Building Production-quality AI systems with MosaicAI
151 videos • 5,338 views • by Databricks
1
Mosaic AI Gateway - Secure and Govern your AI
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2
An Introduction to DBRX
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3
Demo: How Do I Use DBRX?
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How Databricks built the LLM DBRX and how you can build yours
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DBRX: A New Standard for Open Source LLMs
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How to use Large Language Models in Databricks SQL
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7
The Gen AI Payoff in 2024: Accenture Testimonial
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The Gen AI Payoff in 2024: Introduction with Naveen Rao
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The Gen AI Payoff in 2024: Finetuning Showcase + Demo
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10
The Gen AI Payoff in 2024: RAG Demo
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11
The Gen AI Payoff in 2024: ADP Testimonial
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12
Ali Ghodsi's Microsoft Ignite Demo: Data Intelligence Platform
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13
Architecting for an AI-Future with Databricks x AWS
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14
Thrivent consolidates with Databricks to improve analytics and explore AI
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15
Air India deploys generative AI across its operations with Databricks
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16
Deploying LLMs on Databricks Model Serving
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17
Deploying LLMs on Databricks Model Serving
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18
LLM2 Module 1 - Transformers | 1.6 Base/Foundation Models
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LLM2 Module 1 - Transformers | 1.4 Transformer Architectures
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LLM2 Module 4 - Multi-modal LMs | 4.3 Transformers Beyond Text
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21
LLM2 Module 3 - Deployment and Hardware | 3.2 Module Overview
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LLM2 Module 3 - Deployment and Hardware | 3.1 Introduction
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23
LLM2 Module 1 - Transformers | 1.7 Generative Pre-trained Transformer
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LLM2 Module 1 - Transformers | 1.5 The Attention Mechanism
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LLM2 Module 4 - Multi-modal LMs | 4.2 Module Overview
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26
LLM2 Foundation Models from the Ground Up | Primer
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LLM2 Module 4 - Multi-modal LMs | 4.8 Notebook
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LLM2 Module 4 - Multi-modal LMs | 4.4 Training Data for MLLMs
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29
LLM2 Module 1 - Transformers | 1.8 Notebook
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LLM2 Module 3 - Deployment and Hardware | 3.5 Multi-LLM Inferencing
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31
LLM2 Module 1 - Transformers | 1.3 The Transformer Block
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32
LLM2 Module 2 - Efficient Fine-Tuning | 2.2 Module Overview
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33
LLM2 Module 3 - Deployment and Hardware | 3.6 Current Best Practices
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34
LLM2 Module 1 - Transformers | 1.2 Module Overview
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35
LLM2 Module 1 - Transformers | 1.1 Introduction
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36
LLM2 Module 2 - Efficient Fine-Tuning | 2.4 Re-parameterizaion: LoRA
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37
LLM2 Module 4 - Multi-modal LMs | 4.5 Few-shot learning
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38
LLM2 Module 3 - Deployment and Hardware | 3.8 MosaicML Guest Lecture on Training LLMs from Scratch
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39
LLM2 Module 3 - Deployment and Hardware | 3.7 Notebook
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40
LLM2 Module 3 - Deployment and Hardware | 3.4 Improving Learning Efficiency
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41
LLM2 Module 2 - Efficient Fine-Tuning | 2.5 PEFT Limitations
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42
LLM2 Module 3 - Deployment and Hardware | 3.3 Improving Learning Efficiency
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43
LLM2 Module 4 - Multi-modal LMs | 4.7 Emerging Applications
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44
LLM2 Module 2 - Efficient Fine-Tuning | 2.1 Introduction
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45
LLM2 Module 4 - Multi-modal LMs | 4.1 Introduction
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46
LLM2 Module 4 - Multi-modal LMs | 4.6 Challenges and Alternative Architectures
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47
LLM2 Module 2 - Efficient Fine-Tuning | 2.3 PEFT and Soft Prompt
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48
LLM2 Module 2 - Efficient Fine-Tuning | 2.6 Data preparation best practices
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49
LLM2 Module 2 - Efficient Fine-Tuning | 2.7 Notebook
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50
Databricks Vector Search and Lakehouse Monitoring Demo
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51
Your LLM, Your Data, Your Infrastructure
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52
IFC's MALENA Provides Analytics for ESG Reviews in Emerging Markets Using NLP and LLMs
