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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
- Matching networks to specific 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 MLP (Multilayer Perceptron)
- Training MLPs – 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 constitutes deep learning?
- Deep Network architecture – parameters, layers, activation functions, loss functions, and solvers
- Restricted Boltzmann Machines (RBMs)
- Autoencoders
- Deep Network Architectures
- Deep Belief Networks (DBN) – architecture and applications
- Autoencoders
- Restricted Boltzmann Machines
- Convolutional Neural Networks
- Recursive Neural Networks
- Recurrent Neural Networks
- Overview of Python Libraries and Interfaces
- Caffe
- Theano
- TensorFlow
- Keras
- MxNet
- Selecting the appropriate library for the problem
- Building Deep Networks in Python
- Choosing the right architecture for the given problem
- Hybrid deep networks
- Training the network – selecting the library and defining architecture
- Tuning the network – initialization, activation and loss functions, and optimization methods
- Avoiding overfitting – detecting overfitting issues and applying regularization
- Evaluating deep networks
- Case Studies in Python
- Image recognition – CNNs
- Anomaly detection with Autoencoders
- Time series forecasting with RNNs
- Dimensionality reduction with Autoencoders
- Classification with RBMs
Requirements
Familiarity or appreciation of machine learning, system architecture, and programming languages is 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
