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Build a Large Language Model (From Scratch)
2025-07-29 · 19:00
We are going through Hands-On Build a Large Language Models (from Scratch) by Sebastian Raschka. The emphasis during the meetups will be to discuss key aspect of the Chapter being covering. Code focused discussions should be done over Discord. Raschka provides a step-by-step guide coding up your own foundation LLM ground up, spanning initial design and creation stages, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Pages being discussed: Please see the latest message (also pinned) in the #current-reading channel in our Discord chat space to see which pages we'll be reviewing in this session. Please note that the session is not recorded and participants are responsible for obtaining their own copy of the text. Discord joining instructions: Buy the book (affiliate links): Book overview: In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You'll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to:
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Build a Large Language Model (From Scratch)
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Build a Large Language Model (From Scratch)
2025-07-22 · 19:00
We are going through Hands-On Build a Large Language Models (from Scratch) by Sebastian Raschka. The emphasis during the meetups will be to discuss key aspect of the Chapter being covering. Code focused discussions should be done over Discord. Raschka provides a step-by-step guide coding up your own foundation LLM ground up, spanning initial design and creation stages, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Pages being discussed: Please see the latest message (also pinned) in the #current-reading channel in our Discord chat space to see which pages we'll be reviewing in this session. Please note that the session is not recorded and participants are responsible for obtaining their own copy of the text. Discord joining instructions: Buy the book (affiliate links): Book overview: In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You'll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to:
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Build a Large Language Model (From Scratch)
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Build a Large Language Model (From Scratch)
2025-07-15 · 19:00
We are going through Hands-On Build a Large Language Models (from Scratch) by Sebastian Raschka. The emphasis during the meetups will be to discuss key aspect of the Chapter being covering. Code focused discussions should be done over Discord. Raschka provides a step-by-step guide coding up your own foundation LLM ground up, spanning initial design and creation stages, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Pages being discussed: Please see the latest message (also pinned) in the #current-reading channel in our Discord chat space to see which pages we'll be reviewing in this session. Please note that the session is not recorded and participants are responsible for obtaining their own copy of the text. Discord joining instructions: Buy the book (affiliate links): Book overview: In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You'll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to:
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Build a Large Language Model (From Scratch)
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Build a Large Language Model (From Scratch)
2025-07-08 · 19:00
We are going through Hands-On Build a Large Language Models (from Scratch) by Sebastian Raschka. The emphasis during the meetups will be to discuss key aspect of the Chapter being covering. Code focused discussions should be done over Discord. Raschka provides a step-by-step guide coding up your own foundation LLM ground up, spanning initial design and creation stages, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Pages being discussed: Please see the latest message (also pinned) in the #current-reading channel in our Discord chat space to see which pages we'll be reviewing in this session. Please note that the session is not recorded and participants are responsible for obtaining their own copy of the text. Discord joining instructions: Buy the book (affiliate links): Book overview: In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You'll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to:
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Build a Large Language Model (From Scratch)
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Build a Large Language Model (From Scratch)
2025-07-01 · 19:00
We are going through Hands-On Build a Large Language Models (from Scratch) by Sebastian Raschka. The emphasis during the meetups will be to discuss key aspect of the Chapter being covering. Code focused discussions should be done over Discord. Raschka provides a step-by-step guide coding up your own foundation LLM ground up, spanning initial design and creation stages, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Pages being discussed: Please see the latest message (also pinned) in the #current-reading channel in our Discord chat space to see which pages we'll be reviewing in this session. Please note that the session is not recorded and participants are responsible for obtaining their own copy of the text. Discord joining instructions: Buy the book (affiliate links): Book overview: In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You'll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to:
