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Sebastian Raschka

Sebastian Raschka

306 videos87.8K subscribers

ML/AI researcher & former stats professor turned LLM research engineer. Author of "Build a Large Language Model From Scratch" (amzn.to/4fqvn0D) & reasoning (mng.bz/Nwr7).

Recent Videos (50 of 306)

What I Learned From Implementing LLM Architectures From Scratch (And How to Get Started)

What I Learned From Implementing LLM Architectures From Scratch (And How to Get Started)

A Visual Tour of Modern LLM Architectures

A Visual Tour of Modern LLM Architectures

LLM Building Blocks & Transformer Alternatives

LLM Building Blocks & Transformer Alternatives

The Big LLM Architecture Comparison

The Big LLM Architecture Comparison

Build an LLM from Scratch 7: Instruction Finetuning

Build an LLM from Scratch 7: Instruction Finetuning

Build an LLM from Scratch 6: Finetuning for Classification

Build an LLM from Scratch 6: Finetuning for Classification

Build an LLM from Scratch 5: Pretraining on Unlabeled Data

Build an LLM from Scratch 5: Pretraining on Unlabeled Data

Build an LLM from Scratch 4: Implementing a GPT model from Scratch To Generate Text

Build an LLM from Scratch 4: Implementing a GPT model from Scratch To Generate Text

Build an LLM from Scratch 3: Coding attention mechanisms

Build an LLM from Scratch 3: Coding attention mechanisms

Build an LLM from Scratch 2: Working with text data

Build an LLM from Scratch 2: Working with text data

Build an LLM from Scratch 1: Set up your code environment

Build an LLM from Scratch 1: Set up your code environment

Reinforcement Learning with Human Feedback (RLHF) in 4 minutes

Reinforcement Learning with Human Feedback (RLHF) in 4 minutes

LLMs: A Journey Through Time and Architecture

LLMs: A Journey Through Time and Architecture

Building LLMs from the Ground Up: A 3-hour Coding Workshop

Building LLMs from the Ground Up: A 3-hour Coding Workshop

Understanding PyTorch Buffers

Understanding PyTorch Buffers

Developing an LLM: Building, Training, Finetuning

Developing an LLM: Building, Training, Finetuning

Managing Sources of Randomness When Training Deep Neural Networks

Managing Sources of Randomness When Training Deep Neural Networks

Insights from Finetuning LLMs with Low-Rank Adaptation

Insights from Finetuning LLMs with Low-Rank Adaptation

Finetuning Open-Source LLMs

Finetuning Open-Source LLMs

Scaling PyTorch Model Training With Minimal Code Changes

Scaling PyTorch Model Training With Minimal Code Changes

L13.5 What's The Difference Between Cross-Correlation And Convolution?

L13.5 What's The Difference Between Cross-Correlation And Convolution?

Conditional Ordinal Regression for Neural Networks (CORN) With Examples in PyTorch

Conditional Ordinal Regression for Neural Networks (CORN) With Examples in PyTorch

The Three Elements of PyTorch

The Three Elements of PyTorch

Ratings and Rankings -- Using Deep Learning When Class Labels Have A Natural Order

Ratings and Rankings -- Using Deep Learning When Class Labels Have A Natural Order

13.4.5 Sequential Feature Selection -- Code Examples (L13: Feature Selection)

13.4.5 Sequential Feature Selection -- Code Examples (L13: Feature Selection)

13.4.4 Sequential Feature Selection (L13: Feature Selection)

13.4.4 Sequential Feature Selection (L13: Feature Selection)

13.4.3 Feature Permutation Importance Code Examples (L13: Feature Selection)

13.4.3 Feature Permutation Importance Code Examples (L13: Feature Selection)

13.4.2 Feature Permutation Importance (L13: Feature Selection)

13.4.2 Feature Permutation Importance (L13: Feature Selection)

13.4.1 Recursive Feature Elimination (L13: Feature Selection)

13.4.1 Recursive Feature Elimination (L13: Feature Selection)

13.3.2 Decision Trees & Random Forest Feature Importance (L13: Feature Selection)

13.3.2 Decision Trees & Random Forest Feature Importance (L13: Feature Selection)

13.3.1 L1-regularized Logistic Regression as Embedded Feature Selection (L13: Feature Selection)

13.3.1 L1-regularized Logistic Regression as Embedded Feature Selection (L13: Feature Selection)

13.2 Filter Methods for Feature Selection -- Variance Threshold (L13: Feature Selection)

13.2 Filter Methods for Feature Selection -- Variance Threshold (L13: Feature Selection)

13.1 The Different Categories of Feature Selection (L13: Feature Selection)

13.1 The Different Categories of Feature Selection (L13: Feature Selection)

13.0 Introduction to Feature Selection (L13: Feature Selection)

13.0 Introduction to Feature Selection (L13: Feature Selection)

Introduction to Generative Adversarial Networks (Tutorial Recording at ISSDL 2021)

Introduction to Generative Adversarial Networks (Tutorial Recording at ISSDL 2021)

Designing Generative Adversarial Networks for Privacy-enhanced Face Recognition (Conference rec.)

Designing Generative Adversarial Networks for Privacy-enhanced Face Recognition (Conference rec.)

L19.5.2.2 GPT-v1: Generative Pre-Trained Transformer

L19.5.2.2 GPT-v1: Generative Pre-Trained Transformer

L19.5.2.4 GPT-v2: Language Models are Unsupervised Multitask Learners

L19.5.2.4 GPT-v2: Language Models are Unsupervised Multitask Learners

L19.5.2.7: Closing Words -- The Recent Growth of Language Transformers

L19.5.2.7: Closing Words -- The Recent Growth of Language Transformers

L19.5.2.6 BART: Combining Bidirectional and Auto-Regressive Transformers

L19.5.2.6 BART: Combining Bidirectional and Auto-Regressive Transformers

L19.5.2.5 GPT-v3: Language Models are Few-Shot Learners

L19.5.2.5 GPT-v3: Language Models are Few-Shot Learners

L19.6 DistilBert Movie Review Classifier in PyTorch -- Code Example

L19.6 DistilBert Movie Review Classifier in PyTorch -- Code Example

L19.5.2.3 BERT: Bidirectional Encoder Representations from Transformers

L19.5.2.3 BERT: Bidirectional Encoder Representations from Transformers

L19.5.2.1 Some Popular Transformer Models: BERT, GPT, and BART -- Overview

L19.5.2.1 Some Popular Transformer Models: BERT, GPT, and BART -- Overview

L19.5.1 The Transformer Architecture

L19.5.1 The Transformer Architecture

L19.4.3 Multi-Head Attention

L19.4.3 Multi-Head Attention

L19.4.2 Self-Attention and Scaled Dot-Product Attention

L19.4.2 Self-Attention and Scaled Dot-Product Attention

L19.4.1 Using Attention Without the RNN -- A Basic Form of Self-Attention

L19.4.1 Using Attention Without the RNN -- A Basic Form of Self-Attention

L19.3 RNNs with an Attention Mechanism

L19.3 RNNs with an Attention Mechanism

L19.2.1 Implementing a Character RNN in PyTorch (Concepts)

L19.2.1 Implementing a Character RNN in PyTorch (Concepts)

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