101 ways to solve neural search with Jina

Elvis Saravia · Intermediate ·📰 AI News & Updates ·5y ago
About the speaker: Pratik is an open-source NLP engineer passionate about search engines. He enjoys discussing practical solutions and writing on medium and substack. You can find more about him at www.pratik.ai Abstract: Search is one of the most used applications of NLP and has evolved tremendously with time. In this talk, we will discuss different ways to build neural search and touch on the below topics: - What is neural search? - Anatomy of neural search - Making neural search from scratch - Intro to Jina - Tenets of Jina - Jina adapters - Neural search use cases for text, audio and vid…
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1 How to contribute to dair.ai?
How to contribute to dair.ai?
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 2 dair-ai.github.io/_posts at master · dair-ai/dair-ai.github.io
dair-ai.github.io/_posts at master · dair-ai/dair-ai.github.io
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 4 101 ways to solve search (by Pratik Bhavsar)
101 ways to solve search (by Pratik Bhavsar)
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Sentiment Analysis: Key Milestones, Challenges and New Directions
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Discriminative Adversarial Search for Abstractive Summarization (by Thomas Scialom)
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 8 Question Understanding: COVID-Q: 1,600+ Questions about COVID-19
Question Understanding: COVID-Q: 1,600+ Questions about COVID-19
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 9 Getting Started with NLP
Getting Started with NLP
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 10 Building tools and frameworks for large-scale social media mining (by Dr. Juan M. Banda)
Building tools and frameworks for large-scale social media mining (by Dr. Juan M. Banda)
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 11 TextAttack: A Framework for Data Augmentation and Adversarial Training in NLP
TextAttack: A Framework for Data Augmentation and Adversarial Training in NLP
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 12 Dive into Deep Learning (Study Group): Introduction to Deep Learning | Session 1
Dive into Deep Learning (Study Group): Introduction to Deep Learning | Session 1
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 13 Dive into Deep Learning (Study Group): Multilayer Perceptrons | Session 4
Dive into Deep Learning (Study Group): Multilayer Perceptrons | Session 4
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 14 How I read and annotate ML papers
How I read and annotate ML papers
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 15 Keep Learning ML (Session 1) | DSV, CompLex, Modern tools for emotions
Keep Learning ML (Session 1) | DSV, CompLex, Modern tools for emotions
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 16 Dive into Deep Learning (Study Group): Preliminaries | Session 2
Dive into Deep Learning (Study Group): Preliminaries | Session 2
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 17 Keep Learning ML #2 | Language-conditioned policy learning, Effective ML Testing, EagerPy
Keep Learning ML #2 | Language-conditioned policy learning, Effective ML Testing, EagerPy
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 18 Dive into Deep Learning (Study Group): Linear Neural Networks | Session 3
Dive into Deep Learning (Study Group): Linear Neural Networks | Session 3
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 19 Dive into Deep Learning (Study Group): Multilayer Perceptrons | Session 4
Dive into Deep Learning (Study Group): Multilayer Perceptrons | Session 4
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 20 Keep Learning ML #3 | Contrastively Trained Structured World Models
Keep Learning ML #3 | Contrastively Trained Structured World Models
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 21 Dive into Deep Learning (Study Group): Deep Learning Computation with PyTorch | Session 5
Dive into Deep Learning (Study Group): Deep Learning Computation with PyTorch | Session 5
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 22 Dive into Deep Learning (Study Group): Convolutional Neural Networks | Session 6
Dive into Deep Learning (Study Group): Convolutional Neural Networks | Session 6
Elvis Saravia
 23 Dive into Deep Learning (Study Group): Modern CNNs | Session 7
Dive into Deep Learning (Study Group): Modern CNNs | Session 7
Elvis Saravia
 ▶ 101 ways to solve neural search with Jina
101 ways to solve neural search with Jina
Elvis Saravia
 25 (Hopefully-Reusable) Life Lessons for PhD Students in NLP
(Hopefully-Reusable) Life Lessons for PhD Students in NLP
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 26 How to save the world and forward your career in 5 easy steps | Women in NLP Talks
How to save the world and forward your career in 5 easy steps | Women in NLP Talks
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 27 Prompt Engineering Overview
Prompt Engineering Overview
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 28 Getting Started with the OpenAI Playground
Getting Started with the OpenAI Playground
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 29 LM-Guided Chain of Thought
LM-Guided Chain of Thought
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 30 Elements of a Prompt
