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دسته بندی
دسته بندی

Training Neural Networks in Python

24,900 تومان
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ویدئو معرفی این محصول

Having a variety of great tools at your disposal isn’t helpful if you don’t know which one you really need, what each tool is useful for, and how they all work. In this course learn the inner workings of neural networks, so that you're able to work more effectively with machine learning tools. Instructor Eduardo Corpeño helps you learn by example by providing a series of exercises in Python to help you to grasp what’s going on inside. Discover how to relate parts of a biological neuron to Python elements, which allows you to make a model of the brain. Then, learn how to build and train a network, as well as create a neural network that recognizes numbers coming from a seven-segment display. Even though you'll probably work with neural networks from a software suite rather than by writing your own code, the knowledge you’ll acquire in this course can help you choose the right neural network architecture and training method for each problem you face.

This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the “Using GitHub Codespaces with this course” video to learn how to get started.


01 - Introduction
  • 01 - Creating a neural network in Python
  • 02 - What you should know
  • 03 - Using GitHub Codespaces with this course

  • 02 - 1. Choosing a Neural Network
  • 01 - What is a neural network
  • 02 - Why Python
  • 03 - The many applications of machine learning
  • 04 - Types of classifiers
  • 05 - Types of neural networks
  • 06 - Multilayer perceptrons

  • 03 - 2. The Building Blocks of Neural Networks
  • 01 - Neurons and the brain
  • 02 - A simple model of a neuron
  • 03 - Activation functions
  • 04 - Perceptrons A better model of a neuron
  • 05 - Challenge Finish the perceptron
  • 06 - Solution Finish the perceptron
  • 07 - Logic gates
  • 08 - Challenge Logic gates with perceptrons
  • 09 - Solution Logic gates with perceptrons

  • 04 - 3. Building Your Network
  • 01 - Linear separability
  • 02 - Writing the multilayer perceptron class
  • 03 - Challenge Finish the multilayer perceptron class
  • 04 - Solution Finish the multilayer perceptron class

  • 05 - 4. Training Your Network
  • 01 - The need for training
  • 02 - The training process
  • 03 - The error function
  • 04 - Gradient descent
  • 05 - The Delta rule
  • 06 - The Backpropagation algorithm
  • 07 - Challenge Write your own Backpropagation method
  • 08 - Solution Write your own Backpropagation method

  • 06 - 5. Let's Make a Segment Display Classifier
  • 01 - Segment display recognition
  • 02 - Challenge Design your own SDR neural network
  • 03 - Solution Design your own SDR neural network
  • 04 - Challenge Train your own SDR neural network
  • 05 - Solution Train your own SDR neural network
  • 06 - 7 to 1 network GUI demo
  • 07 - 7 to 10 network GUI demo
  • 08 - 7 to 7 network GUI demo

  • 07 - Conclusion
  • 01 - Next steps