoders