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Get ready to embark on an exciting journey into the realm of AI and discover how machines can learn and make intelligent decisions. Our blog articles are crafted with simplicity and clarity in mind, making complex machine learning concepts easy to understand for everyone. Whether you’re a beginner or an experienced practitioner, we’ve got you covered with informative and insightful content. Explore the fascinating world of algorithms, models, and data as we delve into supervised and unsupervised learning, reinforcement learning, and more. Discover practical applications in various domains like healthcare, finance, and autonomous vehicles. From introductory guides to advanced techniques, we’re here to help you demystify machine learning and unlock its potential. Join us on this journey as we unravel the secrets of machine learning and empower you to build intelligent systems that can analyze data, make predictions, and drive innovation.
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is one of the key techniques for reducing the memory footprint of large language models (LLMs). It works by converting the data type of model parameters from higher-precision formats such as 32-bit floating point (FP32) or 16-bit floating point (FP16/BF16) to lower-precision integer formats, typically INT8 or INT4. For example, quantizing a model to 4-bit …
New AI agent evolves algorithms for math and practical applications in computing by combining the creativity of large language models with automated evaluators
Recent large language models (LLMs) — such as OpenAI’s o1/o3, DeepSeek’s R1 and Anthropic’s Claude 3.7 — demonstrate that allowing the model to think deeper and longer at test time can significantly enhance model’s reasoning capability. The core approach underlying their deep thinking capability is called chain-of-thought (CoT), where the model iteratively generates intermediate reasoning …
Empowering LLMs to Think Deeper by Erasing Thoughts Read More »
I enjoyed reading this paper, not because I’ve met some of the authors before🫣, but because it felt necessary. Most of the papers I’ve written about so far have made waves in the broader ML community, which is great. This one, though, is unapologetically African (i.e. it solves a very African problem), and I think …
A Review of AccentFold: One of the Most Important Papers on African ASR Read More »
distributions are the most commonly used, a lot of real-world data unfortunately is not normal. When faced with extremely skewed data, it’s tempting for us to utilize log transformations to normalize the distribution and stabilize the variance. I recently worked on a project analyzing the energy consumption of training AI models, using data from Epoch …
GPT-powered fashion assistant, I expected runway looks—not memory loss, hallucinations, or semantic déjà vu. But what unfolded became a lesson in how prompting really works—and why LLMs are more like wild animals than tools. This article builds on my previous article on TDS, where I introduced Glitter as a proof-of-concept GPT stylist. Here, I explore …
What My GPT Stylist Taught Me About Prompting Better Read More »
Today we’re releasing early access to Gemini 2.5 Pro Preview (I/O edition), an updated version of 2.5 Pro that has significantly improved capabilities for coding, especially building compelling interactive web apps. We were going to release this update at Google I/O in a couple weeks, but based on the overwhelming enthusiasm for this model, we …
Every company I worked for until today, there it was: the resilient MS Excel. Excel was first released in 1985 and has remained strong until today. It has survived the rise of relational databases, the evolution of many programming languages, the Internet with its infinite number of online applications, and finally, it is also surviving …
Generating Data Dictionary for Excel Files Using OpenPyxl and AI Agents Read More »
We’ve seen developers doing amazing things with Gemini 2.5 Pro, so we decided to release an updated version a couple of weeks early to get into developers hands sooner. Today we’re excited to release Gemini 2.5 Pro Preview (I/O edition). This update features even stronger coding capabilities, for you to start building with before Google …
Gemini 2.5 Pro Preview: even better coding performance Read More »
posts, we explored Part I of the seminal book Reinforcement Learning by Sutton and Barto [1] (*). In that section, we delved into the three fundamental techniques underlying nearly every modern Reinforcement Learning (RL) algorithm: Dynamic Programming (DP), Monte Carlo methods (MC), and Temporal Difference Learning (TD). We not only discussed algorithms from each field …
Benchmarking Tabular Reinforcement Learning Algorithms Read More »
