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Unlocking Insights 📊 with Python! In this two-part series, Ong Yi Shen, our intern from NTU, shares some nifty tricks to clean, analyse, and report data from CSV files. Click the link below to learn more! #DataScience #PythonProgramming https://lnkd.in/gn2TUgRy

I think that in the domain of careers, machine learning algorithms work as a fantastic recommendation system for learning. There are use cases where machine learning algorithms are deployed in online learning platforms to recommend modules of growth, personalized drill questions, etc.

As the Data Analyst my core responsibilities include:- Designing and tuning feedback controllers to optimize response time and minimize steady state offset for hydrocarbon outlet temperature and oxygen exit concentration using MATLAB & Simulink software.- Modelling disturbance and manipulated variable relationships for feedforward controller tuning to undertake corrective action before controlled variable deviation.- Exploring the viability of coupled cascade, feedforward and feedback control regimes for furnace control. Show less

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👉🏼 SPROUT: an Interactive Authoring Tool for Generating Programming Tutorials with the Visualization of Large Language Models 🤓 Yihan Liu 👇🏻 https://lnkd.in/eeJfTY3C 🔍 Focus on data insights: - The use of large language models (LLMs) like ChatGPT has significantly improved the efficiency of creating programming tutorials. - LLMs can generate comprehensive text descriptions for code snippets based on text prompts provided by users. - Transparency in the end-to-end generation process is crucial for understanding model behavior and enhancing user control over results. 💡 Main outcomes and implications: - Introducing a novel approach that breaks down the programming tutorial creation task into actionable steps. - Implementing the tree-of-thought method to engage LLMs in an exploratory process for generating diverse and faithful programming tutorials. - Presenting SPROUT, an authoring tool with interactive visualizations that empower users to have greater control and understanding of the programming tutorial creation process. 📚 Field significance: - Enhancing user experience and improving the overall quality of programming tutorials. - Providing users with more control and understanding leads to more reliable and customizable results. - Empowering users to actively participate in the programming tutorial creation process. 🗄️: #LargeLanguageModels #ProgrammingTutorials #SPROUT #DataInsights

I’m going to go against conventional wisdom that tells you to think of and do high impact projects in your portfolio - instead do whatever you’re interested in! Interested in neural style transfer? Try it! I also think it’s ok to follow someone else’s code so long as you code with understanding - and more often than not you’ll notice ways to improve it (that’s your enhancement!). In my opinion , Personal projects help to keep the passion for AI alive, and is a good space of self expression. When you talk about your projects, it’ll be very evident that you’re passionate about the project and not just “doing” a project that looks nice.

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Research- Conceptualized novel copper nanoparticles on yield and selectivity on methanol synthesis from CO2 using Vienna Ab Initio Simulation Package (VASP). - Conducted extensive literature survey on Density Functional Theory of Heterogeneous Catalysis- Investigated coordination effects of CO2 on Nickel catalysts for Fischer-Tropsch synthesis simulations.Data Analyst- Analyzed and elucidated trends in the variation of coordination numbers and shape effects on selectivity of copper catalysts on methanol synthesis from CO2. - Developed and communicated a Wulff Reconstruction procedure for the group to determine equilibrium nanoparticle shape after absorption.Collegiate Mentorship- Mentored final year project students in using the VASP software and understanding Density Functional Theory concepts in relation to catalysis. Show less

Beyond algorithms, it’s more important to know what you want in your career - because if you do, you don’t need to craft bespoke algorithms but simply prompt an LLM and give it search tools to answer. For example, an LLM can be given Serper/Tavily tools to search the Internet, and this LLM agent can be deployed in an ensemble of agents, each doing different things. One agent can have a complete view of your resume, another agent compares search results and your resume, and another agent rewrites your resume for the job.

Check out how GovTech Singapore worked with the Ministry of Manpower to deploy AI solutions, including an automatic job posting classifier and an AI-powered Sensemaker that can extract and summarise insights from large volumes of documents! Henry Chang recently spoke about these innovative projects that combine generative AI and traditional AI at the Amazon Web Services (AWS) summit yesterday (https://lnkd.in/g5xxEMaA) and GovInsider has previously showcased the innovative Analytics.Gov here (https://lnkd.in/gbuV4tPp). How else can government organisations combine traditional and generative AI to make lives easier for staff and citizens? 💡

Learn how to implement the independent samples t-test and Welch's t-test in order to compare benchmarking scores effectively — Boriharn K's new tutorial explains the process in detail.

