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Course Outline
Deep Learning vs. Machine Learning vs. Other Approaches
- When deep learning is the appropriate choice
- Limits of deep learning
- Comparing accuracy and cost across different methods
Overview of Methods
- Networks and Layers
- Forward / Backward: The essential computations of layered compositional models
- Loss: The objective function that defines the task to be learned
- Solver: The component responsible for coordinating model optimization
- Layer Catalogue: The fundamental 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-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
- Pixelwise prediction using FCNs
- Framework design and future directions
Tools
- Caffe
- Tensorflow
- R
- Matlab
- Others...
Requirements
A foundational understanding of any programming language is required. While prior knowledge of 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
