Introduction To Artificial Neural Network And Deep Learning

Course

INTRODUCTION TO ARTIFICIAL NEURAL NETWORK AND DEEP LEARNING

Category

Data Science and Neural Networks Online Institute

Eligibility

All Job Seekers

Mode

Both Classroom and Online Classes

Batches

Week Days and Week Ends

Duration :

2 Months

Data Science and Neural Networks Objectives

•How to work with Data Science and Neural Networks Tool.
•See how to build a Data Science and Neural Networks code.
•You will know how to work with Data Science and Neural Networks.
•A introductory understanding of how to program in Data Science and Neural Networks.
•From A-Z: The Complete Beginners-Advanced Masterclass – Learn Data Science and Neural Networks
•Learn Data Science and Neural Networks at your own pace with quality learning videos.
•Learn all the hooks and crooks of Data Science and Neural Networks at your pace.
•Go through all the steps to designing a game from start to finish.Learn and understand the fundamentals of Data Science and Neural Networks and how to apply it to web development.

introduction to artificial neural network and deep learning Training Highlights

•Advanced Topics covered with examples
•We  Groom up your documents and profiles
•Real time live project training and Guidance
•Online Training with 100% placement assistance
•We provide Classroom and Online training in Metro Cities
•Courseware that is curated to meet the global requirements
•Flexible group timings to admit freshers, students, and employed professionals
•Lifetime access to our 24×7 online support team who will resolve all your technical queries, through ticket based tracking system.

Who are eligible for Data Science and Neural Networks

•CNC Engineer, Software Developer, Testing Engineer, Implementation, Core Java, Struts, hibernate, Asp.net, c#, SQL Server, CNC Programming, backembedded platform software engineers, embedded multimedia developer, Middleware Developers, Android Middleware, device driver developers, c, c++, linux
•Java Developer, Salesforce Developer, Solution Consulting, Qa Testing, Finance Executive, Full Stack Developer, Email Campaign, React.js, Ui Development
•Protocol Testing, Php Developer, Oracle, Senior Managers, Oracle DBA, Dotnet, Java, oracle, DBA, Database Administration, 12c, RAC, Goldengate
•ux, ui, Python Developers, Qa Automation, sales, Ui Development, Ux Design, Software Development, Python, Qa Testing, Automation Testing

INTRODUCTION TO ARTIFICIAL NEURAL NETWORK AND DEEP LEARNING Syllabus

Preliminaries and Essential Definitions in Artificial Neural Networks
•Introduction: Is it a boy or girl?
•Let’s be more mathematical
•A model of an artificial neuron
•An Artificial Neuron (Perceptron)
•An artificial neuron in action: live example
•Terminologies in the field of Machine Learning and Neural Networks
•A mathematical model of perceptron for problems with more than two features
•Time to learn the inspiration of perceptron and Neural Networks
•Learning: How to train a Perceptron
•Learning and training in Neural Networks: minimizing a cost (error) function
•Different cost/error functions
•How to find the minimum of a cost function? Yes this is learning!
•Gradient Descent algorithm: Numerical example for optimizing weights
•Gradient Descent algorithm: Numerical example for optimizing biases
•The impact of the learning rate
•Challenges in training/learning Neural Networks
•Coding a simple Perceptron in Java
•A Perceptron Network, Deep Neural Networks, and deep learning
•Different activation functions
•Multiple neurons: a perceptron network
•MLP: A Multi-layer Perceptron
•MLP in Action: A Live Demo
•Deep Neural Networks and Deep Learning
•BP: Backpropagation Algorithm
•The theory of the Backpropagation Algorithm
•The impact of the momentum1
•Regression using Neural Networks
•Linear and logistic (non-linear) regression using MLPs
•Regression in action: live demo
•Regression examples and issues
•Multiple regression
•Multiple regression in action: live demo
•MLP as a universal approximator
•Neuroph
•Introduction to Neuroph
•Let’s create an artificial neuron in neuroph
•Creating an MLP in Neuroph
•A sample project in Neuroph
•Visualizations in Neuroph
•Hand-written character recognition in Neuroph
•Image recognition in Neuroph
•Free e-book
•My book on NNs