Analytics Vidhya

OCTOBER 12, 2024

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 202

Python 202

Marktechpost

OCTOBER 12, 2024

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

LLM Machine Learning Data Analysis AI 133

Analytics Vidhya

OCTOBER 12, 2024

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.

Data Science 189

Data Science Machine Learning Python 189

Marktechpost

OCTOBER 12, 2024

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

AI AI ML Computer Vision 128

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

Analytics Vidhya

OCTOBER 12, 2024

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.

Chatbots 165

Chatbots Automation AI AI 165

Machine Learning Mastery

OCTOBER 12, 2024

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.

Categorization 69

Categorization Algorithm 69

Marktechpost

OCTOBER 12, 2024

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.

Large Language Models 119

Large Language Models AI AI Artificial Intelligence 119

ODSC - Open Data Science

OCTOBER 12, 2024

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.

Prompt Engineering 52

Prompt Engineering Prompt Engineer Software Development AI 52

Marktechpost

OCTOBER 12, 2024

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

LLM Natural Language Processing Large Language Models Software Development 119

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Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.

Marktechpost

OCTOBER 12, 2024

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.

Machine Learning 116

Machine Learning OpenAI ML Engineer ML 116

Marktechpost

OCTOBER 12, 2024

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

LLM Artificial Intelligence Artificial Intelligence AI 114

Marktechpost

OCTOBER 12, 2024

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.

Large Language Models 111

Large Language Models LLM ML Computer Vision 111

Marktechpost

OCTOBER 12, 2024

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.

Large Language Models 109

Large Language Models Automation ML Artificial Intelligence 109

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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?

Business Intelligence

Marktechpost

OCTOBER 12, 2024

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

ML AI AI Artificial Intelligence 101

Marktechpost

OCTOBER 12, 2024

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

ML AI Tools Artificial Intelligence Artificial Intelligence 59