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50 ML Projects To Understand LLMs: Investigate transformer mechanisms through data analysis, visualization, and experimentation - Softcover
Inhaltsangabe
Most books teach you how to build LLMs from scratch or deploy them via APIs. This book uses guided machine learning projects to teach you how to understand, visualize, and investigate LLMs including GPT and BERT.
Key Features
- Each project is built around three learning goals: machine learning techniques, LLM mechanisms, and Python coding with data visualization.
- This is not a dense theoretical textbook; it's hands-on, practical, and project-oriented.
- You will learn how to measure, visualize, and manipulate the internal components of LLMs directly.
Book Description
Through 50 hands-on, guided projects solved in Python, you will investigate the internal mechanisms of large language models by treating their hidden states, attention patterns, and embeddings as data to analyze. Rather than accepting LLMs as black boxes, you will open them up, examine what's inside, and run experiments to understand why they behave the way they do. All projects are based on Python (using libraries such as NumPy, PyTorch, statsmodels, scikit-learn, Matplotlib, Pandas, and Seaborn) and come with full solutions and partial solution notebook files, so you can practice and improve your skills in data science, deep learning, data visualization, and scientific and statistical coding.
What you will learn
- Tokenization schemes and their statistical properties
- Embedding spaces: cosine similarity, semantic axes, and analogy vectors
- Output logits, softmax distributions, perplexity, and language biases
- Layer-by-layer transformer dynamics and dimensionality
- Attention mechanisms: QKV weights, attention scores, head ablation, and activation patching
- MLP subblocks: neuron tuning, mutual information, subspace analysis, and statistics-based causal manipulations
- Logit lens, indirect object identification, and causal tracing
Who this book is for
This book is for data scientists, ML engineers, and researchers who want to go beyond surface-level understanding of LLMs. Prior Python experience is required. Familiarity with machine learning or deep learning is helpful but not required — techniques are introduced as they arise throughout the projects.
Table of Contents
- Introductions
- Tokenization
- Embeddings
- Output logits
- Transformer outputs
- Attention
- MLP
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Über die Autorin bzw. den Autor
Mike X Cohen is an associate professor at the Radboud University Medical Center and the leader of the Synchronization in the Neural Systems research group. His research focuses on using state-of-the-art neuroscience methods to understand the mechanisms and implications of brain circuit dynamics and has been funded by government agencies in the US, Germany, Netherlands, and Europe, and by private institutions and medical centers. Mike has been teaching time series analysis, applied mathematics, and scientific programming for almost 20 years. He has published several textbooks on these topics and teaches a variety of real-life and online courses.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
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50 ML Projects To Understand LLMs: Investigate transformer mechanisms through data analysis, visualization, and experimentation (Paperback or Softback)
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Paperback. Zustand: New. Most books teach you how to build LLMs from scratch or deploy them via APIs. This book uses guided machine learning projects to teach you how to understand, visualize, and investigate LLMs including GPT and BERT.Key FeaturesEach project is built around three learning goals: machine learning techniques, LLM mechanisms, and Python coding with data visualization.This is not a dense theoretical textbook; it's hands-on, practical, and project-oriented.You will learn how to measure, visualize, and manipulate the internal components of LLMs directly.Book DescriptionThrough 50 hands-on, guided projects solved in Python, you will investigate the internal mechanisms of large language models by treating their hidden states, attention patterns, and embeddings as data to analyze. Rather than accepting LLMs as black boxes, you will open them up, examine what's inside, and run experiments to understand why they behave the way they do. All projects are based on Python (using libraries such as NumPy, PyTorch, statsmodels, scikit-learn, Matplotlib, Pandas, and Seaborn) and come with full solutions and partial solution notebook files, so you can practice and improve your skills in data science, deep learning, data visualization, and scientific and statistical coding.What you will learnTokenization schemes and their statistical propertiesEmbedding spaces: cosine similarity, semantic axes, and analogy vectorsOutput logits, softmax distributions, perplexity, and language biasesLayer-by-layer transformer dynamics and dimensionalityAttention mechanisms: QKV weights, attention scores, head ablation, and activation patchingMLP subblocks: neuron tuning, mutual information, subspace analysis, and statistics-based causal manipulationsLogit lens, indirect object identification, and causal tracingWho this book is forThis book is for data scientists, ML engineers, and researchers who want to go beyond surface-level understanding of LLMs. Prior Python experience is required. Familiarity with machine learning or deep learning is helpful but not required - techniques are introduced as they arise throughout the projects. Bestandsnummer des Verkäufers LU-9781808082559
