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---
title: Best Google for Developers posts — September 2024
description: The most upvoted Google for Developers posts from September 2024, curated by the daily.dev community.
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og:description: The most upvoted Google for Developers posts from September 2024, curated by the daily.dev community.
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# Best of Google for Developers — September 2024

1. 1  
[](https://daily.dev/posts/machine-learning-crash-course-neural-networks-intro-xayvb5ira "Machine Learning Crash Course: Neural Networks Intro")  
Video  
![Avatar of googledevelopers](https://media.daily.dev/image/upload/s--jj3nxRoG--/f_auto/v1725035497/logos/googledevelopers)Google for Developers · 2y  
Machine Learning Crash Course: Neural Networks Intro  
This explains the transition from linear models to neural networks for modeling nonlinear relationships. It covers how traditional linear models use feature crosses and introduces the concept of hidden layers in neural networks. The key highlight is the use of activation functions, like ReLU, to introduce nonlinearity, enabling neural networks to approximate complex functions and automatically learn nonlinear relationships during training.  
38
2. 2  
[](https://daily.dev/posts/machine-learning-crash-course-neural-networks-backprop-m1ixw8cw7 "Machine Learning Crash Course: Neural Networks Backprop")  
Video  
![Avatar of googledevelopers](https://media.daily.dev/image/upload/s--jj3nxRoG--/f_auto/v1725035497/logos/googledevelopers)Google for Developers · 2y  
Machine Learning Crash Course: Neural Networks Backprop  
Neural networks utilize backpropagation to adjust the weights of nodes to improve accuracy in classification tasks. This method assigns blame to different nodes based on their contribution to the error, adjusting parameters more significantly when the error is high. Techniques like these are crucial for tasks such as image classification, although different neural network configurations might be required for specific types of problems.  
27
3. 3  
[](https://daily.dev/posts/machine-learning-crash-course-embeddings-xy95zkil4 "Machine Learning Crash Course: Embeddings")  
Video  
![Avatar of googledevelopers](https://media.daily.dev/image/upload/s--jj3nxRoG--/f_auto/v1725035497/logos/googledevelopers)Google for Developers · 2y  
Machine Learning Crash Course: Embeddings  
Neuron networks represent words as embeddings, which are high-dimensional vectors. A common method to generate these embeddings is through a neural network with an input layer using one-hot encoding, followed by an embedding layer, and an output layer. The model is trained using large text corpora to predict the context of words, creating embedding vectors that encode word meanings. Similar words end up closer together in vector space. This process involves significant training time and memory usage.  
13
4. 4  
[](https://daily.dev/posts/when-your-coworker-is-still-in-wfh-mode--mj086z11q "When your coworker is still in WFH mode...")  
Video  
![Avatar of googledevelopers](https://media.daily.dev/image/upload/s--jj3nxRoG--/f_auto/v1725035497/logos/googledevelopers)Google for Developers · 2y  
When your coworker is still in WFH mode...  
A light-hearted post that humorously addresses a coworker who hasn't completely adapted back to office life after working from home.  
11  
2

[See all Google for Developers archives](/sources/googledevelopers/best-of)

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