Artificial Intelligence

Artificial Intelligence Overview
DIIT Educom offers a comprehensive Artificial Intelligence program that will help you work on today cutting-edge technology Artificial Intelligence (AI). As part of this best AI training, you will master various aspects of artificial neural networks, supervised and unsupervised learning, logistic regression with a neural network mindset, binary classification, vectorization, Python for scripting Machine Learning applications, and much more

Field of machine learning, its impact on the field of artificial intelligence
The benefits of machine learning w.r.t. Traditional methodologies
Deep learning introduction and how it is different from all other machine learning methods
Classification and regression in supervised learning
Clustering and association in unsupervised learning, algorithms that are used in these categories
Introduction to ai and neural networks
Machine learning concepts
Supervised learning with neural networks
Fundamentals of statistics, hypothesis testing, probability distributions, and hidden markov models

Module 02 - Multi-layered Neural Networks

Multi-layer network introduction, regularization, deep neural networks
Multi-layer perceptron
Overfitting and capacity
Neural network hyperparameters, logic gates
Different activation functions used in neural networks, including relu, softmax, sigmoid and hyperbolic functions
Back propagation, forward propagation, convergence, hyperparameters, and overfitting.

Various methods that are used to train artificial neural networks
Perceptron learning rule, gradient descent rule, tuning the learning rate, regularization techniques, optimization techniques
Stochastic process, vanishing gradients, transfer learning, regression techniques,
Lasso l1 and ridge l2, unsupervised pre-training, xavier initialization

Module - 04 Deep Learning Libraries

Understanding how deep learning works
Activation functions, illustrating perceptron, perceptron training
multi-layer perceptron, key parameters of perceptron;
Tensorflow introduction and its open-source software library that is used to design, create and train
Deep learning models followed by google’s tensor processing unit (tpu) programmable ai
Python libraries in tensorflow, code basics, variables, constants, placeholders
Graph visualization, use-case implementation, keras, and more.

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, and neural network training process customization.

Module 06 - TFLearn API for TensorFlow

Module 07 - Dnns (deep neural networks)

Mapping the human mind with deep neural networks (dnns)
Several building blocks of artificial neural networks (anns)
The architecture of dnn and its building blocks
Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.

Module 08 - Cnns (convolutional neural networks)

What is a convolutional neural network?
Understanding the architecture and 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
Understanding recurrent neural networks, kernel filter, feature maps,and pooling, and deploying convolutional neural networks in tensorflow.

Module 09 - Rnns (recurrent neural networks)

Introduction to the rnn model
Use cases of rnn, modeling sequences
Rnns with back propagation
Long short-term memory (lstm)
Recursive neural tensor network theory, the basic rnn cell, unfolded rnn, dynamic rnn
Time-series predictions.

Module 10 - Gpu in deep learning

nce of gpus Gpu’s introduction, ‘how are they different from cpus?,’ the significa
Deep learning networks, forward pass and backward pass training techniques
Gpu constituent with simpler core and concurrent hardware.

Artificial Intelligence Assignments and Projects