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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 to 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 appropriate for forecasting and classification
- Parameters – learning rate, regularization, 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 Networks
- Recursive Neural Networks
- Recurrent Neural Networks
- Overview of libraries and interfaces available in Python
- Caffe
- Theano
- Tensorflow
- Keras
- Mxnet
- Choosing appropriate libraries for specific problems
- Building deep networks in Python
- Choosing appropriate architecture for given problems
- Hybrid deep networks
- Learning networks – appropriate library, architecture definition
- Tuning networks – initialization, activation functions, loss functions, optimization method
- Avoiding overfitting – detecting overfitting problems in deep networks, regularization
- 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
Knowledge or understanding of machine learning, system architecture, and programming languages is desirable.
14 Hours
Testimonials (1)
The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at
