Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
- Overview of Neural Networks and Deep Learning
- Understanding 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 behind neural networks
- Neural networks – neurons, perceptrons, and MLPs (Multilayer Perceptron models)
- Training MLPs – the backpropagation algorithm
- Activation functions – linear, sigmoid, Tanh, and Softmax
- Loss functions suitable for forecasting and classification
- Key parameters – learning rate, regularization, and momentum
- Constructing neural networks in Python
- Evaluating neural network performance in Python
- Foundations of Deep Networks
- Defining deep learning
- Deep network architecture – parameters, layers, activation functions, loss functions, and solvers
- Restricted Boltzmann Machines (RBMs)
- Autoencoders
- Deep Network Architectures
- Deep Belief Networks (DBNs) – 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 specific problems
- Building Deep Networks in Python
- Choosing the right architecture for a given problem
- Hybrid deep networks
- Training networks – selecting libraries and defining architectures
- Tuning networks – initialization, activation functions, loss functions, and optimization methods
- Preventing overfitting – identifying and addressing overfitting issues in deep networks, regularization techniques
- Evaluating deep networks
- Python Case Studies
- Image recognition using CNNs
- Anomaly detection with autoencoders
- Time series forecasting with RNNs
- Dimensionality reduction using autoencoders
- Classification using RBMs
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
