Neural computing – Data science Training Course


14 hours (usually 2 days including breaks)


Knowledge/appreciation of machine learning, systems architecutre and programming languages are desirable


This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries

Course Outline

  1. Overview of neural networks and deep learning
    • The concept of Machine Learning (ML)
    • Why we need neural networks and deep learning?
    • Selecting networks to different problems and data types
    • Learning and validating neural networks
    • Comparing logistic regression to neural network
  2. Neural network
    • Biological inspirations to Neural network
    • Neural Networks– Neuron, Perceptron and MLP(Multilayer Perceptron model)
    • Learning MLP – backpropagation algorithm
    • Activation functions – linear, sigmoid, Tanh, Softmax
    • Loss functions appropriate to forecasting and classification
    • Parameters – learning rate, regularization, momentum
    • Building Neural Networks in Python
    • Evaluating performance of neural networks in Python
  3. Basics of Deep Networks
    • What is deep learning?
    • Architecture of Deep Networks– Parameters, Layers, Activation Functions, Loss functions, Solvers
    • Restricted Boltzman Machines (RBMs)
    • Autoencoders
  4. Deep Networks Architectures
    • Deep Belief Networks(DBN) – architecture, application
    • Autoencoders
    • Restricted Boltzmann Machines
    • Convolutional Neural Network
    • Recursive Neural Network
    • Recurrent Neural Network
  5. Overview of libraries and interfaces available in Python
    • Caffee
    • Theano
    • Tensorflow
    • Keras
    • Mxnet
    • Choosing appropriate library to problem
  6. Building deep networks in Python
    • Choosing appropriate architecture to given problem
    • Hybrid deep networks
    • Learning network – appropriate library, architecture definition
    • Tuning network – initialization, activation functions, loss functions, optimization method
    • Avoiding overfitting – detecting overfitting problems in deep networks, regularization
    • Evaluating deep networks
  7. Case studies in Python
    • Image recognition – CNN
    • Detecting anomalies with Autoencoders
    • Forecasting time series with RNN
    • Dimensionality reduction with Autoencoder
    • Classification with RBM

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