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53
Evaluating LLM-based Applications
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54
Sponsored by: Prophecy | Build a Generative AI App on Enterprise Data in 13 Minutes
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55
Testing Generative AI Models: What You Need to Know
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56
Generative AI at Scale Using GAN and Stable Diffusion
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57
Generative AI at Scale Using GAN and Stable Diffusion
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58
Key Insights From Running LLMs in Production From MLOps.Community
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59
Generative AI Fundamentals
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60
De-Risking Language Models for Faster Adoption
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61
Sponsored by: LTIMindtree | Fast-Track to Future with GenAI-Powered Lakehouse
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62
Navigating the Complexities of LLMs: Insights from Practitioners
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63
Sponsored: Prophecy | Rise Low-Code Lakehouse: How LLMs are Democratizing Data Engineering for All
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64
Public Sector Industry Forum
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65
Data Biases and Generative AI: A Practitioner Discussion
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66
LLM in Practice: How to Productionize Your LLMs
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67
PaLM 2: A Smaller, Faster and More Capable LLM
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68
Discuss How LLMs Will Change the Way We Work
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69
How We Made a Unified Talent Solution Using Databricks Machine Learning, Fine-Tuned LLM & Dolly 2.0
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70
Financial Services Industry Forum
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71
Building the Multi-Modal Future: Open Ecosystems and Data at Play
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72
How to Build LLMs on Your Company’s Data While on a Budget
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73
Advancements in Open Source LLM Tooling, Including MLflow
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74
Advancements in Open Source LLM Tooling, Including MLflow
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75
LLMOps: Everything You Need to Know to Manage LLMs
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76
LLMs: Internals, Hallucinations, and Applications | Data Brew | Episode 33
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77
Live from the Lakehouse: Machine Learning, LLM & market changes over the past decade & data strategy
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78
Live from the Lakehouse: LLMs, LangChain, and analytics engineering workflow with dbt Labs
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79
Live from the Lakehouse: Machine Learning, LLM, Delta Lake, and data engineering
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Live from the Lakehouse: LLMs, AutoML, modern data stacks: Ben Lorica, Conor Jensen, & Franco Patano
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81
Live from the Lakehouse: LLMs, LangChain, and analytics engineering workflow with dbt Labs
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82
Live from the Lakehouse: Developer relations, generative AI, and conference wrap-up
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83
Data + AI Summit Keynote, Wednesday Part 4 - Democratizing LLMs with MosaicML and Databricks
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84
LLM Module 6: LLMOps | 6.7 Notebook Demo
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LLM Module 6: LLMOps | 6.6 Summary
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LLM Module 6: LLMOps | 6.5 LLMOps Details
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LLM Module 6: LLMOps | 6.4 LLMOps
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LLM Module 6: LLMOps | 6.3 Traditional MLOps
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LLM Module 6: LLMOps | 6.2 Module Overview
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LLM Module 6: LLMOps | 6.1 Introduction
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91
LLM Module 5: Society and LLMs | 5.7.2 Notebook Demo Part 2
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92
LLM Module 5: Society and LLMs | 5.7.1 Notebook Demo Part 1
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93
LLM Module 5: Society and LLMs | 5.6 Summary
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94
LLM Module 5: Society and LLMs | 5.5 Mitigation Strategies
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95
LLM Module 5: Society and LLMs | 5.4 Hallucination
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LLM Module 5: Society and LLMs | 5.3 Risks and Limitations
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LLM Module 5: Society and LLMs | 5.2 Module Overview
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LLM Module 5: Society and LLMs | 5.1 Introduction
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99
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.13.2 Notebook Demo Part 2
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100
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.13.1 Notebook Demo Part 1
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101
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.12 Summary
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102
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.11 Guest Lecture Harrison Chase, LangChain Creator
