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This complete course video on neural networks 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 activation functions are, and how a neuron fires. You will get an idea of backpropagation and gradient descent algorithms. You will learn about the convolutional neural network and how it identifies objects in an image. Finally, you will understand recurrent neural networks and lstm in detail. Now let's start with learning neural networks.
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Link to dataset – https://drive.google.com/drive/folders/11T76B8UkTg9lU-sPhPlWqn6MOVhQ-FjS
Below topics are explained in this comprehensive course on Neural Networks:
1. Animated video 00:52
2. What is a neural network 06:35
3. What is Deep Learning 07:40
4. What is an artificial neural network 09:00
5. How neural network works 10:37
6. Advantages of Neural Network 13:39
7. Neural Network Applications 14:59
8. The future of neural network 17:03
9. How Neural Network Works 19:10
10. Types of artificial neural networks 29:27
11. Use case statement-problem 34:57
12. Use implementation case 36:17
13. Backpropagation and gradient descent 01:06:00
14. Loss function 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 a convolutional neural network 01:20:51
21. How CNN recognizes 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 a recurrent neural network 02:24:19
27. Applications of Recurrent Neural Network 02:25:32
28. how does an RNN work 02:28:42
29. Disappearing and exploding gradient problem 02:31:02
30. Long-term and short-term memory 02:35:54
31. implementation of LSTM use case 02:44:32
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Simplilearn's Deep Learning course will turn you into an expert in Deep Learning techniques using TensorFlow, the open source software library designed for conducting machine learning and deep neural network research. With our Deep Learning course, you will master the concepts of Deep Learning and TensorFlow, learn how to implement algorithms, build artificial neural networks, and navigate layers of data abstraction to understand the power of data and prepare to your new role as a Deep Learning scientist.
You can gain in-depth knowledge of Deep Learning by following our Deep Learning certification training. With Simplilearn's Deep Learning course, you'll prepare for a career as a Deep Learning engineer by mastering concepts and techniques, including supervised and unsupervised learning, mathematics and heuristics, and practical modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and execution pipeline
2. Implement Deep Learning algorithms, understand neural networks, and navigate layers of data abstraction that will allow you to understand data like never before.
3. Master and understand advanced topics such as convolutional neural networks, recurrent neural networks, deep network training and high-level interfaces
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