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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.

Free AI Course With Course Completion Certificate: https://www.simplilearn.com/learn-ai-basics-skillup?utm_campaign=AI-ob1yS9g-Zcs&utm_medium=DescriptionFirstFold&utm_source=youtube

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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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

Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=Neural-Network-Full-Course-ob1yS9g-Zcs&utm_medium=Tutorials&utm_source=youtube

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