- Overview
- Curriculum
- Discussion
- Review
- Instructor
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Program neural networks for 3 different problems from scratch in plain Python
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Start simple: Understand input layer, output layer, weights, error function, accuracy, training & testing at an intuitive example
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Complicate the problem: Introduce hidden layers & activation functions for building more useful networks
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Real-life application: Use this network for image recognition
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Basic programing skills are desired if you want to program along with me. We use Python3 without any advanced modules.
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This beginner-friendly course is for everyone! Especially if you:
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Are curious about neural networks and want to really understand how they operate
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Work in machine learning or data science but have not yet programed a neural network yourself from scratch
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Want to really learn about machine learning without fancy frameworks/modules - Just you, me & standard python
** The quickest way to understanding (and programming) neural networks using Python **
This course is for everyone who wants to learn how neural networks work by hands-on programming!
Everybody is talking about neural networks but they are hard to understand without setting one up yourself. Luckily, the mathematics and programming skills (python) required are on a basic level so we can progam 3 neural networks in just over 3 hours. Do not waste your time! This course is optimized to give you the deepest insight into this fascinating topic in the shortest amount of time possible.
The focus is fully on learning-by-doing and I only introduce new concepts once they are needed.
What you will learn
After a short introduction, the course is separated into three segments - 1 hour each:
1) Set-up the most simple neural network: Calculate the sum of two numbers.
You will learn about:
Neural network architecture
Weights, input & output layer
Training & test data
Accuracy & error function
Feed-forward & back-propagation
Gradient descent
2) We modify this network: Determine the sign of the sum.
You will be introduced to:
Hidden layers
Activation function
Categorization
3) Our network can be applied to all sorts of problems, like image recognition: Determine hand-written digits!
After this cool and useful real-life application, I will give you an outlook:
How to improve the network
What other problems can be solved with neural networks?
How to use pre-trained networks without much effort
Why me?
My name is Börge Göbel and I am a postdoc working as a scientist in theoretical physics where neural networks are used a lot.
I have refined my advisor skills as a tutor of Bachelor, Master and PhD students in theoretical physics and have other successful courses here on Udemy.
"Excellent course! In a simple and understandable way explained everything about the functioning of neural networks under the hood." - Srdan Markovic
I hope you are excited and I kindly welcome you to our course!
Overview of the course
Overview of the course
Template files for this course
Template files for this course
Interpolation (or regression) - The fundamental principle of machine learning
Interpolation (or regression) - The fundamental principle of machine learning
Interpolation
Interpolation
Let's get started!
Let's get started!
From interpolation to neural networks
From interpolation to neural networks
What are neural networks?
What are neural networks?
[Project 1] Most simple neural network: Sum of two numbers
[Project 1] Most simple neural network: Sum of two numbers
Prepare the training and testing data
Prepare the training and testing data
Initialize the weights & Calculate the output
Initialize the weights & Calculate the output
Accuracy & Error functions
Accuracy & Error functions
Gradient of the error function
Gradient of the error function
Training the neural network via gradient descent
Training the neural network via gradient descent
Using the trained network on the test data
Using the trained network on the test data
Playing with the parameters
Playing with the parameters
Neural networks
Neural networks
[Project 2] Complete neural network: Sign of the sum of two numbers
[Project 2] Complete neural network: Sign of the sum of two numbers
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Dr. Börge Göbel
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