Data Visualization Explained (Part 4): A Review of Python EssentialsTowards Data Science Learn the foundations of Python to take your data visualization game to the next level.
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Learn the foundations of Python to take your data visualization game to the next level.
The post Data Visualization Explained (Part 4): A Review of Python Essentials appeared first on Towards Data Science. Read More
How to Build, Train, and Compare Multiple Reinforcement Learning Agents in a Custom Trading Environment Using Stable-Baselines3MarkTechPost In this tutorial, we explore advanced applications of Stable-Baselines3 in reinforcement learning. We design a fully functional, custom trading environment, integrate multiple algorithms such as PPO and A2C, and develop our own training callbacks for performance tracking. As we progress, we train, evaluate, and visualize agent performance to compare algorithmic efficiency, learning curves, and decision
The post How to Build, Train, and Compare Multiple Reinforcement Learning Agents in a Custom Trading Environment Using Stable-Baselines3 appeared first on MarkTechPost.
In this tutorial, we explore advanced applications of Stable-Baselines3 in reinforcement learning. We design a fully functional, custom trading environment, integrate multiple algorithms such as PPO and A2C, and develop our own training callbacks for performance tracking. As we progress, we train, evaluate, and visualize agent performance to compare algorithmic efficiency, learning curves, and decision
The post How to Build, Train, and Compare Multiple Reinforcement Learning Agents in a Custom Trading Environment Using Stable-Baselines3 appeared first on MarkTechPost. Read More
AI Agents: From Assistants for Efficiency to Leaders of Tomorrow?Towards Data Science How artificial intelligence is evolving from “simple” assistants to potential architect of our future-even CEOs and governors
The post AI Agents: From Assistants for Efficiency to Leaders of Tomorrow? appeared first on Towards Data Science.
How artificial intelligence is evolving from “simple” assistants to potential architect of our future-even CEOs and governors
The post AI Agents: From Assistants for Efficiency to Leaders of Tomorrow? appeared first on Towards Data Science. Read More
The Power of Framework Dimensions: What Data Scientists Should KnowTowards Data Science Practical guidance and a case study
The post The Power of Framework Dimensions: What Data Scientists Should Know appeared first on Towards Data Science.
Practical guidance and a case study
The post The Power of Framework Dimensions: What Data Scientists Should Know appeared first on Towards Data Science. Read More
5 Common LLM Parameters Explained with ExamplesMarkTechPost Large language models (LLMs) offer several parameters that let you fine-tune their behavior and control how they generate responses. If a model isn’t producing the desired output, the issue often lies in how these parameters are configured. In this tutorial, we’ll explore some of the most commonly used ones — max_completion_tokens, temperature, top_p, presence_penalty, and
The post 5 Common LLM Parameters Explained with Examples appeared first on MarkTechPost.
Large language models (LLMs) offer several parameters that let you fine-tune their behavior and control how they generate responses. If a model isn’t producing the desired output, the issue often lies in how these parameters are configured. In this tutorial, we’ll explore some of the most commonly used ones — max_completion_tokens, temperature, top_p, presence_penalty, and
The post 5 Common LLM Parameters Explained with Examples appeared first on MarkTechPost. Read More
A New AI Research from Anthropic and Thinking Machines Lab Stress Tests Model Specs and Reveal Character Differences among Language ModelsMarkTechPost AI companies use model specifications to define target behaviors during training and evaluation. Do current specs state the intended behaviors with enough precision, and do frontier models exhibit distinct behavioral profiles under the same spec? A team of researchers from Anthropic, Thinking Machines Lab and Constellation present a systematic method that stress tests model specs
The post A New AI Research from Anthropic and Thinking Machines Lab Stress Tests Model Specs and Reveal Character Differences among Language Models appeared first on MarkTechPost.