Elements of a Prompt
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 31 Reasoning with Intermediate Revision and Search with LLMs #chatgpt #ai #llms #science #programming
Reasoning with Intermediate Revision and Search with LLMs #chatgpt #ai #llms #science #programming
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 32 General Tips for Designing Prompts
General Tips for Designing Prompts
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 33 Efficient Infinite Context Transformers #ai #machinelearning #research #llms #science
Efficient Infinite Context Transformers #ai #machinelearning #research #llms #science
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 34 Best Practices and Lessons Learned on Synthetic Data for Language Models #ai #machinelearning #genai
Best Practices and Lessons Learned on Synthetic Data for Language Models #ai #machinelearning #genai
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 35 Reducing Hallucinations in Structured Outputs via RAG #chatgpt #ai #llms #programming
Reducing Hallucinations in Structured Outputs via RAG #chatgpt #ai #llms #programming
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 36 Basic Prompt Examples for LLMs
Basic Prompt Examples for LLMs
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 37 LLM In Context Recall is Prompt Dependent #llms #ai #chatgpt #machinelearning
LLM In Context Recall is Prompt Dependent #llms #ai #chatgpt #machinelearning
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 38 Zero-shot Prompting Explained
Zero-shot Prompting Explained
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 39 RAG Faithfulness #llms #ai #gpt4
RAG Faithfulness #llms #ai #gpt4
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 40 Understanding LLM Settings
Understanding LLM Settings
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 41 Llama 3 is here! | First impressions and thoughts
Llama 3 is here! | First impressions and thoughts
Elvis Saravia
 42 Llama 3 is Here! #ai #llms #llama3
Llama 3 is Here! #ai #llms #llama3
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 43 Microsoft introduces Phi-3 | The most capable small language model?
Microsoft introduces Phi-3 | The most capable small language model?
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 44 Microsoft introduces Phi-3! #ai #llms #microsoft
Microsoft introduces Phi-3! #ai #llms #microsoft
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 45 Make Your LLM Fully Utilize the Context #ai #llms #machinelearning
Make Your LLM Fully Utilize the Context #ai #llms #machinelearning
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 46 When to Retrieve? #ai #llms #machinelearning
When to Retrieve? #ai #llms #machinelearning
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 47 Training an LLM to effectively use information retrieval
Training an LLM to effectively use information retrieval
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 48 State-of-the-art open-source LLM judges #ai #machinelearning #gpt4
State-of-the-art open-source LLM judges #ai #machinelearning #gpt4
Elvis Saravia
 49 Better and Faster LLMs via Multi-token Prediction
Better and Faster LLMs via Multi-token Prediction
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 50 AlphaMath Almost Zero #ai #science #machinelearning
AlphaMath Almost Zero #ai #science #machinelearning
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 51 SWE-Agent | An LLM-based Software Engineering Agent
SWE-Agent | An LLM-based Software Engineering Agent
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 52 [LLM NEWS] AlphaFold 3, xLSTM, OpenAI's Model Spec, DeepSeek-V2, OpenDevin CodeAct 1.0
[LLM NEWS] AlphaFold 3, xLSTM, OpenAI's Model Spec, DeepSeek-V2, OpenDevin CodeAct 1.0
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 53 LLM-powered tool for web scraping #ai #chatgpt #engineering
LLM-powered tool for web scraping #ai #chatgpt #engineering
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 54 Learn about LLMs in this NEW course #ai #chatgpt #engineering
Learn about LLMs in this NEW course #ai #chatgpt #engineering
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 55 [LLM NEWS] KANs, Gemma 10M Context, OpenAI Updates?, Automatic Prompt Engineering, Tokenizer Arena
[LLM NEWS] KANs, Gemma 10M Context, OpenAI Updates?, Automatic Prompt Engineering, Tokenizer Arena
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 56 [LLM News] GPT4-o, Project Astra, Veo, Copilot+ PCs, Gemini 1.5 Flash, Chameleon
[LLM News] GPT4-o, Project Astra, Veo, Copilot+ PCs, Gemini 1.5 Flash, Chameleon
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 57 Enhancing Answer Selection in LLMs #ai #machinelearning #engineering
Enhancing Answer Selection in LLMs #ai #machinelearning #engineering
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 58 Exploring Capabilities of Long-Context LLMs
Exploring Capabilities of Long-Context LLMs
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 59 On exploring LLMs #ai #promptengineering #chatgpt
On exploring LLMs #ai #promptengineering #chatgpt
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 60 Transformers Can Do Arithmetic with the Right Embeddings #ai #machinelearning #engineering
Transformers Can Do Arithmetic with the Right Embeddings #ai #machinelearning #engineering
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