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

Course Content
Module 01 - Introduction to Deep Learning and Neural Networks

  • 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.

Module 03 - Artificial Neural Networks and Various Methods

  •  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.

Module 05 - 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, and neural network training process customization.

Module 06 - TFLearn API for TensorFlow

  • Using tflearn api to implement neural networks
  •  Defining and composing models, and deploying tensorboard

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