AI companies use model specifications to define target behaviors during training and evaluation. Do current specs state the intended behaviors with enough precision, and do frontier models exhibit distinct behavioral profiles under the same spec? A team of researchers from Anthropic, Thinking Machines Lab and Constellation present a systematic method that stress tests model specs
The post A New AI Research from Anthropic and Thinking Machines Lab Stress Tests Model Specs and Reveal Character Differences among Language Models appeared first on MarkTechPost. Read More
Google vs OpenAI vs Anthropic: The Agentic AI Arms Race BreakdownMarkTechPost In this article we will analyze how Google, OpenAI, and Anthropic are productizing ‘agentic’ capabilities across computer-use control, tool/function calling, orchestration, governance, and enterprise packaging. Agent platforms, not only models, now define competitive advantage. Google is aligning Gemini 2.0 with an enterprise control plane on Vertex AI and a new ‘front door’ called Gemini Enterprise.
The post Google vs OpenAI vs Anthropic: The Agentic AI Arms Race Breakdown appeared first on MarkTechPost.
In this article we will analyze how Google, OpenAI, and Anthropic are productizing ‘agentic’ capabilities across computer-use control, tool/function calling, orchestration, governance, and enterprise packaging. Agent platforms, not only models, now define competitive advantage. Google is aligning Gemini 2.0 with an enterprise control plane on Vertex AI and a new ‘front door’ called Gemini Enterprise.
The post Google vs OpenAI vs Anthropic: The Agentic AI Arms Race Breakdown appeared first on MarkTechPost. Read More
How to Build a Fully Functional Computer-Use Agent that Thinks, Plans, and Executes Virtual Actions Using Local AI ModelsMarkTechPost In this tutorial, we build an advanced computer-use agent from scratch that can reason, plan, and perform virtual actions using a local open-weight model. We create a miniature simulated desktop, equip it with a tool interface, and design an intelligent agent that can analyze its environment, decide on actions like clicking or typing, and execute
The post How to Build a Fully Functional Computer-Use Agent that Thinks, Plans, and Executes Virtual Actions Using Local AI Models appeared first on MarkTechPost.
In this tutorial, we build an advanced computer-use agent from scratch that can reason, plan, and perform virtual actions using a local open-weight model. We create a miniature simulated desktop, equip it with a tool interface, and design an intelligent agent that can analyze its environment, decide on actions like clicking or typing, and execute
The post How to Build a Fully Functional Computer-Use Agent that Thinks, Plans, and Executes Virtual Actions Using Local AI Models appeared first on MarkTechPost. Read More
Responsible AI design in healthcare and life sciencesArtificial Intelligence In this post, we explore the critical design considerations for building responsible AI systems in healthcare and life sciences, focusing on establishing governance mechanisms, transparency artifacts, and security measures that ensure safe and effective generative AI applications. The discussion covers essential policies for mitigating risks like confabulation and bias while promoting trust, accountability, and patient safety throughout the AI development lifecycle.
In this post, we explore the critical design considerations for building responsible AI systems in healthcare and life sciences, focusing on establishing governance mechanisms, transparency artifacts, and security measures that ensure safe and effective generative AI applications. The discussion covers essential policies for mitigating risks like confabulation and bias while promoting trust, accountability, and patient safety throughout the AI development lifecycle. Read More
Liquid AI’s LFM2-VL-3B Brings a 3B Parameter Vision Language Model (VLM) to Edge-Class DevicesMarkTechPost Liquid AI released LFM2-VL-3B, a 3B parameter vision language model for image text to text tasks. It extends the LFM2-VL family beyond the 450M and 1.6B variants. The model targets higher accuracy while preserving the speed profile of the LFM2 architecture. It is available on LEAP and Hugging Face under the LFM Open License v1.0.
The post Liquid AI’s LFM2-VL-3B Brings a 3B Parameter Vision Language Model (VLM) to Edge-Class Devices appeared first on MarkTechPost.
Liquid AI released LFM2-VL-3B, a 3B parameter vision language model for image text to text tasks. It extends the LFM2-VL family beyond the 450M and 1.6B variants. The model targets higher accuracy while preserving the speed profile of the LFM2 architecture. It is available on LEAP and Hugging Face under the LFM Open License v1.0.
The post Liquid AI’s LFM2-VL-3B Brings a 3B Parameter Vision Language Model (VLM) to Edge-Class Devices appeared first on MarkTechPost. Read More