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103
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.10 Task specific Evaluations
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104
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.9 Evaluating LLMs
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105
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.8 Dolly
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106
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.7 Fine Tuning: DIY
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LLM Module 4: Fine-tuning and Evaluating LLMs | 4.6 Fine Tuning: LLMs as a Service
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108
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.5 Fine Tuning: Instruction-following LLMs
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LLM Module 4: Fine-tuning and Evaluating LLMs | 4.4 Fine Tuning: Few-shot Learning
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LLM Module 4: Fine-tuning and Evaluating LLMs | 4.3 Applying Foundation LLMs
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111
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.2 Module Overview
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112
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.1 Introduction
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113
LLM Module 3 - Multi-stage Reasoning | 3.7.3 Notebook Demo Part 3
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114
LLM Module 3 - Multi-stage Reasoning | 3.7.2 Notebook Demo Part 2
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115
LLM Module 3 - Multi-stage Reasoning | 3.7.1 Notebook Demo Part 1
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LLM Module 3 - Multi-stage Reasoning | 3.6 Summary
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LLM Module 3 - Multi-stage Reasoning | 3.5 Agents
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118
LLM Module 3 - Multi-stage Reasoning | 3.4 LLM Chains
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119
LLM Module 3 - Multi-stage Reasoning | 3.3 Prompt Engineering
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120
LLM Module 3 - Multi-stage Reasoning | 3.2 Module Overview
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121
LLM Module 3 - Multi-stage Reasoning | 3.1 Introduction
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122
LLM Module 2 - Embeddings, Vector Databases, and Search | 2.10 Notebook Demo Weaviate (Optional)
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LLM Module 2 - Embeddings, Vector Databases, and Search | 2.9 Notebook Demo Pinecone (Optional)
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124
LLM Module 2 - Embeddings, Vector Databases, and Search | 2.8.2 Notebook Demo Part 2
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125
LLM Module 2 - Embeddings, Vector Databases, and Search | 2.8.1 Notebook Demo Part 1
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126
LLM Module 2 - Embeddings, Vector Databases, and Search | 2.7 Summary
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LLM Module 2 - Embeddings, Vector Databases, and Search | 2.6 Best Practices
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LLM Module 2 - Embeddings, Vector Databases, and Search | 2.5 Vector Stores
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LLM Module 2 - Embeddings, Vector Databases, and Search | 2.4 Filtering
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LLM Module 2 - Embeddings, Vector Databases, and Search | 2.3 How does Vector Search work
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131
LLM Module 2 - Embeddings, Vector Databases, and Search | 2.2 Module Overview
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LLM Module 2 - Embeddings, Vector Databases, and Search | 2.1 Introduction
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133
LLM Module 1 - Applications with LLMs | 1.9 Notebook Demo
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LLM Module 1 - Applications with LLMs | 1.8 Summary
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LLM Module 1 - Applications with LLMs | 1.7 Prompt Engineering
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LLM Module 1 - Applications with LLMs | 1.6 Prompts
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LLM Module 1 - Applications with LLMs | 1.5 NLP Tasks
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LLM Module 1 - Applications with LLMs | 1.4 Model Selection
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LLM Module 1 - Applications with LLMs | 1.3 Hugging Face
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LLM Module 1 - Applications with LLMs | 1.2 Module Overview
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LLM Module 1 - Applications with LLMs | 1.1 Introduction
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142
LLM Module 0 - Introduction | 0.4 Language Models
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LLM Module 0 - Introduction | 0.6 Word Embeddings
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LLM Module 0 - Introduction | 0.5 Tokenization
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LLM Module 0 - Introduction | 0.3 Primer
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LLM Module 0 - Introduction | 0.2 Why LLMs
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LLM Module 0 - Introduction | 0.1 Welcome
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LLM Module 0 - Introduction | 0.7 Summary
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LLM Module 0 - Introduction | 0.8 Introduction to Databricks
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LLM Module 0 - Introduction | 0.9 Install Datasets
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151
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