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Course Outline
- Overview of Neural Networks and Deep Learning
- The concept of Machine Learning (ML)
- The necessity of neural networks and deep learning
- Selecting appropriate networks for various problems and data types
- Training and validating neural networks
- Comparing logistic regression with neural networks
- Neural Networks
- Biological inspirations behind neural networks
- Neural Networks – Neurons, Perceptrons, and MLPs (Multilayer Perceptron models)
- MLP Learning – The backpropagation algorithm
- Activation Functions – Linear, Sigmoid, Tanh, and Softmax
- Loss Functions suitable for forecasting and classification
- Parameters – Learning rate, regularization, and momentum
- Building Neural Networks in Python
- Evaluating Neural Network performance in Python
- Basics of Deep Networks
- What is Deep Learning?
- Deep Network Architecture – Parameters, Layers, Activation Functions, Loss Functions, Solvers
- Restricted Boltzmann Machines (RBMs)
- Autoencoders
- Deep Network Architectures
- Deep Belief Networks (DBN) – Architecture and applications
- Autoencoders
- Restricted Boltzmann Machines
- Convolutional Neural Networks (CNN)
- Recursive Neural Networks
- Recurrent Neural Networks (RNN)
- Overview of Available Libraries and Interfaces in Python
- Caffe
- Theano
- TensorFlow
- Keras
- MxNet
- Selecting the appropriate library for specific problems
- Building Deep Networks in Python
- Choosing the right architecture for a given problem
- Hybrid Deep Networks
- Training networks – Selecting appropriate libraries and defining architecture
- Tuning networks – Initialization, activation functions, loss functions, and optimization methods
- Avoiding Overfitting – Detecting overfitting issues and applying regularization
- Evaluating Deep Networks
- Case Studies in Python
- Image Recognition using CNN
- Anomaly Detection with Autoencoders
- Time Series Forecasting with RNN
- Dimensionality Reduction with Autoencoders
- Classification with RBM
Requirements
Familiarity and appreciation for machine learning, system architecture, and programming languages are preferred.
14 Hours
