Sat.Oct 12, 2024

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Understanding Python pop() Method

Analytics Vidhya

Introduction Ever wanted to remove an item from a list but not just any item, specifically the one at a certain index? Enter Python pop() method. This built-in function helps you achieve exactly that. It removes an element from a list based on its index and, most importantly, returns the removed element, giving you control […] The post Understanding Python pop() Method appeared first on Analytics Vidhya.

Python 201
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Google Cloud and Stanford Researchers Propose CHASE-SQL: An AI Framework for Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL

Marktechpost

An essential bridge connecting human language and structured query languages (SQL) is text-to-SQL. With its help, users can convert their queries in normal language into SQL commands that a database can comprehend and carry out. This technology makes it easier for users to interface with complex databases, which is especially helpful for those who are not proficient in SQL.

LLM 133
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19 Free Data Science Courses by Harvard and IBM

Analytics Vidhya

Introduction Data science is a rapidly growing tech field that’s transforming business decision-making. To break into this field, you need the right skills. Fortunately, top institutions like Harvard and IBM offer free online courses. These courses cover everything from basic programming to advanced machine learning. In this article, we’ve listed some of the best free […] The post 19 Free Data Science Courses by Harvard and IBM appeared first on Analytics Vidhya.

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Researchers at Stanford University Propose ExPLoRA: A Highly Effective AI Technique to Improve Transfer Learning of Pre-Trained Vision Transformers (ViTs) Under Domain Shifts

Marktechpost

Parameter-efficient fine-tuning (PEFT) methods, like low-rank adaptation (LoRA), allow large pre-trained foundation models to be adapted to downstream tasks using a small percentage (0.1%-10%) of the original trainable weights. A less explored area of PEFT is extending the pre-training phase without supervised labels—specifically, adapting foundation models to new domains using efficient self-supervised pre-training.

AI 128
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Usage-Based Monetization Musts: A Roadmap for Sustainable Revenue Growth

Speaker: David Warren and Kevin O’Neill Stoll

Transitioning to a usage-based business model offers powerful growth opportunities but comes with unique challenges. How do you validate strategies, reduce risks, and ensure alignment with customer value? Join us for a deep dive into designing effective pilots that test the waters and drive success in usage-based revenue. Discover how to develop a pilot that captures real customer feedback, aligns internal teams with usage metrics, and rethinks sales incentives to prioritize lasting customer eng

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Building Agentic Chatbots Using AutoGen

Analytics Vidhya

Introduction Chatbots have transformed the way we engage with technology, enabling automated, intelligent conversations across various domains. Building these chat systems can be challenging, especially when aiming for flexibility and scalability. AutoGen simplifies this process by leveraging AI agents, which handle complex dialogues and tasks autonomously.

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From Features to Performance: Crafting Robust Predictive Models

Machine Learning Mastery

Feature engineering and model training form the core of transforming raw data into predictive power, bridging initial exploration and final insights. This guide explores techniques for identifying important variables, creating new features, and selecting appropriate algorithms. We'll also cover essential preprocessing techniques such as handling missing data and encoding categorical variables.

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Arcee AI Releases SuperNova-Medius: A 14B Small Language Model Built on the Qwen2.5-14B-Instruct Architecture

Marktechpost

In the ever-evolving world of artificial intelligence (AI), large language models have proven instrumental in addressing a wide array of challenges, from automating complex tasks to enhancing decision-making processes. However, scaling these models has also introduced considerable complexities, such as high computational costs, reduced accessibility, and the environmental impact of extensive resource requirements.

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AI and Data: Enhancing Development with GitHub Copilot

ODSC - Open Data Science

Editor’s note: Mabel is a speaker for ODSC West this October 29th-31st. Be sure to check out her talk, “ Gen AI in Software Development. What should you be looking for? ,” there! Artificial Intelligence (AI) is not a new concept in the data world. For years, the industry has been developing AI and Machine Learning (ML) models to predict and better understand the data that surrounds us.

