Deep Learning for Vision Training Course
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
The more delegates, the greater the savings per delegate. Table reflects price per delegate and is used for illustration purposes only, actual prices may differ.
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