Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka




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( TensorFlow Training – https://www.edureka.co/ai-deep-learning-with-tensorflow )
This Edureka “Neural Network Tutorial” video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.

Below are the topics covered in this tutorial:

1. Why Neural Networks?
2. Motivation Behind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks

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Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE

PG in Artificial Intelligence and Machine Learning with NIT Warangal : https://www.edureka.co/post-graduate/machine-learning-and-ai

Post Graduate Certification in Data Science with IIT Guwahati – https://www.edureka.co/post-graduate/data-science-program
(450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies)

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How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

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About the Course
Edureka’s Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

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Who should go for this course?

The following professionals can go for this course:

1. Developers aspiring to be a ‘Data Scientist’

2. Analytics Managers who are leading a team of analysts

3. Business Analysts who want to understand Deep Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. Professionals who want to captivate and analyze Big Data

6. Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.

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Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

For more information, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free).

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Comment List

  • edureka!
    November 18, 2020

    Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka AI & Deep Learning Course curriculum, Visit our Website: http://bit.ly/2r6pJuI

  • edureka!
    November 18, 2020

    Awesome explation😍😍

  • edureka!
    November 18, 2020

    It is one of the best videos on ANN, will clear all our doubts and give a clear understanding.

  • edureka!
    November 18, 2020

    nice presentation

  • edureka!
    November 18, 2020

    I enjoyed the session 😍😍😍😍
    Thanks a lot

  • edureka!
    November 18, 2020

    can i get the dataset and code for this

  • edureka!
    November 18, 2020

    Thanks……..great

  • edureka!
    November 18, 2020

    Can you give an example for classification problem

  • edureka!
    November 18, 2020

    on which data losses are calculated because the model has seen train_data and train_labels

  • edureka!
    November 18, 2020

    Great Presentation 👍
    Neural networks are amazing!! I want to explore and know about it more

  • edureka!
    November 18, 2020

    thank you so much for better teaching
    i loved to watch this tutorial

  • edureka!
    November 18, 2020

    crystal clear explanation Saurav. Thanks for the video. can i get the code??

  • edureka!
    November 18, 2020

    Can i get the code and dataset

  • edureka!
    November 18, 2020

    can i please have the dataset you are using

  • edureka!
    November 18, 2020

    hello, this is great! Good job, please can I have the code and dataset? thank you

  • edureka!
    November 18, 2020

    Very nice explaination!
    Can you share dataset and code ?

  • edureka!
    November 18, 2020

    what a lecture!! awesome

  • edureka!
    November 18, 2020

    Can you Share the Code and that input file also please!!

  • edureka!
    November 18, 2020

    can i get the code??

  • edureka!
    November 18, 2020

    What if i have multiple labels ??

  • edureka!
    November 18, 2020

    I think there is no need to encode the dependent variable y,, ??????

  • edureka!
    November 18, 2020

    what is encoding the dependent variable mean
    ??

  • edureka!
    November 18, 2020

    Finally I understand the weight 😭thank you man , you are incredible

  • edureka!
    November 18, 2020

    Thank you very much for the session 😊

  • edureka!
    November 18, 2020

    great tutor

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