Deep Learning Tutorial | Deep Learning TensorFlow | Deep Learning With Neural Networks | Simplilearn

This Deep Learning tutorial will help you in understanding what is Deep Learning, why do we need Deep learning, what is neural network, applications of Deep Learning, what is perceptron, implementing logic gates using perceptron, types of neural networks. At the end of the video, you will get introduced to TensorFlow along with a usecase implementation on recognizing hand-written digits. Deep Learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. Deep Learning, on the other hand, uses advanced computing power and special type of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. We will also understand neural networks and how they work in this Deep Learning tutorial video.

Below topics are explained in this Deep Learning Tutorial:
1. What is Deep Learning? ( 03:43 )
2. Why do we need Deep Learning? ( 04:50 )
3. What is Neural network? ( 05:46 )
4. What is Perceptron? ( 07:37 )
5. Implementing logic gates using Perceptron ( 11:58 )
6. Types of Neural networks ( 17:18 )
7. Applications of Deep Learning ( 18:09 )
8. Working of Neural network ( 21:02 )
9. Introduction to TensorFlow ( 34:29 )
10. Use case implementation using TensorFlow ( 39:03 )

#DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse

Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research.

With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:

1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces

We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning

[SimplilearnOfficial]

Machine Learning is the Future and yours can begin today. Comment below with you email to get our latest Machine Learning Career Guide. Let your journey begin

[asmajabeen]

great deep learning tutorial i need its notes for delivered to other students

[CreamyIceluver]

Great video, but several of the TF modules are now deprecated and not possible to code along at home. Would love to see updated version of this tutorial

[SimplilearnOfficial]

Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you.
Thanks for watching the video. Cheers!

[ranasadeghichegani]

Hi, thank you for your tutorial. In the Demo part, I did not understant the cross Entropy section. Can you provide more detail on the function and logist variable.
and for the y variable, if there are lest say 10 layers, I should create the graph from all 10 to make t right?
and what does learning_Rate = 0.5 mean? how do you select it? why it is not 0.6 or 0.7?

[beautifulanimals]

sir can you make videos on these topics with practical ???
Building pipelines for deep learning that can handle significant data
Executing experiments for hypothesis testing
Constantly optimizing

[blakef.]

29:58 shouldn't C=(1/2)(Yhat - Y)^2 instead of C=(1/2)(Y-Y)^2?

[swmf]

can i apply this to predict the customer churn please i need answer... it is an awesome why of explanation thank you so much

[rajasthawal]

Mohan sir, grate work, beautifull explantion on deep learning and its implementation using tensorflow. Being a newbie I find your vedios tutorial to the best so far sir. Your method of explation simplifed a lots of concepts and implementation which previously I found to be confusing. This vedio has boosted my moral, provided valuable insight and the right path to boost of my project sir.Thanking you and Simplilearn

[AkashVerma-mgys]

How we can predict the digit given by the user, I mean to say that what is the code for predicting any random digit?

[amkhatri]

First of was very good, but the second half where the code and demo part started, there was too much to capture. I think too much is covered in too little time. Can you provide some resources to learn all the functions and tensorflow coding in a step by step way.

[nacerzarguit]

Great tutorial. But unfortunately I found that your courses are very expensive. Students can not pay 400 dollars for one course.

[gagananand]

Thanks for excellent tutorial .please share training data set with me .

[vidusha]

Hi, great video. Thanks a lot.
But how do you use an handwritten image of yours to check whether the ai works?

[vinayraog]

Great tutorial for the beginners on neural networks

[marksachin]

Content is informational.. But i am sorry to say that your voice is not good for listening..your voice should be clear and crisp so that we can grasp the points easily..