Convolutional Neural Network (CNN) | Convolutional Neural Networks With TensorFlow | Edureka




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The code referenced in this video is from https://YouTube.com/Sentdex and https://pythonprogramming.net/convolutional-neural-network-kats-vs-dogs-machine-learning-tutorial/
( TensorFlow Training – https://www.edureka.co/ai-deep-learning-with-tensorflow )
This Edureka “Convolutional Neural Network Tutorial” video (Blog: https://goo.gl/4zxMfU) will help you in understanding what is Convolutional Neural Network and how it works. It also includes a use-case, in which we will be creating a classifier using TensorFlow.

Below are the topics covered in this tutorial:

1. How a Computer Reads an Image?
2. Why can’t we use Fully Connected Networks for Image Recognition?
3. What is Convolutional Neural Network?
4. How Convolutional Neural Networks Work?
5. Use-Case (dog and cat classifier)

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

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(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.

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Comment List

  • edureka!
    December 9, 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!
    December 9, 2020

    Great video ,
    Please share/email me code and dataset used .
    Email:. mayankmohan6726@gmail.com

  • edureka!
    December 9, 2020

    One of the most precise and satisfying videos on CNN on this platform. Great work!
    Kindly email me the code at: akshat.agg1506@gmail.com

  • edureka!
    December 9, 2020

    I am satisfied with your video and it's pretty good. Through this video, I have gained knowledge on CNN. Can you Please share the Source Code it will help me. Here I am attaching my Mail ID- rajesh.chandaluri143@gmail.com

  • edureka!
    December 9, 2020

    best video available

  • edureka!
    December 9, 2020

    great explanation, can u please send me the code on this email:hedzz79@gmail.com
    thanks in advance,

  • edureka!
    December 9, 2020

    Thank your for your explanation for this! Could you send me code and datasets to my email so i can practice with it? Thank you!
    (muhabishar@yahoo.co.id)

  • edureka!
    December 9, 2020

    Thankyou

  • edureka!
    December 9, 2020

    It is a very good explanation. Please send me the code and data set. Thank You

  • edureka!
    December 9, 2020

    Edureka..U deserve to be a unicorn in tech studies .thankyou ♥♥♥

  • edureka!
    December 9, 2020

    Can i please have the source code?

  • edureka!
    December 9, 2020

    Sir can I get the coding?

  • edureka!
    December 9, 2020

    Please send me code 🙏

  • edureka!
    December 9, 2020

    I need the code please can you give me ?

  • edureka!
    December 9, 2020

    Hello Team.. I really wanted to understand CNN for image colorization quickly and this video is too good for that. I got the concepts just by viewing this video once which is just 22 minutes long.
    Would it be possible for you to please send the code file and datasets for this so that I can also have some hands-on experience on the same.

  • edureka!
    December 9, 2020

    Thank you so much, It was really helpful!
    Can you please send me the code ?

  • edureka!
    December 9, 2020

    Super Explanation…. FIVE STARS. Can we get the code Please !

  • edureka!
    December 9, 2020

    can we get the code

  • edureka!
    December 9, 2020

    It was Aswome vedio which clearly explained about cnn..
    Could u pls send the code?

  • edureka!
    December 9, 2020

    Thank you so much for such an amazing tutorial.

  • edureka!
    December 9, 2020

    Thankyou very much!

  • edureka!
    December 9, 2020

    Nicely explained and easy to understand

  • edureka!
    December 9, 2020

    its awesome. thank you edureka for this. i need a source code for this dcnn. could you please help me

  • edureka!
    December 9, 2020

    nice tutorial … well explained.

  • edureka!
    December 9, 2020

    Very nice Anna.GODS AND GODDESS BLESS YOU A LOT.

  • edureka!
    December 9, 2020

    Very easy to understand! Thanks for your great work.

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