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Course Outline
Deep Learning vs Machine Learning vs Other Methods
- Appropriate scenarios for Deep Learning
- Constraints and limitations of Deep Learning
- Evaluating accuracy and cost across different methods
Methods Overview
- Networks and Layers
- Forward / Backward: The core computations in layered compositional models
- Loss: How the learning objective is defined by the loss
- Solver: The mechanism coordinating model optimization
- Layer Catalogue: The layer as the basic unit of modeling and computation
- Convolution
Methods and Models
- Backpropagation and modular models
- Logsum module
- RBF Net
- MAP/MLE loss
- Parameter Space Transforms
- Convolutional Module
- Gradient-Based Learning
- Energy functions for inference
- Learning objectives
- PCA; NLL
- Latent Variable Models
- Probabilistic LVM
- Loss Function
- Object detection using Fast R-CNN
- Sequence modeling with LSTMs and Vision + Language integration via LRCN
- Pixelwise prediction with FCNs
- Framework design and future directions
Tools
- Caffe
- Tensorflow
- R
- Matlab
- Others
Requirements
A foundational understanding 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
It felt like we were going through directly relevant information at a good pace (i.e. no filler material)
