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07:23
HuggingFace Agents: Self-Correcting SQL Agent to Chat with SQL Database
06:07
Language Models for Detecting & Masking PII
06:47
Easily build quality Knowledge Graphs From Text
05:08
Detect Copyrighted Content in LLM's Training Data
07:39
AWS Refchecker: Atomic Hallucination Detector
14:05
Fine-grained Framework Evaluating RAG: RAGChecker
15:54
Improve RAG Retrieval by Fine-tuning Embeddings Model
13:24
Jailbreaking & Prompt Injection: LLM Applications
07:09
Augmentoolkit: Convert Documents into Datasets to Fine-tune LLMs
11:41
🔥 Invisible Prompt Injection 🔥
09:25
Extract Data from Unstructured Documents: LlamaExtract
17:05
Finetune GPT-4o on Custom Datasets and Format
11:56
The Best Open Source LLM for Text Summarisation
14:25
How to Detect Attacks on AI ML Models: Adversarial Robustness Toolbox
09:30
Anthropic Claude - Prompt Caching
11:01
LLM-Guard: Secure Your AI Agents from Attacks
07:09
sarvam.ai - Multilingual Text & Speech Models
09:39
AI Model Scan: Detect Serialisation Attacks
13:10
LLM Guard: Controls and Guardrails for LLMs
16:42
Find the Best LLM for RAG Retrieval
14:36
SAM2: Detect Segments in Images Through Prompting
16:41
Code: Find The Best LLM for Querying SQL DB - Txt2SQL
09:46
The Best LLM for Querying SQL DB in Natural Language
08:32
Meta: Segment Anything 2
07:27
LLMs Evaluation Metrics: Text Summarisation
20:31
How to Detect LLM’s Vulnerabilities: Giskard
10:26
Anomaly Detection in Time Series Data: TimeGPT
15:45
TimeGPT: Forecasting Made Easy
17:49
Evaluate Your RAG System Performance: Giskard
12:08
Create Custom Datasets for Evaluating RAG Systems
20:04
Simulating a Pet Movement in a House: Markov Chain Simulations
33:16
Understand & Visualise Microsoft GraphRAG
05:56
Multilingual OCR - PaddleOCR
14:35
Generative RAG QA: Hybrid Databases SQL and Vector
10:16
GPT4All: Create RAGs without any coding & run LLMs locally
16:34
Microsoft GraphRAG: The Best RAG + KG Architecture
11:53
LLM QA: Hybrid Data Sources - SQL DB & Vector DB
16:14
RAG + Property Graphs: Hybrid Retrieval
16:36
Text to SQL: Prompting Strategies - Dynamic Few Shot Prompting
12:39
Extract Data from Emails using LLMs
13:26
Build a Multi-Modal RAG System
14:54
Convert Any Text into a Property Graph and use it for RAG
18:06
Adaptive RAG with Self-Reflection: Part-2
21:27
Build a Web App to Visualise RAG Data
31:30
Adaptive RAG with Self-Reflection: Part-1
21:16
Visualise RAG data
12:03
Build Image Chatbot: Chat with your images
11:53
Build a Reverse or Similar Image Search Engine
17:15
Embeddings: Understanding Vector Operations
07:10
Fitter: Which distribution fits my data?
11:27
Graph Data Science & Machine Learning: Product Recommendation using Node Embeddings
16:42
Graph Data Science & Machine Learning: Community Detection
11:12
Linear Mixed Models: Intuitive Understanding
16:17
Graph Data Science & Machine Learning: Centrality Algorithms
12:35
Deploy LLM Chatbot to Google Cloud Run
16:05
Build a Chatbot: Chat with any document using LLMs
15:49
LLMs Dynamic Few-shot Prompting: Langchain, Neo4J, Graph Database
13:54
Deploy ML Models using GCP Vertex AI
07:10
Deploy ML Models using GCP App Engine
13:50
Deploy ML Models using GCP Cloud Functions