Data Science & AI
Deep Learning
Covers Neural Networks, Deep Neural Networks, CNNs & Autoencoders.
- DURATION
- [Ask us]
- PREREQUISITES
- [Ask us]
- SYLLABUS
- 3 modules
- FEE
- On enquiry
Syllabus
Every module is taught live, with practice problems and assignments between classes.
01Neural Networks5 topics +
- Biological neuron
- McCulloch-Pitts neuron
- Perceptron and perceptron learning
- Delta learning
- Multilayer perceptron
02Deep Neural Networks6 topics +
- Training feedforward DNNs
- Hyperparameter tuning
- Activation functions
- Loss functions
- Optimizers: GD, SGD, mini-batch, momentum, NAG, AdaGrad, RMSProp, Adam
- Regularization: L1/L2, dropout, batch norm, early stopping, data augmentation
03CNNs & Autoencoders3 topics +
- Convolution, padding, stride
- CNN architecture and variants
- Denoising, sparse and contractive autoencoders
Not sure where to start?
Book a free demo class. Pick a few times that suit you and we'll call you back to confirm one, answer your questions and suggest a track.