در این روش نیاز به افزودن محصول به سبد خرید و تکمیل اطلاعات نیست و شما پس از وارد کردن ایمیل خود و طی کردن مراحل پرداخت لینک های دریافت محصولات را در ایمیل خود دریافت خواهید کرد.
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
02 - 1. Choosing a Neural Network
03 - 2. The Building Blocks of Neural Networks
04 - 3. Building Your Network
05 - 4. Training Your Network
06 - 5. Let's Make a Segment Display Classifier
07 - Conclusion