Training Neural Networks in Python

Focused View

Eduardo Corpeño

2:04:56

126 View

01 - Introduction

01 - Creating a neural network in Python.mp4

01:04

02 - What you should know.mp4

01:25

03 - Using GitHub Codespaces with this course.mp4

06:01

02 - 1. Choosing a Neural Network

01 - What is a neural network.mp4

01:26

02 - Why Python.mp4

00:52

03 - The many applications of machine learning.mp4

04:08

04 - Types of classifiers.mp4

04:19

05 - Types of neural networks.mp4

02:38

06 - Multilayer perceptrons.mp4

01:44

03 - 2. The Building Blocks of Neural Networks

01 - Neurons and the brain.mp4

01:49

02 - A simple model of a neuron.mp4

05:43

03 - Activation functions.mp4

06:21

04 - Perceptrons A better model of a neuron.mp4

03:25

05 - Challenge Finish the perceptron.mp4

01:18

06 - Solution Finish the perceptron.mp4

00:44

07 - Logic gates.mp4

03:27

08 - Challenge Logic gates with perceptrons.mp4

01:00

09 - Solution Logic gates with perceptrons.mp4

00:58

04 - 3. Building Your Network

01 - Linear separability.mp4

03:28

02 - Writing the multilayer perceptron class.mp4

02:57

03 - Challenge Finish the multilayer perceptron class.mp4

01:29

04 - Solution Finish the multilayer perceptron class.mp4

02:19

05 - 4. Training Your Network

01 - The need for training.mp4

04:45

02 - The training process.mp4

03:47

03 - The error function.mp4

02:27

04 - Gradient descent.mp4

02:53

05 - The Delta rule.mp4

03:34

06 - The Backpropagation algorithm.mp4

09:12

07 - Challenge Write your own Backpropagation method.mp4

03:20

08 - Solution Write your own Backpropagation method.mp4

04:50

06 - 5. Let's Make a Segment Display Classifier

01 - Segment display recognition.mp4

03:11

02 - Challenge Design your own SDR neural network.mp4

01:21

03 - Solution Design your own SDR neural network.mp4

05:17

04 - Challenge Train your own SDR neural network.mp4

03:34

05 - Solution Train your own SDR neural network.mp4

04:00

06 - 7 to 1 network GUI demo.mp4

06:19

07 - 7 to 10 network GUI demo.mp4

02:46

08 - 7 to 7 network GUI demo.mp4

04:18

07 - Conclusion

01 - Next steps.mp4

00:47

Description

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.

More details

User Reviews

Rating

average 0

Total votes0

Focused display

Python

Neural Networks

Eduardo Corpeño

Instructor's Courses

Electrical and Computer Engineer with over 20 years of experience as an instructor and mentor at a leading technology university. Passionate about excellence and robust solutions that can solve problems efficiently. An embedded systems instructor experienced on the leading industry technologies in both commercial and open architectures. A seasoned programmer in a handful of the most industry relevant programming languages, not afraid of learning others quickly. Passionate about online education since 2011, having authored and produced over 30 online courses ranging from basic programming to AI applications and embedded systems.

Linkedin Learning

View courses Linkedin Learning

LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.