投稿時間:2023-07-09 17:08:28 RSSフィード2023-07-09 17:00 分まとめ(11件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
python	Pythonタグが付けられた新着投稿 - Qiita	Gradient descentで学習した線形回帰アルゴリズムの実装	https://qiita.com/Hiroaki-K4/items/dca8eec753502cb82ab8	estimatepr	2023-07-09 16:31:32
python	Pythonタグが付けられた新着投稿 - Qiita	手間のかかるカルテ記載を音声で行う①（Python＆ChatGPT）	https://qiita.com/ikuro_mori/items/0491b9cd72703e89c6ab	chatgpt	2023-07-09 16:09:27
AWS	AWSタグが付けられた新着投稿 - Qiita	AmazonLinux2023のAD連携とフォルダアクセス権設定	https://qiita.com/u-bayashi/items/d5f85b461f4731e5a7a2	amazon	2023-07-09 16:32:27
AWS	AWSタグが付けられた新着投稿 - Qiita	DataZone に Redshift のテーブルを連携してみた	https://qiita.com/sugimount-a/items/ef4aa882d0516f2b89e5	athena	2023-07-09 16:02:38
golang	Goタグが付けられた新着投稿 - Qiita	GCP学習記　Cloud Functions編（１）	https://qiita.com/BB30330807/items/a43a2428a2e249f69c1a	cloudfunctions	2023-07-09 16:58:31
GCP	gcpタグが付けられた新着投稿 - Qiita	GCP学習記　Cloud Functions編（１）	https://qiita.com/BB30330807/items/a43a2428a2e249f69c1a	cloudfunctions	2023-07-09 16:58:31
Azure	Azureタグが付けられた新着投稿 - Qiita	MS Semantic Kernelを用いたAIアシスタントの作り方（ChatGPT、GPT-4版）【連載第1回】	https://qiita.com/YutaroOgawa2/items/87cf07110255c06a4aa9	chatgpt	2023-07-09 16:42:01
Ruby	Railsタグが付けられた新着投稿 - Qiita	ページネーションについて	https://qiita.com/shinyakoki/items/8edf59148accdc85f3b0	複数	2023-07-09 16:13:38
海外TECH	DEV Community	INTRODUCTION TO LLMS	https://dev.to/abeenoch/introduction-to-llms-46hj	INTRODUCTION TO LLMSSome say ignorance is bliss I beg to differ You may have wondered about large language models LLMs or maybe not let s be honest Let me educate you on how LLMs function and their applications Are you curious now I ll guide you into the realm of LLMs LLMs or extensively known as Large Language Models are advanced deep learning models specifically designed to process human languages They belong to a type of machine learning model capable of performing various natural language processing tasks such as generating text and language translation At the heart of these models lies a powerful transformer model where the magic happens You are most likely thinking What exactly is a Transformer Model Transformers are neural networks that excel at understanding and contextual comprehension through sequential data analysis They rely on a modern mathematical technique known as attention or self attention which was introduced in the paper Attention is All You Need by Vaswani et al Prior to the emergence of transformers text comprehension in machines relied on models based on recurrent neural networks These models processed input text one word or character at a time and generated an output once the entire input was consumed While effective they sometimes forgot what occurred at the beginning of the sequence when reaching the end In contrast the attention mechanism in transformers allows the model to comprehend the entire sentence or paragraph simultaneously rather than one word at a time This enables the transformer model to grasp the context of words more effectively Nowadays state of the art language processing models are predominantly based on transformers The transformer model employs an encoder decoder architecture with the encoder handling the input sequence and the decoder generating the output sequence The input text undergoes tokenization using a byte pair encoding tokenizer with each token converted into a vector via word embedding The encoder comprises multiple encoding layers that process the input iteratively while the decoder consists of decoding layers that generate the output based on the information from the encoder Both encoder and decoder layers utilize an attention mechanism which calculates the relevance of different parts of the input or output sequence The attention mechanism operates through scaled dot product attention wherein attention weights are simultaneously calculated between each token in the sequence These weights determine the importance of other tokens in generating the output for a specific token The transformer model learns three weight matrices query weights key weights and value weights which are employed to calculate attention weights To enhance the model s ability to capture diverse forms of relevance the transformer model incorporates multiple attention heads in each layer Each attention head attends to different relationships between tokens allowing the model to capture various dependencies This parallel processing of attention heads enables efficient computation To prevent information leakage during training the decoder utilizes a masked attention mechanism This mechanism eliminates attention links between specific token pairs ensuring that the decoder doesn t have access to future tokens Another crucial aspect of the transformer model is positional encoding It provides information about the relative positions of tokens within the input sequence Positional encoding entails representing token positions through fixed size vectors The transformer model employs an encoder decoder architecture with attention mechanisms to process input sequences and generate output sequences It leverages scaled dot product attention and multiple attention heads to capture different forms of relevance Furthermore the model incorporates positional encoding to retain information about token positions in the sequence In summary Large Language Models LLMs are transformer models scaled up to a significant size Due to their size these models are typically not runnable on a single computer and are instead provided as a service through APIs or web interfaces Large language models are trained on vast amounts of text data allowing them to learn the patterns and structures of language For instance the GPT model which powers the ChatGPT service was trained on a massive corpus of text from the internet including books articles websites and other sources Through this training process the model learns the statistical relationships between words phrases and sentences enabling it to generate coherent and contextually relevant responses when given a prompt or query Due to the extensive training data the GPT model possesses knowledge of various topics and can understand multiple languages This enables it to generate text in different styles While it may seem impressive that large language models can perform tasks like translation text summarization and question answering it is not surprising when considering that these models are trained to match specific patterns and structures present in the provided prompts That wasn t so hard now was it	2023-07-09 07:14:46
ニュース	BBC News - Home	Meet Sammy, the surfing seal from San Diego	https://www.bbc.co.uk/news/world-us-canada-66146766?at_medium=RSS&at_campaign=KARANGA	exploits	2023-07-09 07:43:32
ビジネス	ダイヤモンド・オンライン - 新着記事	新日本酒紀行「喜平静岡蔵」 - 新日本酒紀行	https://diamond.jp/articles/-/325618	静岡県	2023-07-09 17:00:00