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Build a Large Language Model (From Scratch)
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Build a Large Language Model (From Scratch)
2025-06-03 · 19:00
We are going through Hands-On Build a Large Language Models (from Scratch) by Sebastian Raschka. The emphasis during the meetups will be to discuss key aspect of the Chapter being covering. Code focused discussions should be done over Discord. Raschka provides a step-by-step guide coding up your own foundation LLM ground up, spanning initial design and creation stages, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Pages being discussed: Please see the latest message (also pinned) in the #current-reading channel in our Discord chat space to see which pages we'll be reviewing in this session. Please note that the session is not recorded and participants are responsible for obtaining their own copy of the text. Discord joining instructions: Buy the book (affiliate links): Book overview: In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You'll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to:
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Build a Large Language Model (From Scratch)
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Build a Large Language Model (From Scratch)
2025-05-27 · 19:00
We are going through Hands-On Build a Large Language Models (from Scratch) by Sebastian Raschka. The emphasis during the meetups will be to discuss key aspect of the Chapter being covering. Code focused discussions should be done over Discord. Raschka provides a step-by-step guide coding up your own foundation LLM ground up, spanning initial design and creation stages, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Pages being discussed: Please see the latest message (also pinned) in the #current-reading channel in our Discord chat space to see which pages we'll be reviewing in this session. Please note that the session is not recorded and participants are responsible for obtaining their own copy of the text. Discord joining instructions: Buy the book (affiliate links): Book overview: In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You'll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to:
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Build a Large Language Model (From Scratch)
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Build a Large Language Model (From Scratch)
2025-05-20 · 19:00
We are going through Hands-On Build a Large Language Models (from Scratch) by Sebastian Raschka. The emphasis during the meetups will be to discuss key aspect of the Chapter being covering. Code focused discussions should be done over Discord. Raschka provides a step-by-step guide coding up your own foundation LLM ground up, spanning initial design and creation stages, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Pages being discussed: Please see the latest message (also pinned) in the #current-reading channel in our Discord chat space to see which pages we'll be reviewing in this session. Please note that the session is not recorded and participants are responsible for obtaining their own copy of the text. Discord joining instructions: Buy the book (affiliate links): Book overview: In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You'll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to:
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Build a Large Language Model (From Scratch)
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Build a Large Language Model (From Scratch)
2025-05-13 · 19:00
We are going through Hands-On Build a Large Language Models (from Scratch) by Sebastian Raschka. The emphasis during the meetups will be to discuss key aspect of the Chapter being covering. Code focused discussions should be done over Discord. Raschka provides a step-by-step guide coding up your own foundation LLM ground up, spanning initial design and creation stages, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Pages being discussed: Please see the latest message (also pinned) in the #current-reading channel in our Discord chat space to see which pages we'll be reviewing in this session. Please note that the session is not recorded and participants are responsible for obtaining their own copy of the text. Discord joining instructions: Buy the book (affiliate links): Book overview: In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You'll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to:
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Build a Large Language Model (From Scratch)
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Build a Large Language Model (From Scratch)
2025-05-06 · 19:00
We are going through Hands-On Build a Large Language Models (from Scratch) by Sebastian Raschka. The emphasis during the meetups will be to discuss key aspect of the Chapter being covering. Code focused discussions should be done over Discord. Raschka provides a step-by-step guide coding up your own foundation LLM ground up, spanning initial design and creation stages, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Pages being discussed: Please see the latest message (also pinned) in the #current-reading channel in our Discord chat space to see which pages we'll be reviewing in this session. Please note that the session is not recorded and participants are responsible for obtaining their own copy of the text. Discord joining instructions: Buy the book (affiliate links): Book overview: In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You'll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to:
