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Course Outline
Deep Learning vs Machine Learning vs Other Methods
- When deep learning is appropriate
- Limitations of deep learning
- Comparing the accuracy and cost of different methods
Methods Overview
- Nets and Layers
- Forward and Backward: the core computations in layered compositional models.
- Loss: the task to be learned is defined by the loss function.
- Solver: coordinates model optimisation.
- Layer Catalogue: the layer is the fundamental unit of modelling and computation.
- Convolution
Methods and Models
- Backprop and modular models
- Logsum module
- RBF Net
- MAP/MLE loss
- Parameter space transforms
- Convolutional module
- Gradient-based learning
- Energy for inference,
- Objective for learning
- PCA; NLL:
- Latent variable models
- Probabilistic LVM
- Loss function
- Detection with Fast R-CNN
- Sequences with LSTMs and vision plus language with LRCN
- Pixelwise prediction with FCNs
- Framework design and future directions
Tools
- Caffe
- Tensorflow
- R
- Matlab
- Others...
Requirements
Knowledge of at least one programming language is required. Familiarity with machine learning is not essential but 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
