Analytics Vidhya

JUNE 11, 2024

Introduction Fine-tuning enables large language models to better align with specific tasks, teach new facts, and incorporate new information. Fine-tuning significantly improves performance compared to prompting, typically surpassing larger models due to its speed and cost-effectiveness.

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Analytics Vidhya

JULY 19, 2024

Introduction In an era where artificial intelligence is reshaping industries, controlling the power of Large Language Models (LLMs) has become crucial for innovation and efficiency.

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Analytics Vidhya

SEPTEMBER 11, 2023

It will be engineered to optimize decision-making and enhance performance in real-world complex systems. Introduction Reinforcement Learning from Human Factors/feedback (RLHF) is an emerging field that combines the principles of RL plus human feedback.

Optimization 353

Optimization Strategy Data-driven Interactive 353

Analytics Vidhya

AUGUST 23, 2021

This article was published as a part of the Data Science Blogathon Building a simple Machine Learning model using Pytorch from scratch. Image by my great learning Introduction Gradient descent is an optimization algorithm that is used to train machine learning models and is now used in a neural network.

Machine Learning 400

Machine Learning Modeling Data Science Publishing 400

Analytics Vidhya

SEPTEMBER 29, 2021

This article was published as a part of the Data Science Blogathon Dear readers, In this blog, we will build a random forest classifier(RFClassifier) model to detect breast cancer using this dataset from Kaggle. The post A Hands-On Discussion on Hyperparameter Optimization Techniques appeared first on Analytics Vidhya.

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Analytics Vidhya

MAY 9, 2024

They actively improve the coding process by automating routine tasks, optimizing […] The post IBM Granite Code Models: A Family of Open Foundation Models for Code Intelligence appeared first on Analytics Vidhya. Most of today’s technologies support coding by harnessing the power of artificial intelligence (AI).

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Analytics Vidhya

AUGUST 28, 2024

AdalFlow provides a unified library with strong string processing, flexible tools, multiple output formats, and model monitoring like […] The post Optimizing LLM Tasks with AdalFlow: Achieving Efficiency with Minimal Abstraction appeared first on Analytics Vidhya.

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Analytics Vidhya

MARCH 28, 2024

Introduction Large Language models (LLMs) can generate coherent and contextually relevant text since they are trained on extensive datasets and leveraging billions of parameters. This immense scale endows LLMs with emergent properties, such as nuanced understanding and generation capabilities across domains surpassing simpler models.

Optimization 305

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Analytics Vidhya

MAY 15, 2023

Microsoft AI Research has recently introduced a new framework called Automatic Prompt Optimization (APO) to significantly improve the performance of large language models (LLMs). This framework is designed to help users create better prompts with minimal manual intervention & optimize prompt engineering for better results.

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Analytics Vidhya

JANUARY 15, 2024

In the dynamic realm of language model development, a recent groundbreaking paper titled “Direct Preference Optimization (DPO)” by Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Chris Manning, and Chelsea Finn, has captured the attention of AI luminaries like Andrew Ng.

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Analytics Vidhya

SEPTEMBER 21, 2023

Introduction Large language models (LLMs) are prominent innovation pillars in the ever-evolving landscape of artificial intelligence. These models, like GPT-3, have showcased impressive natural language processing and content generation capabilities.

Modeling 341

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Analytics Vidhya

NOVEMBER 30, 2021

This article was published as a part of the Data Science Blogathon A comprehensive guide for finding the best hyper-parameter for your model efficiently. Tuning hyperparameter is more efficient with Bayesian optimized algorithms compared to Brute-force algorithms. Introduction Optimizing ML models […].

Modeling 336

Modeling Optimization Data Science Publishing 336

Analytics Vidhya

APRIL 26, 2024

Apple has recently unveiled OpenELM, a family of open-source language models optimized for on-device processing. This model has been made open source, encouraging free usage and collaboration. Apple’s move towards transparency aims to empower developers and researchers while enhancing user privacy.

Modeling 328

Modeling Optimization Analytics 328

Analytics Vidhya

NOVEMBER 30, 2023

In a development, Amazon Bedrock introduces the ability to assess, compare, and choose the optimal foundation models (FMs) tailored to your specific need. The Model Evaluation feature, now in preview, empowers developers with a range of evaluation tools, offering both automatic and human benchmarking options.

Modeling 317

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Analytics Vidhya

OCTOBER 30, 2023

The most challenging part of integrating AI into an application is […] The post Mastering AI Optimization and Deployment with Intel’s OpenVINO Toolkit appeared first on Analytics Vidhya. Enterprises and businesses believe in integrating reliable and responsible AI in their application to generate more revenue.

