What you would learn in How to Build Neural Networks in Python course?
This course was developed with years of research to help students understand how to create and build deep neural networks without prior knowledge. Many learners do not have the luxury of 8-9 hours to sit at the computer to learn the fundamentals. Sometimes, we must be able to learn as quickly as we can, and this is precisely what this course will teach you.
The course was designed to ensure that it is designed to train students to be confident in the creation of the neural network by using TensorFlow and Python. The course begins by providing a complete definition of Neural networks, and it then guides students through the creation and evolution of Neural networks. It will then explain the neural network's essential working principles. The course guides students in setting up their work environment in the following lesson. In the final lesson, this course will teach all the information needed by students to know to construct and build a neural network confidently.
This course is based on the following format:
1. The definition of Neural Network
2. Origin and Development of Neural Network
3. What is Neural Network? Neural Network Works
4. Set up the Working Environment
5. Making the Neural Network
The five tips listed here will help anyone feel comfortable building and training neural networks with TensorFlow using Python.
Content of the Course:
- The students will discover the neural basis of a network
- They will also learn about the machine learning algorithm in the code implemented in Google Colab
- Students will be able to construct and develop deep neural networks with TensforFlow in Python
- The students can evaluate the performance of the network visually
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