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Deep Learning
Prerequisite: Knowledge of python programming language and machine learning will be highly needed. Knowledge of basic mathematics and statistics will be needed.
Course Content :
Introduction to Deep Learning
- What is Deep Learning
- Example of Deep Learning
- Architecture of Deep Learning
Types of Deep Learning Networks
- FFNN (Feed Forward Neural Network)
- RNN (Recurrent Neural Network)
- CNN (Convolutional Neural Network)
FFNN (Feed Forward Neural Network)
- What is neuron
- The Activation Function
- How ANN is Work
- What is the learning process?
RNN (Recurrent Neural Network)
- What are Recurrent Neural Networks
- The Vanishing Gradient Problem
- LSTMs
CNN (Convolutional Neural Network)
- What are convolutional neural networks?
- Convolution Operation
- ReLU Layer
- Pooling
Self-Organizing Maps
- How do Self-Organizing Maps Work?
- Why revisit K-Means?
- K-Means Clustering
- How do Self-Organizing Maps Learn?
Autoencoders
- A Note on Biases
- Training an Auto Encoder
- Overcomplete hidden layers
- Sparse Autoencoders
- Denoising Autoencoders
- Other Autoencoders