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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 different problems and data types
- Training and validating neural networks
- Comparing logistic regression with neural networks
- Neural Networks
- Biological inspirations for neural networks
- Neural Networks – Neurons, Perceptrons, and MLP (Multilayer Perceptron models)
- Training MLPs – the backpropagation algorithm
- Activation functions – linear, sigmoid, Tanh, Softmax
- Loss functions suitable 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?
- Deep Network Architecture – 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 the appropriate library for the problem
- Building Deep Networks in Python
- Selecting the appropriate architecture for a given problem
- Hybrid deep networks
- Training the network – selecting the appropriate library, defining architecture
- Tuning the network – initialization, activation functions, loss functions, optimization methods
- Avoiding overfitting – detecting overfitting issues in deep networks, regularization
- Evaluating deep networks
- Case Studies in Python
- Image recognition – CNN
- Anomaly detection with Autoencoders
- Time series forecasting with RNN
- Dimensionality reduction with Autoencoders
- Classification with RBM
Requirements
Familiarity and appreciation of machine learning, system architecture, and programming languages are 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
