iMocha is a skills intelligence and assessment platform that enables talent teams to make smarter talent decisions. More than 300 organisations in 70+ countries are using iMocha’s platform to acquire job-fit talent faster and in measuring the ROI from their talent development initiatives. The platform comes with innovative features to conduct asynchronous interviews, AI-LogicBox (AI-based pseudo-coding simulator), AI-powered language analyser, skill benchmarking, talent analytics, and custom assessment consulting etc. Enterprises from IT/ITeS, Telecom, Banking, Financial and Insurance services, Engineering, and Healthcare verticals are using iMocha’s assessments for technical, functional and soft skills leveraging the world’s largest skill library comprising 2500+ skills across over 300 job roles.
iMocha is recommended for HR professionals, recruitment agencies, and organizations that need to conduct technical and non-technical assessments. It's also beneficial for companies aiming to enhance their workforce's skills through targeted training and development programs.
Based on our record, Jupyter seems to be more popular. It has been mentiond 216 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / 2 months ago
LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 4 months ago
Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 5 months ago
Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 9 months ago
One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 12 months ago
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