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This full course video on Neural Network tutorial will help you understand what a neural network is, how it works, and what are the different types of neural networks. You will learn how each neuron processes data, what are activation functions, and how a neuron fires. You will get an idea about backpropagation and gradient descent algorithms. You will have a look at the convolution neural network and how it identifies objects in an image. Finally, you will understand about the recurrent neural networks and lstm in detail. Now, let’s get started with learning neural networks.
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Dataset Link – https://drive.google.com/drive/folders/11T76B8UkTg9lU-sPhPlWqn6MOVhQ-FjS
Below topics are explained in this Neural Network Full Course:
1. Animated Video 00:52
2. What is A Neural Network 06:35
3. What is Deep Learning 07:40
4. What is Artificial Neural Network 09:00
5. How Does Neural Network Works 10:37
6. Advantages of Neural Network 13:39
7. Applications of Neural Network 14:59
8. Future of Neural Network 17:03
9. How Does Neural Network Works 19:10
10. Types of Artificial Neural Network 29:27
11. Use Case-Problem Statement 34:57
12. Use Case-Implementation 36:17
13. Backpropagation & Gradient Descent 01:06:00
14. Loss Fubction 01:10:26
15. Gradient Descent 01:11:26
16. Backpropagation 01:13:07
17. Convolutional Neural Network 01:17:54
18. How Image recognition Works 01:17:58
19. Introduction to CNN 01:20:25
20. What is Convolutional Neural Network 01:20:51
21. How CNN recognize Images 01:25:34
22. Layers in Convolutional Neural Network 01:26:19
23. Use Case implementation using CNN 01:39:21
24. What is a Neural Network 02:21:24
25. Popular Neural Network 02:23:08
26. Why Recurrent Neural Network 02:24:19
27. Applications of Recurrent Neural Network 02:25:32
28. how does a RNN works 02:28:42
29. vanishing And Exploding Gradient Problem 02:31:02
30. Long short term Memory 02:35:54
31. use case implementation of LSTM 02:44:32
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#NeuralNetwork #NeuralNetworkFullCourse #NeuralNetworkTutorial #WhatIsNeuralNetwork #DeepLearning #DeepLearningTutorial #DeepLearningCourse #DeepLearningExplained #Simplilearn
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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
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Source
Thank you for the tutorial!! Could I please have a copy of the dataset?
Peterwarren62388@gmail.com
A great introduction to Neural Networks! Could you please send me the used data sets, I would like to try it out myself 🙂
email: Komalpreetsingh081gmail.com
Thanks!
It was very informative and awesome…Can you send me the code and datasets used..It will be very helpful for me to do my project..
Mail id : akshayakarayi5599@gmail.com
Can you send me the dataset s.ellenberger95@gmail.com
I have one doubt. How do u come to know the output shape of convolutional layer 1 and maxpooling layer 1 as 32×64? Help me out please.
can you provide the dataset and sourcecode?
Please will u send related material and datasets to my mail webdevloperphp6@gmail.com
Thank you very much for this tutorial. May i please have the datasets used in my email : bongi.edube@gmail.com, i would like to follow through the training
Can I get the data set on congzhou821@gmail.com?
can i have the datasets and code??
Good…..
This is great . Kindly send me the code at ashish2759@gmail.com
A IS NOT TRUE ACTIVATION FUNCTION ARE THRESHOLD FUNCTION
Could you please send me all those used Data set to this mail? Mail-Id: sarangandhi66@gmail.com
Your tutorials are great, really explanatory and are one of the best, but why do they generally have lots of echo. The echo reduces concentration.Thanks
This is the simplest explanation, i had heard before 🙂
Please share the datasets
Very easy to flow tutorial please share datasets
Could someone please share the sequence in which i shall begin simplelearn tutorial ,i am a beginner ,what all shall i know before coming to this video
please send the data set
Dear Richard could you send me the dataset to cm24121964@gmail.com in order to try it by myself? Thank you in advance. Im just a curious beginner in this area but this video is shinning a lot of light in to matter. thank you again.
great tutorial , could you please send me the data sets , email ,hambly13@hotmail.com
Thank you, this is amazing! 🙏
Can we apply this on matlab too?
b option
An error in slides when u were explaining rnn where you said its many to many on a slide of many to one 2.30.30
Very informative. Thank you so much for the clear content. Could you please send me the dataset to mbungeelliot@gmail.com . Thanks in Advance
Can you please send me the dataset along with the ipynb files ?
My email is naumanumer10@outlook.com
Very informative. Thank you so much for amazing content. Could you please send me the source code and the dataset to baekhyunee129@gmail.com. Thanks in Advance.
Hello Simplilearn,I find your tutorials awesome.
Can I have the datasets on email account pratikrajpoot7@gmail.com?
NICE :)))))))))))
How to use neural network for face recognition
I have a doubt, what is the difference between neural network and neural network in python?
Thank you Simplilearn!😄
Well put together course, are the slides available anywhere?
Great class.
Keep up the good work.
Thank You,
Natasha Samuel
Very good course. Can Richard's recording can be made sound more natural rather than a flight pilot? Please consider as it hurts ears.
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13. Backpropagation & Gradient Descent 01:06:00
14. Loss Function 01:10:26
15. Gradient Descent 01:11:26
16. Backpropagation 01:13:07
nice tutorial
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