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Build a Large Language Model (From Scratch)
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Pré-entrainement et finetuning des LLMs à partir de zéro (from scratch)
2025-03-09 · 15:00
Plongez dans l'univers fascinant des modèles de langage en participant à notre série d'événements ! Basée sur l'ouvrage "Build a Large Language Model from Scratch" de Sebastian Raschka (https://www.manning.com/books/build-a-large-language-model-from-scratch), cette série d'événements se focalise sur deux aspects fondamentaux : 1. La création complète d'un modèle de langage depuis zéro (from scratch) 2. Les techniques d'ajustement (fine-tuning) d'un modèle pré-entraîné Format des sessions : - Chaque rencontre se concentre sur un chapitre spécifique du livre - Les sessions alternent entre présentation théorique et mise en pratique - Un temps dédié aux questions et discussions permet d'approfondir les concepts complexes Pour tirer le meilleur parti de cette expérience, nous vous encourageons à : - Lire le chapitre correspondant avant chaque session - Préparer vos questions et observations - Partager vos réflexions lors des discussions de groupe Prérequis techniques : - Maîtrise de la programmation orientée objet en Python - Curiosité et envie d'explorer les mécanismes internes des modèles de langage Cette dernière session est spéciale car nous avons l'honneur d'accueillir l'auteur du livre lui-même Sebastian Raschka qui nous prodiguera des conseils et nous fournira des recommandations pour aller plus loin en Machine Learning, particulièrement en Traitement Automatique des langues et LLMs. Nous aurons également l'occasion de lui poser nos questions. Programme de la dernière session de la série ce dimanche 09/03/2025 : 1. Présentation du chapitre 7. Finetuning to follow instructions. Comment ajuster un LLM pré-entraîné pour qu'il soit à mesure de suivre des instructions : 16h00 à 17h00 2. Questions et réponses sur le chapitre 7 : 17h00 à 17h10 3. Conseils et recommandations de Sebastian Raschka pour aller plus loin : 17h10 à 17h30 4. Questions et réponses avec Sebastian Raschka : 17h30 à 18h00 Rejoignez-nous pour cette aventure passionnante au cœur des technologies qui façonnent l'avenir du traitement du langage naturel ! |
Pré-entrainement et finetuning des LLMs à partir de zéro (from scratch)
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Pré-entrainement et finetuning des LLMs à partir de zéro (from scratch)
2025-03-02 · 15:00
Plongez dans l'univers fascinant des modèles de langage en participant à notre série d'événements ! Basée sur l'ouvrage "Building a Large Language Model from Scratch" de Sebastian Raschka (https://www.manning.com/books/build-a-large-language-model-from-scratch), cette série d'événements se focalisera sur deux aspects fondamentaux : 1. La création complète d'un modèle de langage depuis zéro (from scratch) 2. Les techniques d'ajustement (fine-tuning) d'un modèle pré-entraîné Format des sessions : - Chaque rencontre se concentre sur un chapitre spécifique du livre - Les sessions alternent entre présentation théorique et mise en pratique - Un temps dédié aux questions et discussions permet d'approfondir les concepts complexes Pour tirer le meilleur parti de cette expérience, nous vous encourageons à : - Lire le chapitre correspondant avant chaque session - Préparer vos questions et observations - Partager vos réflexions lors des discussions de groupe Prérequis techniques : - Maîtrise de la programmation orientée objet en Python - Curiosité et envie d'explorer les mécanismes internes des modèles de langage Cette série d'événements vous permettra non seulement de comprendre les principes théoriques, mais aussi d'acquérir une expérience pratique dans la construction et l'optimisation des modèles de langage, des compétences particulièrement recherchées dans le domaine de l'IA. Cette semaine nous verrons comment faire du finetuning des LLMs pour la classification. Comment télécharger un modèle pré-entraîné et l'adapter à vos propres données. Rejoignez-nous pour cette aventure passionnante au cœur des technologies qui façonnent l'avenir du traitement du langage naturel ! |
Pré-entrainement et finetuning des LLMs à partir de zéro (from scratch)
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Fine-tuning a Pre-trained Language Model
2025-02-23 · 15:00
Session on 2025-02-23 focusing on fine-tuning techniques for a pre-trained model, in line with the series described in the description. |
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From scratch: Building a Language Model
2025-02-23 · 15:00
Session on 2025-02-23 focusing on the complete creation of a language model from zero, based on 'Building a Large Language Model from Scratch' by Sebastian Raschka. The session is chapter-based and alternates between theoretical and practical components. |
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Pré-entrainement et finetuning des LLMs à partir de zéro (from scratch)
2025-02-16 · 15:00
