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research#deep learning📝 BlogAnalyzed: Jan 20, 2026 12:00

Unlocking MNIST: Handwritten Digit Recognition from Scratch with Python!

Published:Jan 20, 2026 11:59
1 min read
Qiita DL

Analysis

This article offers a fresh, hands-on approach to MNIST digit recognition using Python, bypassing complex frameworks and focusing on fundamental concepts. It's a fantastic resource for learners eager to understand the inner workings of neural networks and deep learning without relying on external libraries. The author's dedication to building from the ground up provides a uniquely insightful learning experience.
Reference

“MNIST digit recognition is tackled in Python without using frameworks or the like.”

PermalinkQiita DL
research#backpropagation📝 BlogAnalyzed: Jan 18, 2026 08:00

Deep Dive into Backpropagation: A Student's Journey with Gemini

Published:Jan 18, 2026 07:57
1 min read
Qiita DL

Analysis

This article beautifully captures the essence of learning deep learning, leveraging the power of Gemini for interactive exploration. The author's journey, guided by a reputable textbook, offers a glimpse into how AI tools can enhance the learning process. It's an inspiring example of hands-on learning in action!
Reference

“The article is based on conversations with Gemini.”

PermalinkQiita DL
research#gradient📝 BlogAnalyzed: Jan 11, 2026 18:36

Deep Learning Diary: Calculating Gradients in a Single-Layer Neural Network

Published:Jan 11, 2026 10:29
1 min read
Qiita DL

Analysis

This article provides a practical, beginner-friendly exploration of gradient calculation, a fundamental concept in neural network training. While the use of a single-layer network limits the scope, it's a valuable starting point for understanding backpropagation and the iterative optimization process. The reliance on Gemini and external references highlights the learning process and provides context for understanding the subject matter.
Reference

“Based on conversations with Gemini, the article is constructed.”

PermalinkQiita DL
research#numerical differentiation📝 BlogAnalyzed: Jan 10, 2026 04:42

Deep Learning Diary Vol. 4: Numerical Differentiation - A Practical Guide

Published:Jan 8, 2026 14:43
1 min read
Qiita DL

Analysis

This article seems to be a personal learning log focused on numerical differentiation in deep learning. While valuable for beginners, its impact is limited by its scope and personal nature. The reliance on a single textbook and Gemini for content creation raises questions about the depth and originality of the material.

Key Takeaways

Reference

“Geminiとのやり取りを元に、構成されています。”

PermalinkQiita DL
research#loss📝 BlogAnalyzed: Jan 10, 2026 04:42

Exploring Loss Functions in Deep Learning: A Practical Guide

Published:Jan 8, 2026 07:58
1 min read
Qiita DL

Analysis

This article, based on a dialogue with Gemini, appears to be a beginner's guide to loss functions in neural networks, likely using Python and the 'Deep Learning from Scratch' book as a reference. Its value lies in its potential to demystify core deep learning concepts for newcomers, but its impact on advanced research or industry is limited due to its introductory nature. The reliance on a single source and Gemini's output also necessitates critical evaluation of the content's accuracy and completeness.
Reference

“ニューラルネットの学習機能に話が移ります。”

PermalinkQiita DL
Education#NLP📝 BlogAnalyzed: Jan 3, 2026 02:10

Deep Learning from Scratch 2: Natural Language Processing - Chapter 1 Summary

Published:Jan 2, 2026 15:52
1 min read
Qiita AI

Analysis

This article summarizes Chapter 1 of the book 'Deep Learning from Scratch 2: Natural Language Processing'. It aims to help readers understand the chapter's content and key vocabulary, particularly those struggling with the book.
Reference

“This article summarizes Chapter 1 of the book 'Deep Learning from Scratch 2: Natural Language Processing'.”

PermalinkQiita AI