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Course Outline
- Overview of Neural Networks and Deep Learning
- Concepts of Machine Learning (ML)
- The necessity of neural networks and deep learning
- Selecting appropriate networks for specific problems and data types
- Training and validating neural networks
- Comparing logistic regression with neural networks
- Fundamentals of Neural Networks
- Biological inspirations behind neural networks
- Neural Networks – Neurons, Perceptrons, and MLP (Multilayer Perceptron)
- Training MLPs – the backpropagation algorithm
- Activation functions – linear, sigmoid, Tanh, Softmax
- Loss functions suitable for forecasting and classification
- Key parameters – learning rate, regularization, momentum
- Implementing neural networks in Python
- Evaluating neural network performance in Python
- Introduction to Deep Networks
- Understanding 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 (CNNs)
- Recursive Neural Networks
- Recurrent Neural Networks (RNNs)
- Overview of Python Libraries and Interfaces
- Caffe
- Theano
- TensorFlow
- Keras
- MxNet
- Selecting the appropriate library for your problem
- Building Deep Networks in Python
- Choosing the right architecture for a given problem
- Hybrid deep networks
- Training networks – selecting appropriate libraries and defining architecture
- Optimizing networks – initialization, activation functions, loss functions, optimization methods
- Preventing overfitting – identifying issues in deep networks and applying regularization
- Evaluating deep networks
- Python Case Studies
- Image recognition using CNNs
- Anomaly detection with autoencoders
- Time series forecasting with RNNs
- Dimensionality reduction using autoencoders
- Classification with RBMs
Requirements
Familiarity with machine learning concepts, 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
