Browsing: Business & Startups

Image by Editor   # Introduction  Tuning hyperparameters in machine learning models is, to some extent, an art or craftsmanship, requiring the right skills to balance experience, intuition, and plenty of experimentation. In practice, the process might sometimes appear daunting because sophisticated models have a large search space, interactions between hyperparameters are complex, and performance gains due to their adjustment are sometimes subtle. Below, we curate a list that contains 7 Scikit-learn tricks for taking your machine learning models’ hyperparameter tuning skills to the next level.   # 1. Constraining Search Space with Domain Knowledge  Not constraining an otherwise vast search space…

LLMs aren’t limited to AI and related fields! They’re powering almost every tech, and thereby is one of the most asked about topics in interviews. This makes it essential to have a surface level familiarity of the technology. This article is designed to mirror how LLMs show up in real interviews. We’ll start from first principles and build forward, so even if you’re new to the topic, you’ll be able to follow the logic behind each answer instead of memorizing jargon. We’ll start by providing 10 interview questions that challenge the fundamentals of LLMs. Then we’d move on to more…

Image by Author   # Introduction  You’ve probably heard people talk about APIs a lot. Basically, an API allows a software to ask another piece of software for help. For example, when we use our weather app, it might use a real-time API to get the data from a remote server. This little conversation saves you from having to build everything on your own. In this article, we’re going to look at five APIs that are actually fun and surprisingly easy to use. You will get to explore AI models, web data, search engines, model fine-tuning, and synthetic data. Each of…

Hot take – just like the Internet era, Google is leading the AI revolution. While there are many AI solutions out there, almost none of them integrate with the daily lives of users as deeply as Google’s AI ecosystem. Case in point: the tech giant has now partnered with PhysicsWallah and Career360 and come up with specially vetted content on Gemini for JEE preparation for students in India. This means students across the country can now access the entire JEE syllabus, concepts, and even mock tests, right from the comfort of their homes. The best part: all of this is…

Image by Author   # Introduction  An online portfolio isn’t optional anymore. It’s usually the first thing people check when they want to see what you can actually do. Not your CV. Not your LinkedIn. Your work. If you’re building things as a developer, data scientist, ML engineer, designer, or even just learning, a portfolio gives your projects a place to live. And you don’t need paid hosting to do it. Hugging Face Spaces is one of those tools people overlook. It’s free, easy to deploy, and good enough to host a clean personal site with live demos. You can keep…

Late last year, Google came up with a comprehensive plan to fix the current educational system for the better. How? Majorly with the help of Artificial Intelligence, or AI, in education. We had covered the plan in a detailed report at the time, which you can read here. Ever since, the tech giant’s AI solutions have evolved to cater especially to the stakeholders of education, be it the students or their teachers. Its recent free AI bundle for students was a glimpse of it. Its suite of extensive features for teachers in NotebookLM is yet another hint. The idea may…

Image by Author   # Introduction to Keeping Secrets  Storing sensitive information like API keys, database passwords, or tokens directly in your Python code is dangerous. If these secrets are leaked, attackers can break into your systems, and your organization can suffer loss of trust, financial and legal consequences. Instead, you should externalize secrets so they never appear in code or version control. A common best practice is to store secrets in environment variables (outside your code). This way, secrets never appear in the codebase. Though, manual environment variables work, for local development it’s convenient to keep all secrets in a…

Just earlier this month, Moonshot AI dropped a bomb in the AI world with the Kimi K2.5. With a 1 trillion-parameter MoE model with 32 billion active parameters, Kimi K2.5 roared onto the scene, shaking the likes of GPT 5 and Gemini 3 Pro. At the time, we had covered how it is one of the strongest contenders for the title of ‘best open-source model of 2026.’ In this article, we shall explore some more features of Kimi K2.5 that make it worthy of all the accolades it is fast-garnering across the internet. To recall, Moonshot AI dubs Kimi K2.5…

Image by Editor   # Introduction  Feature engineering is an essential process in data science and machine learning workflows, as well as in any AI system as a whole. It entails the construction of meaningful explanatory variables from raw — and often rather messy — data. The processes behind feature engineering can be extremely simple or overly complex, depending on the volume, structure, and heterogeneity of the dataset(s) as well as the machine learning modeling objectives. While the most popular Python libraries for data manipulation and modeling, like Pandas and scikit-learn, enable basic and moderately scalable feature engineering to some extent,…

If you’ve worked with DeepSeek OCR, you already know it was efficient at extracting text and compressing documents. Where it often fell short was reading order and layout-heavy pages, multi-column PDFs, dense tables, and mixed content still needed cleanup. DeepSeek OCR 2 is DeepSeek’s answer to that gap. Instead of focusing only on compression, this update shifts attention to how documents are actually read. Early results show cleaner structure, better sequencing, and far fewer layout-related errors, especially on real-world business and technical documents. Let’s explore all the new features of DeepSekk OCR 2! Key Features and Improvements of DeepSeek OCR 2 DeepEncoder V2 architecture for…