Deep Learning With Python | Deep Learning And Neural Networks | Deep Learning Tutorial | Simplilear

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This video on Deep Learning with Python will help you understand what is deep learning, applications of deep learning, what is a neural network, biological versus artificial neural networks, introduction to TensorFlow, activation function, cost function, how neural networks work, and what gradient descent is. Deep learning is a technology that is used to achieve machine learning through neural networks. We will also look into how neural networks can help achieve the capability of a machine to mimic human behavior. We'll also implement a neural network manually. Finally, we'll code a neural network in Python using TensorFlow.

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Below topics are explained in this Deep Learning with Python tutorial:
1. What is Deep Learning (01:56)
2. Biological versus Artificial Intelligence (02:45)
3. What is a Neural Network (04:09)
4. Activation function (08:49)
5. Cost function (14:08)
6. How do Neural Networks work (16:05)
7. How do Neural Networks learn (18:58)
8. Implementing the Neural Network (20:26)
9. Gradient descent (23:21)
10. Deep Learning platforms (24:48)
11. Introduction to TensoFlow (26:00)
12. Implementation in TensorFlow (28:56)

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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.

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
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence

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