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OPTIMA: Enhancing Efficiency and Effectiveness in LLM-Based Multi-Agent Systems

Marktechpost

Large Language Models (LLMs) have gained significant attention for their versatility in various tasks, from natural language processing to complex reasoning. A promising application of these models is the development of autonomous multi-agent systems (MAS), which aim to utilize the collective intelligence of multiple LLM-based agents for collaborative problem-solving.

LLM 119
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15 Modern Use Cases for Enterprise Business Intelligence

Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

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OpenAI Researchers Introduce MLE-bench: A New Benchmark for Measuring How Well AI Agents Perform at Machine Learning Engineering

Marktechpost

Machine Learning (ML) models have shown promising results in various coding tasks, but there remains a gap in effectively benchmarking AI agents’ capabilities in ML engineering. Existing coding benchmarks primarily evaluate isolated coding skills without holistically measuring the ability to perform complex ML tasks, such as data preparation, model training, and debugging.

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IBM Researchers ACPBench: An AI Benchmark for Evaluating the Reasoning Tasks in the Field of Planning

Marktechpost

LLMs are gaining traction as the workforce across domains is exploring artificial intelligence and automation to plan their operations and make crucial decisions. Generative and Foundational models are thus relied on for multi-step reasoning tasks to achieve planning and execution at par with humans. Although this aspiration is yet to be achieved, we require extensive and exclusive benchmarks to test our models’ intelligence in reasoning and decision-making.

LLM 114
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CausalMM: A Causal Inference Framework that Applies Structural Causal Modeling to Multimodal Large Language Models (MLLMs)

Marktechpost

Multimodal Large Language Models (MLLMs) have made significant progress in various applications using the power of Transformer models and their attention mechanisms. However, these models face a critical challenge of inherent biases in their initial parameters, known as modality priors, which can negatively impact output quality. The attention mechanism, which determines how input information is weighted to generate outputs, is especially prone to these biases.

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UNC Chapel Hill Researchers Propose DataEnvGym: A Testbed of Teacher Environments for Data Generation Agents

Marktechpost

Large Language Models (LLMs) have gained significant attention in recent years, but improving their performance remains a challenging task. Researchers are striving to enhance already-trained models by creating additional, targeted training data that addresses specific weaknesses. This process, known as instruction tuning and alignment, has shown promise in enhancing model capabilities across various tasks.

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From Diagnosis to Delivery: How AI is Revolutionizing the Patient Experience

Speaker: Simran Kaur, Founder & CEO at Tattva Health Inc.

The healthcare landscape is being revolutionized by AI and cutting-edge digital technologies, reshaping how patients receive care and interact with providers. In this webinar led by Simran Kaur, we will explore how AI-driven solutions are enhancing patient communication, improving care quality, and empowering preventive and predictive medicine. You'll also learn how AI is streamlining healthcare processes, helping providers offer more efficient, personalized care and enabling faster, data-driven

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MatMamba: A New State Space Model that Builds upon Mamba2 by Integrating a Matryoshka-Style Nested Structure

Marktechpost

Scaling state-of-the-art models for real-world deployment often requires training different model sizes to adapt to various computing environments. However, training multiple versions independently is computationally expensive and leads to inefficiencies in deployment when intermediate-sized models are optimal. Current solutions like model compression and distillation have limitations, often requiring additional data and retraining, which may degrade model accuracy.

ML 101
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GORAM: A Graph-Oriented Data Structure that Enables Efficient Ego-Centric Queries on Federated Graphs with Strong Privacy Guarantees

Marktechpost

Ego-centric searches are essential in many applications, from financial fraud detection to social network research, because they concentrate on a single vertex and its immediate neighbors. These queries offer insights into direct connections by analyzing interconnections around a key node. Enabling such searches without jeopardizing privacy becomes a major difficulty when graphs are dispersed over several data sources, especially ones with limited mutual trust.

ML 59