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
- Neural Networks and Deep Learning Overview
- Understanding the fundamentals 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
- Core Neural Networks
- Biological foundations of neural networks
- Key components: Neurons, Perceptrons, and MLPs (Multilayer Perceptron models)
- Training MLPs using the backpropagation algorithm
- Activation functions: linear, sigmoid, Tanh, and Softmax
- Loss functions suitable for forecasting and classification tasks
- Key parameters: learning rate, regularization, and momentum
- Implementing neural networks in Python
- Evaluating neural network performance in Python
- Fundamentals 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 (DBN): architecture and applications
- Autoencoders
- Restricted Boltzmann Machines
- Convolutional Neural Networks (CNNs)
- Recursive Neural Networks
- Recurrent Neural Networks (RNNs)
- Python Libraries and Interfaces Overview
- 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
- Network training: selecting the appropriate library and defining architecture
- Network tuning: initialization, activation functions, loss functions, and optimization methods
- Preventing overfitting: identifying overfitting issues in deep networks and applying regularization
- Evaluating deep network performance
- 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
