Biological neuron, idea of computational units, McCulloch – pitts unit and thresholding logic, linear perceptron, perceptron learning algorithm, convergence theorem for Perceptron learning algorithm, logistic regression, gradient descent.
Feed forward neural network, activation functions, non-linear activation functions. multi-layer neural network.
Practical aspects of deep Learning: training, testing, regularization –dataset augmentation, Noise robustness, multitask learning, bagging and other ensemble methods, dropout- generalization.
Convolution neural networks, backpropagation convolutions and pooling – optimization algorithms: mini-batch gradient descent, – convolutional nets case studies using Keras / TensorFlow.
Neural network architectures – recurrent neural networks, adversarial neural networks Spectral CNN, self-organizing maps, restricted boltzmann machines, long short-term memory networks, deep meta learning – deep reinforcement learning.
1. Ian Goodfellow, YoshuaBengio and Aeron Courville, Deep Learning, MIT Press,First Edition, 2016.
2. Gibson and Josh Patterson, Deep Learning A practitioner’s approach, Adam O’Reilly, First Edition, 2017.
3. Francois Chollet, Deep Learning with Python, Manning Publications Co, First Edition, 2018.
4. Bishop C.M.Neural Networks for Pattern Recognition, Oxford University Press,1995.
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