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Building Neural Networks from Scratch with Python

Written by

Kleber Alcatrao

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Price: $24.95
(as of Dec 24,2024 09:10:58 UTC – Details)


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From the Publisher

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recurrent neural networks Neural Networks for beginners Neural Networks for dummies

Neural Networks in python neural networks and deep learning a textbook

Neural Networks in python neural networks and deep learning a textbook

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introduction to neural networks recurrent neural networks physics informed neural networks

recurrent neural networks Neural Networks for beginners Neural Networks for dummies

recurrent neural networks Neural Networks for beginners Neural Networks for dummies

Neural Networks in python neural networks and deep learning a textbook

Neural Networks in python neural networks and deep learning a textbook

introduction to neural networks recurrent neural networks physics informed neural networks

introduction to neural networks recurrent neural networks physics informed neural networks

Neural Networks in python neural networks and deep learning a textbook

Neural Networks in python neural networks and deep learning a textbook

ASIN ‏ : ‎ B0CQKNMJZK
Publication date ‏ : ‎ December 17, 2023
Language ‏ : ‎ English
File size ‏ : ‎ 454 KB
Simultaneous device usage ‏ : ‎ Unlimited
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 178 pages
Page numbers source ISBN ‏ : ‎ B0CQXL4GFG

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Building Neural Networks from Scratch with Python

In this post, we will explore how to create neural networks from scratch using Python. Neural networks are a powerful tool in machine learning and can be used for tasks such as image recognition, natural language processing, and more. By understanding the fundamentals of neural networks and implementing them in Python, you can gain a deeper insight into how they work and how to customize them for your specific needs.

To build a neural network from scratch, we will cover the following steps:

1. Define the structure of the neural network, including the number of layers, the number of nodes in each layer, and the activation functions.
2. Initialize the weights and biases for each layer.
3. Implement the forward propagation algorithm to pass input data through the network and calculate the output.
4. Define a loss function to measure the error between the predicted output and the actual output.
5. Implement the backpropagation algorithm to update the weights and biases based on the error calculated by the loss function.
6. Train the neural network by repeating the forward and backward passes with a training dataset to minimize the loss function.

By following these steps and implementing them in Python, you can create a fully functional neural network that can be used for a variety of machine learning tasks. Stay tuned for our upcoming tutorials on building and training neural networks from scratch!
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