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+--- Thread: Neural NetWorks in Python from Scratch: Learning by Doing (/Thread-Neural-NetWorks-in-Python-from-Scratch-Learning-by-Doing)
Neural NetWorks in Python from Scratch: Learning by Doing - AD-TEAM - 07-13-2024
![[Image: 8362335e777d56f4cf28024bf1928b10.jpg]](https://i123.fastpic.org/big/2024/0713/10/8362335e777d56f4cf28024bf1928b10.jpg)
Neural Networks in Python from Scratch: Learning by Doing
Published 06/2022
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 452 MB | Duration: 29 lectures • 3h 32m
From intuitive examples to image recognition in 3 hours - Experience neuromorphic computing & machine learning hands-on
What you'll learn
Program neural networks for 3 different problems from scratch in plain Python
Start simple: Understand input layer, output layer, weights, error function, accuracy, training & testing at an intuitive example
Complicate the problem: Introduce hidden layers & activation functions for building more useful networks
Real-life application: Use this network for image recognition
Requirements
Basic programing skills are desired if you want to program along with me. We use Python3 without any advanced modules.
Description
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.
I hope you are excited and I kindly welcome you to our course!
Who this course is for
This beginner-friendly course is for everyone! Especially if you
Are curious about neural networks and want to really understand how they operate
Work in machine learning or data science but have not yet programed a neural network yourself from scratch
Want to really learn about machine learning without fancy frameworks/modules - Just you, me & standard python
![[Image: sPqgyYy0_o.jpg]](https://images2.imgbox.com/21/b5/sPqgyYy0_o.jpg)
What you'll learn
Program neural networks for 3 different problems from scratch in plain Python
Start simple: Understand input layer, output layer, weights, error function, accuracy, training & testing at an intuitive example
Complicate the problem: Introduce hidden layers & activation functions for building more useful networks
Real-life application: Use this network for image recognition
Requirements
Basic programing skills are desired if you want to program along with me. We use Python3 without any advanced modules.
Description
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.
I hope you are excited and I kindly welcome you to our course!
Who this course is for
This beginner-friendly course is for everyone! Especially if you
Are curious about neural networks and want to really understand how they operate
Work in machine learning or data science but have not yet programed a neural network yourself from scratch
Want to really learn about machine learning without fancy frameworks/modules - Just you, me & standard python