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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 inspiration for neural networks
- Neural network components: neurons, perceptrons, and MLP (Multilayer Perceptron) models
- MLP learning – the backpropagation algorithm
- Activation functions – linear, sigmoid, Tanh, Softmax
- Loss functions suitable for forecasting and classification
- Key parameters – learning rate, regularization, momentum
- Constructing 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 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, optimization methods
- Avoiding overfitting – detecting issues in deep networks 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 using RBM
Requirements
Familiarity with machine learning, system architecture, and programming languages is recommended.
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
