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Course Outline
- Overview of neural networks and deep learning
- The concept of Machine Learning (ML)
- Why we need neural networks and deep learning?
- Selecting networks for different problems and data types
- Learning and validating neural networks
- Comparing logistic regression with neural networks
- Neural networks
- Biological inspirations for neural networks
- Neural networks – Neuron, Perceptron and MLP (Multilayer Perceptron model)
- Learning MLP – backpropagation algorithm
- Activation functions – linear, sigmoid, Tanh, Softmax
- Loss functions suitable for forecasting and classification
- Parameters – learning rate, regularisation, momentum
- Building neural networks in Python
- Evaluating the performance of neural networks in Python
- Basics of deep networks
- What is deep learning?
- Architecture of deep networks – parameters, layers, activation functions, loss functions, solvers
- Restricted Boltzmann Machines (RBMs)
- Autoencoders
- Deep network architectures
- Deep Belief Networks (DBN) – architecture, application
- Autoencoders
- Restricted Boltzmann Machines
- Convolutional Neural Network
- Recursive Neural Network
- Recurrent Neural Network
- Overview of libraries and interfaces available in Python
- Caffe
- Theano
- TensorFlow
- Keras
- MXNet
- Choosing the appropriate library for the problem
- Building deep networks in Python
- Choosing the appropriate architecture for a given problem
- Hybrid deep networks
- Learning the network – appropriate library, architecture definition
- Tuning the network – initialisation, activation functions, loss functions, optimisation method
- Avoiding overfitting – detecting overfitting problems in deep networks, regularisation
- Evaluating deep networks
- Case studies in Python
- Image recognition – CNN
- Detecting anomalies with autoencoders
- Forecasting time series with RNN
- Dimensionality reduction with autoencoders
- Classification with RBM
Requirements
A knowledge or appreciation of machine learning, system architecture, and programming languages is desirable.
14 Hours
