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
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