Introduction to Deep Learning & Neural Networks
The domain of machine learning and its implications to the artificial intelligence sector, the advantages of machine learning over other conventional methodologies, introduction to Deep Learning within machine learning, how it differs from all others methods of machine learning, training the system with training data, supervised and unsupervised learning, classification and regression supervised learning, clustering and association unsupervised learning, the algorithms used in these types of learning. Introduction to AI, Introduction to Neural Networks, Supervised Learning with Neural Networks, Concept of Machine Learning, Basics of statistics, probability distributions, hypothesis testing, Hidden Markov Model.
Multi-layered Neural Networks
Introduction to Multi Layer Network, Concept of Deep neural networks, Regularization. Multi-layer perceptron, capacity and overfitting, neural network hyperparameters, logic gates, the various activation functions in neural networks like Sigmoid, ReLu and Softmax, hyperbolic functions. Backpropagation, convergence, forward propagation, overfitting, hyperparameters.
Training of neural networks
The various techniques used in training of artificial neural networks, gradient descent rule, perceptron learning rule, tuning learning rate, stochastic process, optimization techniques, regularization techniques, regression techniques Lasso L1, Ridge L2, vanishing gradients, transfer learning, unsupervised pre-training, Xavier initialization, vanishing gradients.
Deep Learning Libraries
How Deep Learning Works, Activation Functions, Illustrate Perceptron, Training a Perceptron, Important Parameters of Perceptron, Multi-layer Perceptron What is TensorFlow, Introduction to TensorFlow open source software library for designing, building and training Deep Learning models, Python Library behind TensorFlow, Tensor Processing Unit (TPU) programmable AI accelerator by Google, TensorFlow code-basics, Graph Visualization, Constants, Placeholders, Variables, Step by Step – Use-Case Implementation, Keras.
Introduction to Keras API
Keras high-level neural network for working on top of TensorFlow, defining complex multi-output models, composing models using Keras, sequential and functional composition, batch normalization, deploying Keras with TensorBoard, neural network training process customization.
TFLearn API for TensorFlow
Implementing neural networks using TFLearn API, defining and composing models using TFLearn, deploying TensorBoard with TFLearn.
DNN: Deep Neural Networks
Mapping the human mind with Deep Neural Networks, the various building blocks of Artificial Neural Networks, the architecture of DNN, its building blocks, the concept of reinforcement learning in DNN, the various parameters, layers, activation functions and optimization algorithms in DNN.
CNN: Convolutional Neural Networks
What is a Convolutional Neural Network, understanding the architecture of CNN, use cases of CNN, what is a pooling layer, how to visualize using CNN, how to fine-tune a Convolutional Neural Network, what is Transfer Learning and understanding Recurrent Neural Networks, feature maps, Kernel filter, pooling, deploying convolutional neural network in TensorFlow
RNN: Recurrent Neural Networks
Intro to RNN Model, Application use cases of RNN, Modelling sequences, Training RNNs with Backpropagation, Long Short-Term memory (LSTM), Recursive Neural Tensor Network Theory, Recurrent Neural Network Model, basic RNN cell, unfolded RNN, training of RNN, dynamic RNN, time-series predictions.
GPU in Deep Learning
Introduction to GPUs and how they differ from CPUs, the importance of GPUs in training Deep Learning Networks, the forward pass and backward pass training technique, the GPU constituent with simpler core and concurrent hardware.
Autoencoders & Restricted Boltzmann Machine (RBM)
Introduction to RBM and autoencoders, deploying it for deep neural networks, collaborative filtering using RBM, features of autoencoders, applications of autoencoders.
Automated conversation bots using one of the descriptive techniques
Open-Close Domain Bots
Sequence to Sequence model (LSTM).