This 2018 paper demonstrates how common spreadsheet software can be used to simulate neural networks, offering a readily accessible and interactive educational tool. It details the implementation of a multilayer perceptron (MLP) within a spreadsheet, using built-in functions to perform calculations for forward propagation, backpropagation, and gradient descent. The authors argue that this approach allows for a deeper understanding of neural network mechanics due to its transparent and step-by-step nature, which can be particularly beneficial for teaching purposes. They provide examples of classification and regression tasks, showcasing the spreadsheet's capability to handle different activation functions and datasets. The paper concludes that spreadsheet-based simulations, while not suitable for large-scale applications, offer a valuable pedagogical alternative for introducing and exploring fundamental neural network concepts.
This spreadsheet documents a personal file system designed to mitigate data loss at home. It outlines a tiered backup strategy using various methods and media, including cloud storage (Google Drive, Backblaze), local network drives (NAS), and external hard drives. The system emphasizes redundancy by storing multiple copies of important data in different locations, and incorporates a structured approach to file organization and a regular backup schedule. The author categorizes their data by importance and sensitivity, employing different strategies for each category, reflecting a focus on preserving critical data in the event of various failure scenarios, from accidental deletion to hardware malfunction or even house fire.
Several commenters on Hacker News expressed skepticism about the practicality and necessity of the "Home Loss File System" presented in the linked Google Doc. Some questioned the complexity introduced by the system, suggesting simpler solutions like cloud backups or RAID would be more effective and less prone to user error. Others pointed out potential vulnerabilities related to security and data integrity, especially concerning the proposed encryption method and the reliance on physical media exchange. A few commenters questioned the overall value proposition, arguing that the risk of complete home loss, while real, might be better mitigated through insurance rather than a complex custom file system. The discussion also touched on potential improvements to the system, such as using existing decentralized storage solutions and more robust encryption algorithms.
Summary of Comments ( 1 )
https://news.ycombinator.com/item?id=43155881
HN users discuss the practicality and educational value of simulating neural networks in spreadsheets. Some find it a clever way to visualize and understand the underlying mechanics, especially for beginners, while others argue its limitations make it unsuitable for real-world applications. Several commenters point out the computational constraints of spreadsheets, making them inefficient for larger networks or datasets. The discussion also touches on alternative tools for learning and experimenting with neural networks, like Python libraries, which offer greater flexibility and power. A compelling point raised is the potential for oversimplification, potentially leading to misconceptions about the complexities of real-world neural network implementations.
The Hacker News post titled "Computer Simulation of Neural Networks Using Spreadsheets (2018)" linking to the arXiv paper "Reliable Training and Initialization of Deep Residual Networks" has several comments discussing the practicality and educational value of implementing neural networks in spreadsheets.
Several commenters are skeptical of the usefulness of this approach for anything beyond very simple networks or educational purposes. One commenter points out the computational limitations of spreadsheets, especially when dealing with large datasets or complex architectures. They argue that specialized tools and libraries are far more efficient and practical for serious neural network development. Another commenter echoes this sentiment, suggesting that while conceptually interesting, the performance limitations would make this approach unsuitable for real-world applications.
Others see value in the spreadsheet approach for educational purposes. One commenter suggests it could be a good way to visualize and understand the underlying mechanics of neural networks in a more accessible way than abstract code. They emphasize the benefit of seeing the calculations unfold step-by-step, which can aid in grasping the concepts of forward and backward propagation. Another agrees, adding that the readily available nature of spreadsheets makes them a low barrier to entry for beginners interested in experimenting with neural networks.
A recurring theme in the comments is the limitations of spreadsheets in handling the scale and complexity of modern deep learning. One comment highlights the difficulty of implementing more advanced techniques like convolutional or recurrent layers within a spreadsheet environment. Another points out that even for simpler networks, training time would be significantly longer compared to dedicated deep learning frameworks.
Some commenters discuss alternative tools for educational purposes, such as interactive Python notebooks, arguing that they offer a better balance between accessibility and functionality. While acknowledging the simplicity of spreadsheets, they emphasize the importance of transitioning to more powerful tools as learning progresses.
A few comments also touch upon the potential use of spreadsheet implementations for very specific, limited applications where computational resources are extremely constrained or where a simple model is sufficient. However, these are presented as niche scenarios rather than a general recommendation.
Overall, the comments express a mix of skepticism and cautious optimism regarding the use of spreadsheets for neural network simulation. While recognizing the potential educational value for beginners, they overwhelmingly agree that spreadsheets are not a viable alternative to dedicated tools for serious deep learning work. The limitations in performance, scalability, and implementation of complex architectures are seen as major drawbacks that outweigh the perceived simplicity of the spreadsheet approach.