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

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

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

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.

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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.

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Optimization Strategy Data-driven Interactive 334

David Menninger's Analyst Perspectives

NOVEMBER 5, 2024

For example, many tasks in the accounting close follow iterative paths involving multiple participants, as do supply chain management events where a delivery delay can set up a complex choreography of collaborative decision-making to deal with the delay, preferably in a relatively optimal fashion.

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Analytics Vidhya

OCTOBER 7, 2021

A deep learning model consists of activation function, input, output, hidden layers, loss function, etc. Any deep learning model tries to […]. The post A Comprehensive Guide on Deep Learning Optimizers appeared first on Analytics Vidhya.

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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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Optimization Modeling Analytics IT 294

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.

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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.

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Modeling Optimization Analytics 293

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 308

Modeling Optimization Analytics 308

Analytics Vidhya

DECEMBER 24, 2021

The post ML Hyperparameter Optimization App using Streamlit appeared first on Analytics Vidhya. It was designed especially for Machine Learning and Data Scientist team. Using Streamlit, we can quickly create interactive web apps and deploy them. Frontend […].

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Optimization Machine Learning Interactive Data Science 376

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.

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Modeling Optimization Software Analytics 212

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 139

Optimization Modeling 139

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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Optimization Software Enterprise Analytics 329

KDnuggets

OCTOBER 28, 2024

From prompt engineering to model tuning and compression, explore five ways to make your language model improve its responses.

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Modeling Optimization IT 67

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.

Optimization 293

Optimization Data Science Publishing Modeling 293

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 280

Optimization Analytics IoT Deep Learning 280

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.

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Analytics Vidhya

AUGUST 31, 2024

Introduction This week, the AI field saw significant updates as top companies unveiled new models and tools. AnthropicAI improved Claude 3, and Bindu Reddy introduced Dracarys, a coding-focused model. AI21 Labs launched Jamba 1.5,

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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 399

Machine Learning Modeling Data Science Publishing 399

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 279

Predictive Modeling Modeling Statistics Optimization 279

AWS Big Data

AUGUST 21, 2024

To address this requirement, Redshift Serverless launched the artificial intelligence (AI)-driven scaling and optimization feature, which scales the compute not only based on the queuing, but also factoring data volume and query complexity. The slider offers the following options: Optimized for cost – Prioritizes cost savings.

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Optimization Data Lake Data Warehouse Cost-Benefit 82

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 […].

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Modeling Optimization Data Science Publishing 327

CIO Business Intelligence

OCTOBER 2, 2023

As long as you remain aware of potential risk factors and take action to mitigate potential risk, there should be no reason you cannot certify out of your ULA and move toward alternative options such as Rimini Street to identify tangible cost savings and optimize your Return on Investment (ROI).

ROI 103

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CIO Business Intelligence

OCTOBER 17, 2024

With the prohibitive cost of computing power and other expenses needed to deploy AI models, many businesses not only lack the resources, they also often require help identifying and executing the best use cases. To choose the right AI model, you should first examine how AI can add value to their operations while driving efficiencies.

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Smart Data Collective

FEBRUARY 3, 2022

Therefore, the subscription business model is changing how customers pay for the service they receive. Hover, the subscription model can be quite challenging for businesses. If they wish to implement this model, they need to have an information collection system to keep track of billing and customer requests.

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Optimization Data Analytics Modeling Analytics 115

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 240

Modeling Optimization Analytics IT 240

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 262

Deep Learning Modeling Data Science Publishing 262

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.

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