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Course Outline
Deep Learning vs Machine Learning vs Other Methods
- Identifying when Deep Learning is the appropriate approach
- Understanding the limitations of Deep Learning
- Comparing the accuracy and cost implications of various methods
Methods Overview
- Nets and Layers
- Forward and Backward Passes: The essential computations within layered compositional models
- Loss: How the loss function defines the task to be learned
- Solver: The role of the solver in coordinating model optimization
- Layer Catalogue: Layers as the fundamental units of 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
- Objective Functions for Learning
- PCA and NLL
- Latent Variable Models
- Probabilistic Latent Variable Models
- Loss Functions
- Object Detection using Fast R-CNN
- Sequence Modeling with LSTMs and Vision-Language Integration with LRCN
- Pixelwise Prediction with Fully Convolutional Networks (FCNs)
- Framework Design and Future Directions
Tools
- Caffe
- TensorFlow
- R
- Matlab
- And more...
Requirements
Proficiency in any programming language is mandatory. While prior knowledge of Machine Learning is not strictly required, 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)