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Paperback. Zustand: New. Most books teach you how to build LLMs from scratch or deploy them via APIs. This book uses guided machine learning projects to teach you how to understand, visualize, and investigate LLMs including GPT and BERT.Key FeaturesEach project is built around three learning goals: machine learning techniques, LLM mechanisms, and Python coding with data visualization.This is not a dense theoretical textbook; it's hands-on, practical, and project-oriented.You will learn how to measure, visualize, and manipulate the internal components of LLMs directly.Book DescriptionThrough 50 hands-on, guided projects solved in Python, you will investigate the internal mechanisms of large language models by treating their hidden states, attention patterns, and embeddings as data to analyze. Rather than accepting LLMs as black boxes, you will open them up, examine what's inside, and run experiments to understand why they behave the way they do. All projects are based on Python (using libraries such as NumPy, PyTorch, statsmodels, scikit-learn, Matplotlib, Pandas, and Seaborn) and come with full solutions and partial solution notebook files, so you can practice and improve your skills in data science, deep learning, data visualization, and scientific and statistical coding.What you will learnTokenization schemes and their statistical propertiesEmbedding spaces: cosine similarity, semantic axes, and analogy vectorsOutput logits, softmax distributions, perplexity, and language biasesLayer-by-layer transformer dynamics and dimensionalityAttention mechanisms: QKV weights, attention scores, head ablation, and activation patchingMLP subblocks: neuron tuning, mutual information, subspace analysis, and statistics-based causal manipulationsLogit lens, indirect object identification, and causal tracingWho this book is forThis book is for data scientists, ML engineers, and researchers who want to go beyond surface-level understanding of LLMs. Prior Python experience is required. Familiarity with machine learning or deep learning is helpful but not required - techniques are introduced as they arise throughout the projects. Bestandsnummer des Verkäufers LU-9781808082559
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Paperback. Zustand: new. Paperback. Most books teach you how to build LLMs from scratch or deploy them via APIs. This book uses guided machine learning projects to teach you how to understand, visualize, and investigate LLMs including GPT and BERT.Key FeaturesEach project is built around three learning goals: machine learning techniques, LLM mechanisms, and Python coding with data visualization.This is not a dense theoretical textbook; it's hands-on, practical, and project-oriented.You will learn how to measure, visualize, and manipulate the internal components of LLMs directly.Book DescriptionThrough 50 hands-on, guided projects solved in Python, you will investigate the internal mechanisms of large language models by treating their hidden states, attention patterns, and embeddings as data to analyze. Rather than accepting LLMs as black boxes, you will open them up, examine what's inside, and run experiments to understand why they behave the way they do. All projects are based on Python (using libraries such as NumPy, PyTorch, statsmodels, scikit-learn, Matplotlib, Pandas, and Seaborn) and come with full solutions and partial solution notebook files, so you can practice and improve your skills in data science, deep learning, data visualization, and scientific and statistical coding.What you will learnTokenization schemes and their statistical propertiesEmbedding spaces: cosine similarity, semantic axes, and analogy vectorsOutput logits, softmax distributions, perplexity, and language biasesLayer-by-layer transformer dynamics and dimensionalityAttention mechanisms: QKV weights, attention scores, head ablation, and activation patchingMLP subblocks: neuron tuning, mutual information, subspace analysis, and statistics-based causal manipulationsLogit lens, indirect object identification, and causal tracingWho this book is forThis book is for data scientists, ML engineers, and researchers who want to go beyond surface-level understanding of LLMs. Prior Python experience is required. Familiarity with machine learning or deep learning is helpful but not required techniques are introduced as they arise throughout the projects. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9781808082559
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Paperback. Zustand: New. Most books teach you how to build LLMs from scratch or deploy them via APIs. This book uses guided machine learning projects to teach you how to understand, visualize, and investigate LLMs including GPT and BERT.Key FeaturesEach project is built around three learning goals: machine learning techniques, LLM mechanisms, and Python coding with data visualization.This is not a dense theoretical textbook; it's hands-on, practical, and project-oriented.You will learn how to measure, visualize, and manipulate the internal components of LLMs directly.Book DescriptionThrough 50 hands-on, guided projects solved in Python, you will investigate the internal mechanisms of large language models by treating their hidden states, attention patterns, and embeddings as data to analyze. Rather than accepting LLMs as black boxes, you will open them up, examine what's inside, and run experiments to understand why they behave the way they do. All projects are based on Python (using libraries such as NumPy, PyTorch, statsmodels, scikit-learn, Matplotlib, Pandas, and Seaborn) and come with full solutions and partial solution notebook files, so you can practice and improve your skills in data science, deep learning, data visualization, and scientific and statistical coding.What you will learnTokenization schemes and their statistical propertiesEmbedding spaces: cosine similarity, semantic axes, and analogy vectorsOutput logits, softmax distributions, perplexity, and language biasesLayer-by-layer transformer dynamics and dimensionalityAttention mechanisms: QKV weights, attention scores, head ablation, and activation patchingMLP subblocks: neuron tuning, mutual information, subspace analysis, and statistics-based causal manipulationsLogit lens, indirect object identification, and causal tracingWho this book is forThis book is for data scientists, ML engineers, and researchers who want to go beyond surface-level understanding of LLMs. Prior Python experience is required. Familiarity with machine learning or deep learning is helpful but not required - techniques are introduced as they arise throughout the projects. Bestandsnummer des Verkäufers LU-9781808082559
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