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Build a Large Language Model (From Scratch)
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Build a Large Language Model (From Scratch)
This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning.
Build a Large Language Model (From Scratch)
Item #: 127561311

Build a Large Language Model (From Scratch)

Item #: 127561311

PKR 21011

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What Stands Out

Comprehensive Guide
This book offers an in-depth, step-by-step approach to building a large language model from scratch, making complex concepts accessible for both beginners and professionals.
Practical Examples
Includes hands-on examples and coding exercises that enhance learning, enabling readers to directly apply concepts and solidify their understanding of language model development.
Expert Insights
Authored by industry experts, it provides insider knowledge and best practices that keep readers ahead in the rapidly evolving field of AI and machine learning.

Product Details

Shop Build a Large Language Model (From Scratch) online at a best price in Pakistan. 1633437167
  • Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up! 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: Plan and code all the parts of an LLMPrepare a dataset suitable for LLM trainingFine-tune LLMs for text classification and with your own dataUse human feedback to ensure your LLM follows instructionsLoad pretrained weights into an LLM Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you'll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant. About the technology Physicist Richard P. Feynman reportedly said, “I don't understand anything I can't build.” Based on this same powerful principle, bestselling author Sebastian Raschka guides you step by step as you build a GPT-style LLM that you can run on your laptop. This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning.
Publisher Manning Publications
Publication date 1 Nov. 2024
Edition 1st
Language English
Print length 368 pages
ISBN-10 1633437167
ISBN-13 978-1633437166
Item weight 612 g
Dimensions 18.73 x 1.78 x 23.5 cm

Who Should Buy?

Suitable For
  • AI Enthusiasts

    This product is ideal for individuals keen on understanding and building language models from foundational principles.

  • Students in AI

    Perfect for students pursuing degrees related to AI who want practical experience in language model development.

  • Industry Professionals

    Structured for tech professionals wanting to deepen their knowledge in natural language processing and AI applications.

Not Suitable For
  • Casual Learners

    Individuals looking for light reading or general knowledge won’t find this technical guide engaging or suitable.

Product Description

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Customer Questions & Answers

  • Question: What is 'Build a Large Language Model From Scratch'?

    Answer: 'Build a Large Language Model From Scratch' is a comprehensive guide or course focused on teaching users how to develop and implement a large language model using machine learning techniques. It typically includes theoretical concepts as well as practical coding examples.
    Category: overview
    Confidence: medium
  • Question: What do I need to get started with this guide?

    Answer: To get started, you will need a good understanding of programming, specifically in Python, and basic knowledge of machine learning concepts. Additionally, access to suitable hardware for training large models, like a GPU or cloud computing resources, is recommended.
    Category: setup
    Confidence: medium
  • Question: What programming languages or tools are used?

    Answer: The guide primarily uses Python as the programming language alongside popular libraries such as TensorFlow or PyTorch for building the model. Familiarity with these tools will enhance your learning experience.
    Category: specs
    Confidence: medium
  • Question: How is the model trained?

    Answer: The model is trained using large datasets through techniques like supervised learning, fine-tuning, and optimization algorithms. The process involves feeding data into the model and adjusting parameters based on the output accuracy.
    Category: usage
    Confidence: medium
  • Question: Are there any prerequisites before starting?

    Answer: Yes, prerequisites include a foundational understanding of machine learning principles, programming skills in Python, and familiarity with data processing techniques. These basics are essential to navigate the materials effectively.
    Category: setup
    Confidence: medium
  • Question: What kind of hardware is recommended?

    Answer: For optimal performance, a machine with a powerful GPU is recommended, as training large language models can be computationally intensive. Cloud-based platforms can also be considered if local resources are limited.
    Category: power
    Confidence: medium
  • Question: How can I clean and preprocess data for the model?

    Answer: Cleaning and preprocessing data typically involves removing noise, handling missing values, and normalizing text. Techniques may include tokenization, stemming, and lemmatization to prepare the dataset for training.
    Category: maintenance
    Confidence: medium
  • Question: What safety precautions should I consider?

    Answer: When building and deploying large language models, it is important to ensure data privacy and security, especially if using sensitive information. Ethical considerations regarding model bias and misinformation should also be addressed.
    Category: safety
    Confidence: medium
  • Question: What should I do if the model produces inaccurate results?

    Answer: If the model produces inaccurate results, consider revisiting the training data for quality issues, adjusting model parameters, or experimenting with different architectures. Additionally, implementing validation techniques can help identify problems early.
    Category: troubleshooting
    Confidence: medium
  • Question: Is there a warranty or return policy for the guide?

    Answer: Typically, digital guides or courses do not come with a warranty, but return policies may vary depending on the platform where it is purchased. It's advisable to check the terms before making a purchase.
    Category: returns
    Confidence: medium
  • Question: Where can I purchase this guide?

    Answer: The guide can usually be found on educational platforms or e-commerce sites that specialize in technology and machine learning resources. Availability may vary by region.
    Category: availability
    Confidence: medium
  • Question: Are there any software or app recommendations to assist learning?

    Answer: While no specific software is mandated, using IDEs like Jupyter Notebook or PyCharm can greatly enhance the coding experience. Additional online courses and forums may also provide useful supplementary materials.
    Category: software
    Confidence: medium
  • Question: What are the differences between versions of this guide?

    Answer: If multiple versions exist, the differences might include updated content reflecting the latest advancements in machine learning or language model research. Always check the version details for the most current information.
    Category: other
    Confidence: medium
  • Question: How long does it take to build a language model?

    Answer: The time required to build a language model varies widely based on the complexity of the model, the size of the dataset, and the computational resources available. It could range from a few days to several weeks.
    Category: usage
    Confidence: medium
  • Question: What are the common challenges in building a language model?

    Answer: Common challenges include managing vast amounts of training data, tuning model parameters effectively, and addressing issues related to model biases and overfitting. Staying updated with the latest methodologies can help mitigate these challenges.
    Category: other
    Confidence: medium

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PKR 21011

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Features & Benefits

  • Learn to build large language models (LLMs) from scratch.
  • Step-by-step guidance from renowned author Sebastian Raschka.
  • Hands-on approach covering planning, coding, training, and fine-tuning.
  • Develop your own LLM that runs on an ordinary laptop.
  • Includes a free eBook with the print purchase for easy access.
  • Gain a deep understanding of LLMs and their customization methods.

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