Plongez dans l'univers fascinant des modèles de langage en participant à notre série d'événements ! Basée sur l'ouvrage "Building a Large Language Model from Scratch" de Sebastian Raschka (https://www.manning.com/books/build-a-large-language-model-from-scratch), cette série d'événements se focalisera sur deux aspects fondamentaux : 1. La création complète d'un modèle de langage depuis zéro (from scratch) 2. Les techniques d'ajustement (fine-tuning) d'un modèle pré-entraîné Format des sessions : - Chaque rencontre se concentre sur un chapitre spécifique du livre - Les sessions alternent entre présentation théorique et mise en pratique - Un temps dédié aux questions et discussions permet d'approfondir les concepts complexes Pour tirer le meilleur parti de cette expérience, nous vous encourageons à : - Lire le chapitre correspondant avant chaque session - Préparer vos questions et observations - Partager vos réflexions lors des discussions de groupe Prérequis techniques : - Maîtrise de la programmation orientée objet en Python - Curiosité et envie d'explorer les mécanismes internes des modèles de langage - Un ordinateur personnel pour pratiquer (pas besoin des GPUs. Les exemples du livre sont simplifiés pour permettre une exécution sur CPU) Cette série d'événements vous permettra non seulement de comprendre les principes théoriques, mais aussi d'acquérir une expérience pratique dans la construction et l'optimisation des modèles de langage, des compétences particulièrement recherchées dans le domaine de l'IA. Rejoignez-nous pour cette aventure passionnante au cœur des technologies qui façonnent l'avenir du traitement du langage naturel ! |
Pré-entrainement et finetuning des LLMs à partir de zéro (from scratch)
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Pré-entrainement et finetuning des LLMs à partir de zéro (from scratch)
2025-02-09 · 15:00
Plongez dans l'univers fascinant des modèles de langage en participant à notre série d'événements ! Basée sur l'ouvrage "Building a Large Language Model from Scratch" de Sebastian Raschka (https://www.manning.com/books/build-a-large-language-model-from-scratch), cette série d'événements se focalisera sur deux aspects fondamentaux : 1. La création complète d'un modèle de langage depuis zéro (from scratch) 2. Les techniques d'ajustement (fine-tuning) d'un modèle pré-entraîné Format des sessions : - Chaque rencontre se concentre sur un chapitre spécifique du livre - Les sessions alternent entre présentation théorique et mise en pratique - Un temps dédié aux questions et discussions permet d'approfondir les concepts complexes Pour tirer le meilleur parti de cette expérience, nous vous encourageons à : - Lire le chapitre correspondant avant chaque session - Préparer vos questions et observations - Partager vos réflexions lors des discussions de groupe Prérequis techniques : - Maîtrise de la programmation orientée objet en Python - Curiosité et envie d'explorer les mécanismes internes des modèles de langage - Un ordinateur personnel pour pratiquer (pas besoin des GPUs. Les exemples du livre sont simplifiés pour permettre une exécution sur CPU) Cette série d'événements vous permettra non seulement de comprendre les principes théoriques, mais aussi d'acquérir une expérience pratique dans la construction et l'optimisation des modèles de langage, des compétences particulièrement recherchées dans le domaine de l'IA. Rejoignez-nous pour cette aventure passionnante au cœur des technologies qui façonnent l'avenir du traitement du langage naturel ! |
Pré-entrainement et finetuning des LLMs à partir de zéro (from scratch)
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Pré-entrainement et finetuning des LLMs à partir de zéro (from scratch)
2025-02-02 · 15:00
Plongez dans l'univers fascinant des modèles de langage en participant à notre série d'événements ! Basée sur l'ouvrage "Building a Large Language Model from Scratch" de Sebastian Raschka (https://www.manning.com/books/build-a-large-language-model-from-scratch), cette série d'événements se focalisera sur deux aspects fondamentaux : 1. La création complète d'un modèle de langage depuis zéro (from scratch) 2. Les techniques d'ajustement (fine-tuning) d'un modèle pré-entraîné Format des sessions : - Chaque rencontre se concentre sur un chapitre spécifique du livre - Les sessions alternent entre présentation théorique et mise en pratique - Un temps dédié aux questions et discussions permet d'approfondir les concepts complexes Pour tirer le meilleur parti de cette expérience, nous vous encourageons à : - Lire le chapitre correspondant avant chaque session - Préparer vos questions et observations - Partager vos réflexions lors des discussions de groupe Prérequis techniques : - Maîtrise de la programmation orientée objet en Python - Curiosité et envie d'explorer les mécanismes internes des modèles de langage - Un ordinateur personnel pour pratiquer (pas besoin des GPUs. Les exemples du livre sont simplifiés pour permettre une exécution sur CPU) Cette série d'événements vous permettra non seulement de comprendre les principes théoriques, mais aussi d'acquérir une expérience pratique dans la construction et l'optimisation des modèles de langage, des compétences particulièrement recherchées dans le domaine de l'IA. Rejoignez-nous pour cette aventure passionnante au cœur des technologies qui façonnent l'avenir du traitement du langage naturel ! |
Pré-entrainement et finetuning des LLMs à partir de zéro (from scratch)
