Basic Concept of Neurons – Perceptron Algorithm – Single Layer Neural Network and Multilayer Neural Network- Feed Forward Neural Network and Backpropagation Networks- Create and deploy neural networks using Tensor Flow for Image data.
Deep Neural Networks – Gradient Descent –Differentiation Algorithms – Vanishing Gradient Problem – Mitigation – Rectified Linear Unit (ReLU) – Heuristics for Avoiding Bad Local Minima – Heuristics for Faster Training – Nestors Accelerated Gradient Descent.
CNN Architectures – Convolution Layer– Pooling Layers –Hyper parameter–Activation Function–Recurrent and Recursive Nets – Recurrent Neural Networks – Deep Recurrent Networks – Recursive Neural Networks – Create and deploy Convolutional Neural networks using Keras for Image data.
Long Short-Term Memory (LSTM) Networks – Sequence Prediction – Gated Recurrent – Encoder/Decoder Architectures – Autoencoders – Standard – Sparse – Denoising – Contractive – Variational Autoencoders – Applications of Autoencoders – Case Study: Representation Learning
Images segmentation – Object Detection – Automatic Image Captioning – Image generation with Generative adversarial networks – Video to Text with LSTM models – Attention models for Computer Vision. Case Study: Named Entity Recognition – Opinion Mining using Recurrent Neural Networks – Parsing and Sentiment Analysis using Recursive Neural Networks
Reference Book:
1 Phil Kim, “Matlab Deep Learning: With Machine Learning, Neural Networks, and Artificial Intelligence”, Apress, 2017. 2 Ragav Venkatesan, Baoxin Li, “Convolutional Neural Networks in Visual Computing”, CRC Press, 2018. 3 Navin Kumar Manaswi, “Deep Learning with Applications Using Python”, Apress, 2018. 4 Joshua F. Wiley, “R Deep Learning Essentials”, Packt Publications, 2016.
Text Book:
Ian J. Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2017. 2 Francois Chollet, “Deep Learning with Python”, Manning Publications, 2018.