Artificial Intelligence Course in Hyderabad

Learning Objective: Deep learning is a subfield of Machine learning and works based on artificial neural networks. In this module, you will learn all the fundamental concepts of deep learning.

Topics:

• • Introduction to machine learning
  • Advantages of Machine Learning
  • Introduction to Deep learning
  • Deep learning vs other machine learning methods
  • Regression and Classification in supervised learning
  • Introduction to association and clustering in unsupervised learning
  • Introduction to Neural networks and their role in AI
  • Machine learning concepts
  • Introduction to Supervised learning
  • Supervised learning with neural networks
  • Fundamentals of statistics, and probability distributions
  • Introduction to hypothesis testing and hidden Markov models.

Learning Outcome: Upon the completion of this module you will have gained a clear overview of machine learning, neural networks applications of Neural Networks and much more.

Learning Objective: Multi-layered Neural Networks defined as networks that contain more than one layer of networks or nodes. Majority of the networks used today are the multi-layered ones.

Topics:

• • Overview of Multi-layer network
  • Introduction to deep neural networks and regularization
  • Multi-layer perceptron
  • Capacity and Overfitting
  • Activation functions used in Neural Networks
  • Neural network logic gates and  hyperparameters
  • Introduction to forwarding propagation and backpropagation

Learning Outcome: By the completion of this module you will gain in-depth knowledge of Multilayered Neural Networks and different concepts in it.

Learning Objective: An artificial neural network (ANN) is a specially developed computing system designed to analyze and process information similar to the human brain.

Topics:

• • Methods used for training artificial neural networks
  • Derailed overview of methods such as  Perceptron learning rule,  tuning the learning rate, gradient descent rule, optimization techniques
  • transfer learning, vanishing gradients,  Stochastic process, and  regression techniques
  • Overview of Lasso l1 and ridge l2, Xavier initialization and unsupervised pre-training

Learning Outcome: By the end of this module you will have gained practical experience in implementing various Artificial Neural Network Methodologies.

Learning Objective: Deep Learning libraries consist of readily written, high-quality code that can be used in any project.

Topics:

• • A brief overview of Deep Learning
  • Understand how deep learning works
  • Illustrating perceptron, activation functions, perceptron training
  • Introduction to multi-layer perceptron, and key components in it. 
  • TensorFlow and its library
  • Open-source software library used to design, create and train
  • Deep Learning models
  • Google’s tensor processing unit (tpu) programmable ai
  • Libraries available in Python variables, constants, code basics, placeholders
  • Graph visualization, Keras, and more.

Learning Outcome: By the end of this chapter you will have gained practical knowledge on how Depp learning methods work in real-time and various models available in it.

Learning Objective: Keras is one of the popular and high-level neural network APIs.  Keras is written in using Python and supports various neural network computation engines.

Topics:

• • Introduction to Keras Neural Networks
  • Keras on-top TensorFlow
  • Composing models using Keras 
  • Defining complex multi-output models
  • Functional and sequential composition, batch normalization
  • Customization of Neural network training process
  • Deploying Keras with Tensorboard

Learning outcome: Upon the completion of this chapter you will gain in-depth knowledge of Keras API and the process to use it on top of TensorFlow.

Learning Objective: TFLearn is a transparent and modular learning aspect that is being used in TensorFlow framework. TFLearn provides a higher-level API to TensorFlow which enables in conducting new experiments.

Topics:

• • Implementing Neural networks using TFLearn API
  • Deploying Tensorboard

Learning Outcome: You will gain hands-on experience in using TFlearn on top of TensorFlow.

Learning objective: The deep neural networks can be trained from end-to-end. Deep Neural Networks can learn on their own based on the given training data.

Topics:

• • Comparison between the human mind and Deep Neural Networks
  • Building blocks of Artificial Neural Networks (ANS)
  • DNN architecture and its building blocks
  • Reinforcement learning in DNN

Learning Outcome: By the end of this chapter you will have learnt all the modules associated with Deep Neural Networks.

Learning Objective: It is a Deep Learning algorithm that is capable of processing images and differentiate images from one another.

Topics:

• • Introduction to Conventional Neural Network
  • CNN Architecture
  • Overview of pooling Layer
  • Making customizations to CNN
  • What is Transfer learning?
  • Introduction to recurrent neural networks, feature maps, kernel filter, using convolutional neural networks in TensorFlow.

Learning Outcome: By the end of this chapter you will have gained hands-on experience in using Conventional Neural Networks and transfer learning.

Learning Objective: Recurrent Neural Networks (RNN)  is widely used for modelling sequence data. RNN functions similar to the human brain. Following are the concepts that you are going to master in this Recurrent Neural Networks (RNN) module.

Topics:

• • Overview of RNN model
  • Modelling Sequences in RNN and use cases
  • Backpropagation using Rnns 
  • Long short-term memory (lstm)
  • Time-series predictions. 
  • Recursive neural tensor network theory

Learning Outcome: By the completion of this module, you will have gained hands-on experience in modelling sequences using RNN, backpropagation using Rnns, prediction of time-series, etc.

Learning Objective: A GPU is a microprocessor which is capable of running various computations simultaneously and minimizes the time taken to train Artificial Neural Networks (ANN).

Topics:

• • Introduction to GPU’s
  • Comparison between GPUs and CPUs
  • Techniques to be used in forwarding pass and backward pass
  • Gpu constituent with concurrent hardware and simpler core.

Learning Outcome: Upon the completion of this chapter you will have gained practical knowledge in Handling multiple tasks associated with GPU.

Learning Objective:  A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network which has got the ability to learn probability distribution on the given input.

Topics:

• • Introduction to RBM
  • using RBM for collaborative filtering
  • Introduction to autoencoders
  • Applications of autoencoders

Learning Outcome: By the end of this chapter you will gain hands-on exposure in using RBM for Collaborative filtering and gain knowledge of applications of autoencoders.

Learning Objective: Deep learning has become an integral part of every business organization and offers a wide range of solutions to complex issues. In this module, you will learn the various applications of Deep Learning and their implementation process.

Topics:

• • Image processing
  • Speech recognition
  • video analytics.

Learning Outcome: By the end of this module you will have gained the complete knowledge of various applications of AI.

Learning Objective: A Chatbot is a popular software application that is being used for online chat conversation and provides the required information to the users.

Topics:

• • IBM Watson
  • Open–closed domain bots
  • Microsoft’s Luis
  • Sequence to sequence model
  • Generative model

Learning Outcome: By the end of this chapter you will have gained in-depth knowledge of various types of AI bots available in AI.