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
Deep Learning vs Machine Learning vs Other Methods
- Scenarios where Deep Learning is appropriate
- Limitations of Deep Learning
- Comparing accuracy and costs across different methods
Overview of Methods
- Neural Networks and Layers
- Forward and Backward Pass: The core computations of layered compositional models.
- Loss: The learning objective is defined by the loss function.
- Solver: The solver orchestrates model optimization.
- Layer Catalogue: The layer serves as the fundamental unit for modeling and computation.
- Convolution
Methods and Models
- Backpropagation and modular models
- Logsum module
- RBF Network
- MAP/MLE loss
- Parameter Space Transforms
- Convolutional Module
- Gradient-Based Learning
- Energy-based inference
- Learning objectives
- PCA; NLL:
- Latent Variable Models
- Probabilistic LVM
- Loss Function
- Detection using Fast R-CNN
- Sequences with LSTMs and Vision-Language integration with LRCN
- Pixel-wise prediction using FCNs
- Framework design and future directions
Tools
- Caffe
- TensorFlow
- R
- Matlab
- Others...
Requirements
Knowledge of any programming language is required. While prior familiarity with Machine Learning is not mandatory, it is advantageous.
21 Hours
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
