21 hours (usually 3 days including breaks)
Any programming language knowledge is required. Familiarity with Machine Learning is not required but beneficial.
This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source ) for analyzing computer images
This course provide working examples.
Deep Learning vs Machine Learning vs Other Methods
- When Deep Learning is suitable
- Limits of Deep Learning
- Comparing accuracy and cost of different methods
- Nets and Layers
- Forward / Backward: the essential computations of layered compositional models.
- Loss: the task to be learned is defined by the loss.
- Solver: the solver coordinates model optimization.
- Layer Catalogue: the layer is the fundamental unit of modeling and computation
Methods and models
- Backprop, 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 + Language with LRCN
- Pixelwise prediction with FCNs
- Framework design and future
Bookings, Prices and Enquiries
Guaranteed to run even with a single delegate!
Participants are from one organisation only. No external participants are allowed. Usually customised to a specific group, course topics are agreed between the client and the trainer.
The instructor and the participants are in two different physical locations and communicate via the Internet. More Information
|Number of Delegates||Private Remote|
From £3900 (94)
Participants from multiple organisations. Topics usually cannot be customised
|Number of Delegates||Public Classroom|
Where would you like to take it?
When are you looking to take it?
|Location||Date||Course Price [Remote/Classroom]|