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- 発売日：2024/10/18
- 著者：鎌形桂太 著/三好大悟 監修
- ISBN：9784295020318
- 文章分類問題を解いてみよう
- 言語モデルを動かしてみよう
- chap05_LLM_intro.ipynb
- ① 穴埋め問題を解くMLM
- ② 次のトークンを予測するCLM
- chap05_LLM_intro.ipynb
- 画像分類問題を解いてみよう
- オートエンコーダを作ってみよう
- VAEを作ってみよう
- ▶をクリックすると展開できます
- []内の番号は本文の脚注番号です
第1章
- [1] similarweb Blog
- [3] similarweb社による上位ウェブサイトランキング, ChatGPT への月間アクセス数
- [5] 自治体AI zevo
- [6] 埼玉県戸田市によるChatGPTに関する調査研究
- [7] ディープフェイク（deepfake）を用いて作成されたCM動画
- [8] Bruce Willis denies selling rights to his face
- [12] Hugging Face, Hugging Face, Civitai
- [17] A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks
- [21] On the Opportunities and Risks of Foundation Models
- [22] Language Models are Few-Shot Learners
- [24] On the Opportunities and Risks of Foundation Models
- [25] Learning Transferable Visual Models From Natural Language Supervision
第2章
- [1] 23-1444 - Federal Trade Commission v. Automators LLC et al
- [2] AI Washing
- [3] 令和元年版情報通信白書
- [4] A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence
- [5] A Collection of Definitions of Intelligence
- [6] Introducing Superalignment
- [7] プリペアドネス（preparedness） チーム
- [8] Mark Zuckerberg’s new goal is creating artificial general intelligence
- [9] Rule-based Expert Systems : The MYCIN Experiments of the Stanford Heuristic Programming Project, Computer-Based Medical Consultations: Mycin
- [13] Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence
- [16] Does Deep Blue use Artificial Intelligence?
- [17] Does Deep-Blue use AI?
- [18] Recommendation of the Council on Artificial Intelligence, H.R.6216 - National Artificial Intelligence Initiative Act of 2020, SEC. 3 (3)
- [21] H.R.6216 - National Artificial Intelligence Initiative Act of 2020, SEC. 3 (9)
- [28] A survey on semi-supervised learning
- [29] Unsupervised and self-supervised deep learning approaches for biomedical text mining , Self-supervised Learning: A Succinct Review
- [33] Mastering the game of Go with deep neural networks and tree search
- [34] Training language models to follow instructions with human feedback, Introducing ChatGPT
- [39] Updates to the OECD’s definition of an AI system explained
- [46] 1.1. Linear Models
- [64] Visualizing the Loss Landscape of Neural Nets, Loss Visualization
- [66] A logical calculus of the ideas immanent in nervous activity
- [68] The perceptron: A probabilistic model for information storage and organization in the brain
- [69] MARK I PERCEPTRON OPERATORS' MANUAL
- [70] Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark
第3章
- [1] Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine
- [2] 生成 AI による検索体験 (SGE) のご紹介
- [3] Introducing Duolingo Max, a learning experience powered by GPT-4
- [10] 日本語の自然言語処理ライブラリ「GiNZA」
- [11] 日本語形態素解析における未知語処理の一手法―既知語から派生した表記と未知オノマトペの処理―
- [14] pneumonoultramicroscopicsilicovolcanoconiosis
- [20] SentencePiece
- [21] OpenAIのTokenizer
- [34] 実践で学ぶBM25 - パート2：BM25のアルゴリズムと変数
- [41] Efficient Estimation of Word Representations in Vector Space
第4章
第5章
- [6] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Deep contextualized word representations
- [10] bert-base-japanese-whole-word-masking
- [11] A Primer in BERTology: What we know about how BERT works
- [12] GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
- [13] JGLUE: Japanese General Language Understanding Evaluation
- [14] GLUE leaderboard
- [15] japanese-gpt2-medium
- [20] Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
- [21] The Curious Case of Neural Text Degeneration
- [27] Finetuned Language Models Are Zero-Shot Learners
- [28] databricks-dolly-15k-ja
- [32] LLMのための日本語インストラクションデータ 公開ページ
- [33] Fine tuning is for form, not facts
- [39] AttentionViz: A Global View of Transformer Attention
- [40] Attention Is All You Need
- [43] Scaling Laws for Neural Language Models, Language Models are Few-Shot Learners
- [44] Emergent Abilities of Large Language Models
- [45] BIG-bench tasks
- [49] Are Emergent Abilities of Large Language Models a Mirage?
- [51] Finetuning an LLM: RLHF and alternatives (Part I)
- [54] 検索拡張生成（RAG）とは？
- [55] NotebookLM
- [56] PRtimes上で「RAG」と検索した結果
第6章
- [24] 図6-02-11をDeep playground上で再現
- [25] 図6-02-12をDeep playground上で再現
- [31] convolution-shape-calculator
- [33] Image Kernels, Image-Convolution-Playground
- [35] CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
- [36] Gradient-based learning applied to document recognition
- [37] ImageNet Classification with Deep ConvolutionalNeural Networks
- [38] MARK I PERCEPTRON OPERATORS' MANUAL
- [39] Very Deep Convolutional Networks for Large-Scale Image Recognition
- [40] Comparative Analysis of Steering Angle Prediction For Automated Object Using Deep Neural Network
- [44] Clinical ABCDE rule for early melanoma detection
- [45] Skin Cancer MNIST: HAM10000
- [47] FixCaps: An Improved Capsules Network for Diagnosis of Skin Cancer
- [48] SkinVision
- [49] ステートメント：人工知能AIと病理医について
- [50] Rich feature hierarchies for accurate object detection and semantic segmentation
- [51] You Only Look Once: Unified, Real-Time Object Detection
- [52] SSD: Single Shot MultiBox Detector
- [53] MRI画像から神経膠腫の疑いのある領域を精密に抽出するAI技術を共同開発
- [54] The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI
第7章
第8章
- [1] Reducing the Dimensionality of Data with Neural Networks
- [3] Building Autoencoders in Keras
- [12] Auto-Encoding Variational Bayes
- [14] chap08_mnist_digit_VAE.ipynb
- [15] Generative Adversarial Networks
- [16] This X Does Not Exist
- [17] StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
- [18] Only a Matter of Style: Age Transformation Using a Style-Based Regression Model
- [19] Denoising Diffusion Implicit Models
- [21] Learning Transferable Visual Models From Natural Language Supervision
- [23] ImageNet Data
- [25] Brazil: Children’s Personal Photos Misused to Power AI Tools, YFCC100M: the new data in multimedia research
- [26] High-Resolution Image Synthesis with Latent Diffusion Models
- [30] Ciditai
- [32] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- [34] Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models, Make-An-Audioによって生成した音声
- [37] Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation
- [38] Introducing Stable Audio Open - An Open Source Model for Audio Samples and Sound Design
- [39] Stable Video Diffusion