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Pré-entrainement et finetuning des LLMs à partir de zéro (from scratch)
2025-01-26 · 15:00
Plongez dans l'univers fascinant des modèles de langage en participant à notre série d'événements ! Basée sur l'ouvrage "Building a Large Language Model from Scratch" de Sebastian Raschka (https://www.amazon.com/Build-Large-Language-Model-Scratch/dp/1633437167) , cette série d'événements se focalisera sur deux aspects fondamentaux : 1. La création complète d'un modèle de langage depuis zéro (from scratch) 2. Les techniques d'ajustement (fine-tuning) d'un modèle pré-entraîné Format des sessions : - Chaque rencontre se concentre sur un chapitre spécifique du livre - Les sessions alternent entre présentation théorique et mise en pratique - Un temps dédié aux questions et discussions permet d'approfondir les concepts complexes Pour tirer le meilleur parti de cette expérience, nous vous encourageons à : - Lire le chapitre correspondant avant chaque session - Préparer vos questions et observations - Partager vos réflexions lors des discussions de groupe Prérequis techniques : - Maîtrise de la programmation orientée objet en Python - Curiosité et envie d'explorer les mécanismes internes des modèles de langage - Un ordinateur personnel pour pratiquer (pas besoin des GPUs. Les exemples du livre sont simplifiés pour permettre une exécution sur CPU) Cette série d'événements vous permettra non seulement de comprendre les principes théoriques, mais aussi d'acquérir une expérience pratique dans la construction et l'optimisation des modèles de langage, des compétences particulièrement recherchées dans le domaine de l'IA. Rejoignez-nous pour cette aventure passionnante au cœur des technologies qui façonnent l'avenir du traitement du langage naturel ! |
Pré-entrainement et finetuning des LLMs à partir de zéro (from scratch)
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Machine Learning Q and AI
2024-04-16
Sebastian Raschka
– author
If you're ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of Machine Learning Q and AI will make things fast and easy for you, without a lot of mucking about. Born out of questions often fielded by author Sebastian Raschka, the direct, no-nonsense approach of this book makes advanced topics more accessible and genuinely engaging. Each brief, self-contained chapter journeys through a fundamental question in AI, unraveling it with clear explanations, diagrams, and hands-on exercises. WHAT'S INSIDE: FOCUSED CHAPTERS: Key questions in AI are answered concisely, and complex ideas are broken down into easily digestible parts. WIDE RANGE OF TOPICS: Raschka covers topics ranging from neural network architectures and model evaluation to computer vision and natural language processing. PRACTICAL APPLICATIONS: Learn techniques for enhancing model performance, fine-tuning large models, and more. You'll also explore how to: Manage the various sources of randomness in neural network training Differentiate between encoder and decoder architectures in large language models Reduce overfitting through data and model modifications Construct confidence intervals for classifiers and optimize models with limited labeled data Choose between different multi-GPU training paradigms and different types of generative AI models Understand performance metrics for natural language processing Make sense of the inductive biases in vision transformers If you've been on the hunt for the perfect resource to elevate your understanding of machine learning, Machine Learning Q and AI will make it easy for you to painlessly advance your knowledge beyond the basics. |
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Machine Learning with PyTorch and Scikit-Learn is a comprehensive resource for developers looking to dive deep into the world of machine learning. It introduces foundational concepts alongside practical implementations using Python and leading libraries such as PyTorch and Scikit-Learn. With well-explained techniques and real-world examples, you'll gain the knowledge needed to design, build, and optimize machine learning systems. What this Book will help me do Understand and apply core concepts in machine learning using Scikit-Learn. Develop and deploy deep learning models using PyTorch efficiently. Configure and optimize neural networks, transformers, and GANs for various applications. Handle and preprocess data effectively for building robust models. Follow best practices for model evaluation, tuning, and deployment. Author(s) Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili are experienced professionals in the field of machine learning with extensive teaching and writing backgrounds. They bring their expertise in Python and machine learning frameworks like PyTorch to provide both theoretical and practical insights helpful for learners. Their combined knowledge ensures a thorough and engaging learning experience suited for aspiring data scientists. Who is it for? This book is tailored for Python developers and data scientists eager to master machine learning and deep learning techniques. If you're familiar with Python programming and possess fundamental knowledge of calculus and linear algebra, you will find this book incredibly insightful. Whether you're entering the field or seeking to enhance your expertise, this resource caters to your professional growth in building advanced machine learning systems. |
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