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Analytics Vidhya

JANUARY 29, 2024

But, here’s the problem: this encyclopedia is huge and requires significant time and effort […] The post Optimizing Neural Networks: Unveiling the Power of Quantization Techniques appeared first on Analytics Vidhya. Now, this friend has a precise way of doing things, like he has a dictionary in his head.

Optimization 314

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Analytics Vidhya

OCTOBER 7, 2021

This article was published as a part of the Data Science Blogathon Overview of Model Deployment Using Heroku Image 1 One of the most prevalent misunderstandings and mistakes for a failed ML project is spending a significant amount of time optimizing the ML model.

Machine Learning 306

Machine Learning Modeling Data Science Publishing 306

O'Reilly on Data

JANUARY 9, 2024

They had bugs, particularly if they were optimizing your code (were optimizing compilers a forerunner of AI?). With the current models, every time you generate code, you’re likely to get something different. An updated model is likely to produce completely different source code. The process isn’t repeatable.

Modeling 288

Modeling Software Testing Optimization 288

Analytics Vidhya

JUNE 1, 2023

With franchise leagues like IPL and BBL, teams rely on statistical models and tools for competitive edge. This article explores how data analytics optimizes strategies by leveraging player performances and opposition weaknesses. Introduction Cricket embraces data analytics for strategic advantage.

Predictive Modeling 291

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Analytics Vidhya

AUGUST 27, 2024

Introduction Diving into the world of AI models, language models and other software that can be applied in real tasks like virtual assistance and content creation are very popular. However, there is still a lot to explore with image-to-text models.

Modeling 269

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Analytics Vidhya

MAY 15, 2022

In order to develop any deep learning model, one must decide on the most optimal values of […]. The post Impact of Hyperparameters on a Deep Learning Model appeared first on Analytics Vidhya.

Deep Learning 271

Deep Learning Modeling Data Science Publishing 271

Analytics Vidhya

NOVEMBER 28, 2024

Large Language Models (LLMs) have revolutionized characteristic dialect preparing (NLP), fueling applications extending from summarization and interpretation to conversational operators and retrieval-based frameworks.

Optimization 203

Optimization Modeling Analytics 203

Analytics Vidhya

APRIL 26, 2022

The post How to Optimize ROI Using Customer Lifetime Value? More customers translate into higher revenues and better profitability in the long run. But in recent times, and due to heavy competition customer loyalty to a brand has […]. appeared first on Analytics Vidhya.

ROI 271

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Analytics Vidhya

AUGUST 26, 2023

In this article, we will explore how PEFT methods optimize the adaptation of Large Language Models (LLMs) to specific tasks. We will unravel the advantages and disadvantages of PEFT, […] The post Parameter-Efficient Fine-Tuning of Large Language Models with LoRA and QLoRA appeared first on Analytics Vidhya.

Modeling 269

Modeling Optimization Analytics IT 269

KDnuggets

AUGUST 9, 2024

Optimize LLM performance and scalability using techniques like prompt engineering, retrieval augmentation, fine-tuning, model pruning, quantization, distillation, load balancing, sharding, and caching.

Optimization 157

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Analytics Vidhya

NOVEMBER 24, 2023

Leveraging Large Language Models (LLMs) such as OpenAI’s GPT-3.5 for project optimization in customer support introduces a unique perspective. Introduction In the fast-paced world of customer support efficiency and responsiveness are paramount.

Optimization 323

Optimization Modeling Analytics Management 323

Analytics Vidhya

APRIL 11, 2024

Employing a tiered approach empowers individuals and organizations to select the optimal model aligning with their specific requirements. Introduction Anthropic’s Claude 3 API ushers in a new era of artificial intelligence, offering a comprehensive suite of AI assistants tailored to diverse user needs.

Optimization 314

Optimization Modeling Analytics Deep Learning 314

David Menninger's Analyst Perspectives

SEPTEMBER 16, 2021

It can support AI/ML processes with data preparation, model validation, results visualization and model optimization. Rapidminer Studio is its visual workflow designer for the creation of predictive models.

Data Science 341

Data Science Data Lake Data mining Deep Learning 341

Analytics Vidhya

NOVEMBER 19, 2021

In the model-building phase of any supervised machine learning project, we train a model with the aim to learn the optimal values for all the weights and biases from labeled examples. If we use the same labeled examples for testing our model […]. This is article was published as a part of the Data Science Blogathon.

Machine Learning 361

Machine Learning Testing Data Science Publishing 361

Analytics Vidhya

MAY 7, 2024

However, inaccurate retrieval can lead to sub-optimal responses. Cohere’s re-ranker model enhances this process by evaluating and ordering search results based on contextual relevance, improving accuracy and saving time for specific information seekers.

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