MIT: 35 Best Courses in Machine Learning! @MIT Explore a world of knowledge with free online courses from MIT on edX, featuring lessons in AI, machine learning, computer science engineering, circuits and electronics, Genetics, data science, statistics and much more. Many people are unaware that edX hosts an incredible collection of free online courses from some of the top educational institutions globally. You can dive into everything from AI to Python programming without any cost. A significant number of these courses are crafted by MIT experts. We highly encourage you to take advantage of this opportunity, and to kickstart your learning journey, here’s a curated selection of the best free online courses from MIT that you can explore this month. #Bigdata #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode References Green, J. (2024, December 11). MIT: 35 best courses in machine learning! @MIT_CSAIL Mashable. Retrieved December 11, 2024, from mashable.com/article/free-mi…
Grade Body Measurement with Machine Learning Grading a patient’s obesity level is a critical component of effective healthcare. Obesity is a significant risk factor for a range of serious diseases, including chronic conditions, type-2 diabetes, heart disease, and certain cancers. Understanding a person's obesity status can serve as a powerful catalyst for individuals to take control of their weight. Additionally, intentional weight management not only mitigates health risks but also offers the compelling benefit of reducing disease susceptibility. Despite its widespread use, the Body Mass Index (BMI) – the standard metric defined by the World Health Organization (WHO) – fails to capture the complexities of obesity, as it overlooks essential body-type variations. Furthermore, nutritional needs differ markedly across regions and body types, underscoring the necessity for a more nuanced approach to obesity assessment. Traditional anthropometric measurements, while effective, are often impractical due to the requirement for trained professionals to perform them accurately. In response to this challenge, innovative research utilizing 3D scanning technology is gaining momentum as a less-invasive and more accessible alternative. Unlike Computed Tomography (CT) or Dual-energy X-ray absorptiometry (DXA)—considered the gold standard for measuring body fat percentage (bf%)—3D scanners eliminate the risks associated with radiation exposure during frequent assessments. Moreover, evaluating health risks demands a multifaceted approach rather than relying solely on a singular measure. In this study, we collected paired data from 3D body scans and DXA for a Korean population, providing a more comprehensive understanding of obesity and paving the way for improved health management strategies. This pioneering research has the potential to transform how we assess and respond to obesity, ultimately leading to healthier outcomes for individuals and communities alike. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode References Jeon, S., Kim, M., Yoon, J., & et al. (2023). Machine learning-based obesity classification considering 3D body scanner measurements. Scientific Reports, 13, 3299. Retrieved February 27, 2025, from doi.org/10.1038/s41598-023-3…
GAIA: An LLM Benchmark. Large Language Models (LLMs) herald a new era for artificial general intelligence general-purpose systems, showcasing remarkable fluency, extensive knowledge, and a notable alignment with human preferences. These advanced models can be augmented with powerful tools like Hyperbrowser, web browsers and code interpreters, operating effectively in zero or few-shot scenarios. Despite these advancements, evaluating their performance remains a formidable challenge. As LLMs continue to evolve, they are rapidly surpassing traditional AI benchmarks at an unprecedented pace. In pursuit of more demanding evaluations, the prevailing trend is toward identifying tasks that not only pose significant challenges for humans but also stretch the capabilities of LLMs. That includes complex educational assessments in fields such as STEM and Law, or even ambitious endeavors like crafting a coherent book. However, it’s crucial to recognize that tasks difficult for humans may not equate to similar challenges for these cutting-edge systems. For instance, benchmarks like MMLU and GSM are nearing resolution, likely due to the rapid advancements in LLM technology coupled with potential data contamination impacts. Moreover, open-ended generation necessitates a paradigm shift in evaluation methods, often relying on human or model-based assessments. As task complexity escalates—evident in longer outputs or specialized skills—the feasibility of human evaluation diminishes. How can we assess a book generated by AI or evaluate solutions to intricate math problems that are beyond the grasp of most experts? Conversely, model-based evaluations are inherently limited; they depend on prior models that may not adequately assess new state-of-the-art models and can introduce subtle biases, such as favoring the initial choice presented. In summary, as we advance into uncharted territories of AI capabilities, it is imperative to innovate our assessment frameworks to ensure they accurately reflect the profound potential of these transformative technology. That's where GAIA reigns. #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode References Mialon, G., Fourrier, C., Swift, C., Wolf, T., LeCun, Y., & Scialom, T. (2023, November 21). GAIA: A benchmark for General AI Assistants. arXiv. doi.org/10.48550/arXiv.2311.…
GPT with LangGraph! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode The rapid advancement of large model technology is leading to an increasing application of agent technology across various fields and industries significantly transforming how people work and live. In complex and dynamic environments, multi-agent systems are able to tackle intricate tasks that would be challenging for a single agent, thanks to their collaborative and division-of-labor approaches. The following stack of research papers and hands on tutorials highlight the integrated use of GPT with LangGraph and CrewAI. LangGraph enhances information transmission efficiency through its graph-based structure, while CrewAI boosts team collaboration and system performance via intelligent task allocation and resource management. The key areas of this research include: The design of agent architectures based on LangGraph for precise control . The enhancement of agent capabilities through CrewAI to tackle a range of tasks. The goal of this study is to explore the combined potential of GPT and LangGraph and CrewAI in multi-agent systems, offering fresh insights for the ongoing evolution of agent technology and fostering innovation in the application of large model intelligent agents. References Duan, Z., & Wang, J. (2024, November 27). Exploration of LLM multi-agent application implementation based on LangGraph+CrewAI. arXiv. Retrieved March 9, 2025, from arxiv.org/abs/2411.18241 Horsey, J. (2025, March 9). Build a powerful Python chatbot in minutes with LangGraph. Geeky Gadgets. Retrieved March 9, 2025, from geeky-gadgets.com/build-a-po… Ong, R. (2024, July 10). GPT-4o and LangGraph tutorial: Build a TNT-LLM application. DataCamp. Retrieved March 9, 2025, from datacamp.com/tutorial/gpt-4o… Sivan, V. (2024). Building AI agent systems with LangGraph. Medium. Retrieved March 9, 2025, from medium.com/pythoneers/buildi… Wang, J., & Duan, Z. (2024, December 2). Intelligent Spark agents: A modular LangGraph framework for scalable, visualized, and enhanced big data machine learning workflows. arXiv. Retrieved March 9, 2025, from arxiv.org/abs/2412.01490
