Published inTowards Data Science·PinnedMember-onlyOrganizational Processes for Machine Learning Risk ManagementOrganizational processes are a key nontechnical determinant of reliability in ML systems. — In our ongoing series on Machine Learning Risk Management, we've embarked on a journey to unravel the critical elements that ensure the trustworthiness of Machine Learning (ML) systems. In our first installment, we delved into "Cultural Competencies for Machine Learning Risk Management," exploring the human dimensions required to navigate this…Data Science7 min readData Science7 min read
Published inTowards Data Science·PinnedMember-onlyCultural Competencies for Machine Learning Risk ManagementAn organization's culture is an essential aspect of responsible AI. — In the race for progress, we must tread carefully, for haste in engineering and data science can shatter more than just code. Imagine a world where Artificial Intelligence (AI) powered systems could do no wrong, where they flawlessly executed their tasks without a glitch. Sounds like a sci-fi dream, doesn't…Machine Learning7 min readMachine Learning7 min read
Published inTowards Data Science·PinnedMember-onlyExploring the Vulnerability of Language Models to Poisoning AttacksCan the strengths of Language Models be turned into their weaknesses? — In 2016, Microsoft experienced a significant incident with their chatbot, Tay, highlighting the potential dangers of data poisoning. Tay was designed as an advanced chatbot created by some of the best minds at Microsoft Research to interact with users on Twitter and promote awareness about artificial intelligence. …Large Language Models9 min readLarge Language Models9 min read
Published inTowards Data Science·PinnedMember-onlyInterpretable or Accurate? Why Not Both?Building interpretable Boosting Models with IntepretML — As summed up by Miller, interpretability refers to the degree to which a human can understand the cause of a decision. A common notion in the machine learning community is that a trade-off exists between accuracy and interpretability. This means that the learning methods that are more accurate offer less…Machine Learning8 min readMachine Learning8 min read
PinnedMember-onlyAccessing Formula -1 Race's 🏎 historical data using PythonAnalyzing the 2021 Abu Dhabi Grand Prix with the Fast F1 library — # First published on Oct 15, 2021. Edited on March 20, 2022 What’s behind you doesn’t matter — Enzo Ferrari The 73rd season of the Formula One World Championship or F1 commenced in Bahrain on 20th March 2022. Fierce battles are being witnessed between the ten teams for the ultimate…Formula 19 min readFormula 19 min read
Published inTowards Data Science·May 23Member-onlySimplifying subplots creation in MatplotlibInfusing Mosaic Magic into Your Plots — Recently, I was working on a project that required creating subplots using the Matplotlib library in Python. If you’ve ever used the Matplotlib library, there’s a high chance you’ve also utilized its subplot functionality. Subplots are an effective tool for generating multiple plots simultaneously, which…Data5 min readData5 min read
Published inBreaking the Jargons·May 17Member-onlyExplaining models built in H2OEvaluating single models based on global and local explanations — Machine Learning explainability refers to the ability to understand and interpret the decisions and predictions made by a machine learning model. Explainability is crucial for ensuring the trustworthiness and transparency of machine learning models, particularly in high-stakes situations where the consequences of incorrect predictions can be significant.Machine Learning10 min readMachine Learning10 min read
Published inBreaking the Jargons·May 13Member-onlyHow effective is Google's Bold and Responsible Approach to AI?Google I/O 2023: A Look at Google's Approach to Building AI Responsibly — It is said that with great power comes great responsibility, but when it comes to the immense capabilities of AI, it brings a whole new level of ethical considerations and accountability. The same sentiment was echoed during Google's much anticipated I/O event on 10th May 2023. …Artificial Intelligence4 min readArtificial Intelligence4 min read
Published inBreaking the Jargons·Apr 28Member-onlyEffortless Fine-Tuning of Large Language Models with Open-Source H2O LLM StudioA framework and no-code GUI designed for fine-tuning state-of-the-art large language models (LLMs). — “Information flow is what the Internet is about. Information sharing is power. If you don’t share your ideas, smart people can’t do anything about them, and you’ll remain anonymous and powerless.” — Vint Cerf While the pace at which Large Language Models (LLMs) have been driving breakthroughs is remarkable, these…ChatGPT12 min readChatGPT12 min read
Published inTowards Data Science·Feb 6Member-onlyWriting a book on NLP is a bit like solving a complex data science projectAn interview with Lewis Tunstall, co-author of the book- Natural Language Processing with Transformers — A series of interviews highlighting the incredible work of writers in the space of data science and their path of writing. “In fiction, the language and the senses it evokes are important, whereas in technical writing, the content, and the information it conveys, are important.” …Naturallanguageprocessing7 min readNaturallanguageprocessing7 min read
