Artificial Intelligence

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