Autoencoders Tutorial | Autoencoders In Deep Learning | Tensorflow Training | Edureka




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** AI & Deep Learning with Tensorflow Training: www.edureka.co/ai-deep-learning-with-tensorflow **
This Edureka video of “Autoencoders Tutorial” provides you with a brief introduction about autoencoders and how they compress unsupervised data. You will get detailed information on the different types of Autoencoders with the code for each type. You will see the various applications and types of autoencoders used in deep learning for dimentionality reduction.

This video covers the following topics:
1. Why do we need Autoencoders?
2. What are Autoencoders?
3. Properties of Autoencoders
4. Autoencoders Training & Architecture
5. Types of Autoencoders
6. Applications of Autoencoders

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

#Autoencoder #Tensorflow #DeepLearning #NeuralNetworks #python #MachineLearning #DimensionalityReduction

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Comment List

  • edureka!
    December 14, 2020

    Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.
    For Tensorflow Training and Certification, Call us at US: +18336900808 (Toll Free) or India: +918861301699
    Or, write back to us at sales@edureka.co

  • edureka!
    December 14, 2020

    awesome tutorial I have watched many videos and read the article but I didn't have a clear understanding, after watching this now I definitely have deep intuition on autoencoder. keep making such good tutorial …

  • edureka!
    December 14, 2020

    Really Loved the explanation provided ….!!!
    Thank you Edureka

  • edureka!
    December 14, 2020

    thank you for another beautiful session .

  • edureka!
    December 14, 2020

    how we can use autoencoder for text summarization?

  • edureka!
    December 14, 2020

    very nice!!

  • edureka!
    December 14, 2020

    Please can i get the code used in the demo?

  • edureka!
    December 14, 2020

    Also which autoencoder was used in the demo code of the video…

  • edureka!
    December 14, 2020

    Can you help me in getting a spectral dataset to use it for autoencoders and other non linear methods ,,,,, Also if possible some papers for vanilla autoencoders.. Also i have a question like PCA method can be used for spectral datasets as well ?

  • edureka!
    December 14, 2020

    Can I get the code for this?
    My email ID is wadood3003@gmail.com

  • edureka!
    December 14, 2020

    thanks for this nice video

  • edureka!
    December 14, 2020

    Can i get the source code for this .

  • edureka!
    December 14, 2020

    Hi,well explained…Just have an query …Can we use autoencoders for building recommendation system ? What will be output of it ..Like i have studies dat we can use it for dimentionality reduction ..But i am not able to understand the output of it ..Like we will apply movie dataset of 10k to autoencoder..Wat output it will produce ….Thnx in advance ..Plz help its urgent

  • edureka!
    December 14, 2020

    Could you send the sample code for autoencoder for each type.

  • edureka!
    December 14, 2020

    please upload the code also

  • edureka!
    December 14, 2020

    Hi ! Great video! I had a doubt in understanding the working of the regularizer term in the loss function of autoencoder neural networks. Regularization in general means to penalise the loss function so that the network does not over fit the training set and generalises well to unseen data.
    What does the term "call back labeller divergence" mean here? And please explain the meaning of the sentence : "If regularizer term was not included, encoder could learn to cheat map each data point a representation in a different region of Euclidean space" ??

  • edureka!
    December 14, 2020

    Can you plz share the code?

  • edureka!
    December 14, 2020

    Hey, great explanation!

    If only I could run the code for myself and see its working..

    Can you share the source code?

  • edureka!
    December 14, 2020

    can autoencoders use for signal processing?

  • edureka!
    December 14, 2020

    can you provide the code for this?

  • edureka!
    December 14, 2020

    Very good explanation, but I wanted to know 1) how an autoencoder can be used for network anomaly detection and 2)how to combine LSTM cell within autoencoders.
    Thanks.

  • edureka!
    December 14, 2020

    how does dimensionality reduction take place through encoder?

  • edureka!
    December 14, 2020

    Nice video for beginners. I am new to the topic and want to understand how can convolutional autoencoders generate high-resolution image because the outputs of autoencoders are usually lossy/compressed. Thanks.

  • edureka!
    December 14, 2020

    Everywhere in auto encoders the input and output are same.But in the IEEE transaction paper which I am working the input and output of stack auto encoder are different and it is said in paper that they are using SAE for mapping input and output.can you explain how to SAE can be used for mapping

  • edureka!
    December 14, 2020

    Good Explanation. Quick question on sparse autoencoders. Is the KL divergence penalization similar to an L2 regularization?

  • edureka!
    December 14, 2020

    great explain!

  • edureka!
    December 14, 2020

    Sir please upload tutorial about text to speech converter app with own voice practically…

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