Build and Train a Neural Network in Python: Iris Flower Classification

Name: Build and Train a Neural Network in Python: Iris Flower Classification
Uploaded: 2023-09-03 12:16:23
Description: The video teaches how to build and train a neural network from scratch using linear algebra and Python to classify iris flowers based on their attributes.

This article is a summary of a YouTube video "Нейронная сеть на Python с нуля" by Дмитрий Коробченко

TLDR The video teaches how to build and train a neural network from scratch using linear algebra and Python to classify iris flowers based on their attributes.

Key insights

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There is another way to calculate a fully connected layer in a neural network that is more economical and easier to implement.

Timestamped Summary

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00:00
Dmitry Korobchenko's neural networks can classify an iris flower using scalar features.
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01:26
Simplify neural network calculations with row vectors and shorter formulas, learn matrix multiplication and build a 2-layer network to classify iris based on 4 attributes.
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03:47
The neural network has 2 layers, 5 neurons in the first layer, and 3 classes in the last layer, using soft max function for probabilities.
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06:04
Write a neural network from scratch using pure python and linear algebra to calculate the output with input vector x, weight matrices, and displacement vectors for each layer.
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07:56
Forward propagation involves matrix multiplication, activation functions, and output calculation.
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09:05
The predict function classifies irises into three types based on their probabilities.
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10:40
💻 Training neural networks involves using weight matrices and hyperparameters to extract input data.
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12:26
Learn how to train neural networks and calculate gradients using backpropagation on the channel.