Project Neural Network #3
Difficulty: medium
Time: 2-3h
Languages: The one you picked
=======================
Use the perceptrons of the last Challenge and arrange them into layers.
The first layer is the input layer, the next layers are hidden layers and the last one is the output layer.
Every Perceptron gets as input the SUM of all the outputs of the last layer, each multiplied by some value (called "weights"). In later challenges, we will learn how to improve those weights in order to make out Neural Network be a more and more accurate mathematical model.
#input #output #NeuralNetwork #MachineLearning #Arrays #Structure
Solutions to @Linuxer4Fun
Documentation: https://en.wikipedia.org/wiki/Artificial_neural_network
Difficulty: medium
Time: 2-3h
Languages: The one you picked
=======================
Use the perceptrons of the last Challenge and arrange them into layers.
The first layer is the input layer, the next layers are hidden layers and the last one is the output layer.
Every Perceptron gets as input the SUM of all the outputs of the last layer, each multiplied by some value (called "weights"). In later challenges, we will learn how to improve those weights in order to make out Neural Network be a more and more accurate mathematical model.
#input #output #NeuralNetwork #MachineLearning #Arrays #Structure
Solutions to @Linuxer4Fun
Documentation: https://en.wikipedia.org/wiki/Artificial_neural_network
Project Neural Network #4
Difficulty: hard
Time: 7h
Languages: the one you picked
========================
With this basic Neural Network structure, improve the weights of each single perceptron. For this, use backpropagation. Intuitively it means: "how much does the error value change, when I change this weight?".
#input #output #NeuralNetwork #MathematicalModels #Maths #Arrays #algorythm #backpropagation
Solutions to @Linuxer4Fun
Documentation: https://de.wikipedia.org/wiki/Backpropagation
++++++++++++++++++++++++++
This marks the end of the first project. Did you like it? Do you want more? Do you have suggestions? Discuss those in the challenge-solvers group: https://t.me/joinchat/EXSmZwy0PqOMNWPKxI3shw
Do you want to share the programming fun with your friends? Then, give them this link: https://t.me/prograchallenges
Difficulty: hard
Time: 7h
Languages: the one you picked
========================
With this basic Neural Network structure, improve the weights of each single perceptron. For this, use backpropagation. Intuitively it means: "how much does the error value change, when I change this weight?".
#input #output #NeuralNetwork #MathematicalModels #Maths #Arrays #algorythm #backpropagation
Solutions to @Linuxer4Fun
Documentation: https://de.wikipedia.org/wiki/Backpropagation
++++++++++++++++++++++++++
This marks the end of the first project. Did you like it? Do you want more? Do you have suggestions? Discuss those in the challenge-solvers group: https://t.me/joinchat/EXSmZwy0PqOMNWPKxI3shw
Do you want to share the programming fun with your friends? Then, give them this link: https://t.me/prograchallenges
Circles
Difficulty: easy
Time: 2h
Languages: Any that supports image manipulation
=======================
Program a program that draws a circle in a picture, to get used to Image manipulation.
Input: radius, center.x, center.y
Output: image with a circle
#input #output #images #maths #arrays #algorythm
Solutions to @LInuxer4Fun
Difficulty: easy
Time: 2h
Languages: Any that supports image manipulation
=======================
Program a program that draws a circle in a picture, to get used to Image manipulation.
Input: radius, center.x, center.y
Output: image with a circle
#input #output #images #maths #arrays #algorythm
Solutions to @LInuxer4Fun