Neural Network Full Course | Neural Network Tutorial For Beginners | Neural Networks | Simplilearn

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.

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

#NeuralNetwork #NeuralNetworkFullCourse #NeuralNetworkTutorial #WhatIsNeuralNetwork #DeepLearning #DeepLearningTutorial #DeepLearningCourse #DeepLearningExplained #Simplilearn

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 our Deep Learning course, you'll master Deep Learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as Deep Learning scientist.

You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. 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

For more information about Simplilearn’s courses, visit:

🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - [phone]

[SimplilearnOfficial]

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
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!

[sonalmoon]

Getting deep idea about deep learning and ANN.
Thanks to you simplilearn!

[ashmakader]

13. Backpropagation & Gradient Descent 01:06:00
14. Loss Function 01:10:26
15. Gradient Descent 01:11:26
16. Backpropagation 01:13:07

[natashasamuel]

Great class.
Keep up the good work.

Thank You,
Natasha Samuel

[nabeelhasan]

thanks for such an amazing new year gift

[landryplacid]

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

[vitthaldharne]

Such a clean content. Kudos to you guys♥️

[abikkrishnan]

Haven't completed yet.. but really impressive so far.. will update after watching complete video..

[ifmondayhadaface]

3:00:57, I though dropout is when 30% if neurons get randomly dropped as a regularization technique?

[arthurkandakai]

Thank for the lecture it was wunnerful

[akshayakumars]

This is the simplest explanation, i had heard before :-)

[diptadutta]

Your video quality is so good. I expect better sound quality while describing a lesson! This will be great if the sound quality is better. Thank you for the awesome courses.

[seeking]

Can a smart person answer me on question? Is there a tutorial of making a NN from scatch without using any librarys? I know this is much more work and maybe not so efficient, but I just want to learn every part of deep learning by doing and I also want my code not to be dependent from other code, which may be deleted in future so all my work won't work anymore or other versions are not working with my code or something like that. I want my code just to be independend from any librarys.

[ChaosSlave]

I was very unclear after watching the first part of the video. You point the code at 2 data sets, a learning data set and a testing dataset. How do you specify in these sets what images in the set are cats, and what images in the set are dogs?

[jinalpatel]

one question, I have noticed while learning from this session, line "In [10]" should be as that line is preparing test set data ?

[justinking]

Great video. But sound quality is very poor. In upcoming videos if you improve sound quality. It will be very useful for us. 👍

[kashyapjyotigohain]

Thanks a lot for sharing the video, the way of teaching is very nice.
If possible could you send me the exercise datasets.

[neerajpal]

Hello Team, can you please explain how to create custom dataset and how to use in model train (tensorflow). Thanks in advance

[ganeshsubramanian]

Very good course. Can Richard's recording can be made sound more natural rather than a flight pilot? Please consider as it hurts ears.

[Artexpert-]

Osmmmm great suppperb informative video