• Designed and conducted local optimization of the concentration unit of the distillation separation network to concentrate butanol product stream from 2wt% to 40wt%, resulting in economical operating costs to produce 175,000t/yr of butanol. • Coordinated global profitability analysis to optimize return on investment at the end of the plant’s operating span of 10 years.• Executed cost sensitivity analysis to quantify economic risks of plant profitability factors, resulting in the subsequent development of hedging strategies and identification of alternative feedstock to ensure plant profits.• Initiated social, economic and environmental sustainability analysis of the plant, identifying Texas (USA) and Brazil as potential startup locations. Show less

[🐍-Python-Update] Region Marker #1 -Setting the region with the selected media as it is - ReaScript! https://lnkd.in/gjApnYhQ Creating a region, this time a Python version. The mechanism can be implemented in almost the same way as in Lua. By always deleting all the regions first The implementation is such that the necessary amount of regions are created immediately. ✨ Region creation is a very important feature of the individual export method, so once mastered, it can be used in various places and will lead to more efficient development work. 💪

It’s impossible to compete against the freelancing market because it’s impossible to master every skill (data engineering, MLOps, full stack dev, data science, Math, stats, etc.) /programming language, and more often than not you need the freelancing market to be successful to succeed yourself (just think of how long you spend on stack overflow). So it’ll be better to differentiate yourself by perspective and approach towards problem statements, and making your name by doing select projects well instead of doing many projects. Refine mastery of skills that help you in your projects by taking online short courses / watching videos and practicing them when given a chance :)

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Let's evaluate Llama3's RAG performance on an arXiv paper (about RAG evaluation, this is a very meta notebook, puns intended)! In this tutorial, we build on our work earlier this week in combining Unstructured's API for pdf document preprocessing with OpenAI's GPT-4o + ragas for evaluation, Hugging Face for Llama3, and LangChain to integrate all of these systems to evaluate Llama3. Llama3's metrics on RAG for our quick example: context precision: 0.9867, faithfulness: 0.8297, answer relevancy: 0.8643, context_recall: 0.9733 Try this out with your favorite unstructured data by swapping the arXiv pdf URL in the notebook! Colab Notebook: https://lnkd.in/gSndzTuC

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👉🏼 Performance of Large Language Models in Patient Complaint Resolution: Web-Based Cross-Sectional Survey 🤓 Lorraine Pei Xian Yong 👇🏻 https://lnkd.in/ewQy8z-p 🔍 Focus on data insights: - 📊 A significant majority (87.2% to 97.3%) of respondents preferred responses generated by ChatGPT over those from human health care workers. - 🏆 ChatGPT-4.0 outperformed human responses across all assessed metrics: appropriateness, completeness, empathy, and satisfaction, with median scores consistently higher. - ✍️ Responses from ChatGPT had a higher average word count (238 words) compared to human responses (76 words), indicating a potential correlation between response length and perceived quality. 💡 Main outcomes and implications: - 🤖 The study provides strong evidence for the effectiveness of large language models in enhancing patient complaint resolution, suggesting a shift towards AI-assisted communication in healthcare. - ⏱️ Future applications could lead to improved response times and reduced workload for health care professionals, potentially increasing overall patient satisfaction. - 💰 Cost-effectiveness and efficiency gains from using AI in patient relations could reshape resource allocation within healthcare institutions. 📚 Field significance: - 🌐 This research highlights the growing role of artificial intelligence in healthcare, particularly in improving patient interactions and satisfaction. - 🔬 It opens avenues for further studies on the integration of AI technologies in various aspects of healthcare delivery, emphasizing the need for ongoing evaluation of AI's impact on patient care. 🗄️: [#PatientComplaints #LargeLanguageModels #AIinHealthcare #ChatGPT #PatientSatisfaction #HealthcareInnovation #DataInsights]

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Ever wonder how Large Language Models (LLMs) go from being general-purpose text processors to experts in tasks like translation, sentiment analysis, or question answering? 🤔 That's where fine-tuning comes in. 👍 🔧 How LLM Fine-tuning Works (Traditional Approach) 1. Model Selection: Start with a pre-trained LLM. 2. Dataset Preparation: Clean and format your task-specific dataset. 3. Set Hyperparameters: Define key settings like learning rate, batch size, and number of epochs. 4. Train the Model: Fine-tune the model by adjusting its parameters based on the new dataset. 5. Evaluation: Measure the model’s performance using metrics like accuracy or F1 score. While this process works, it has its challenges. 🚫 Limitations of Traditional LLM Fine-tuning 🛠️ It’s Complex: Setting up fine-tuning requires deep knowledge of frameworks like PyTorch, which is a widely-used machine learning library developed by Facebook’s AI Research (FAIR) team. 💸 It’s Resource-Intensive: The process can demand high computational power, often requiring powerful GPUs. ⚙️ Code-Heavy: Tuning strategies and adjusting parameters often means digging deep into the code, which can slow down experimentation. 💪 Introducing Torchtune: A Simpler Way to Fine-tune Developed on top of PyTorch, torchtune makes fine-tuning large language models much more accessible. It simplifies the process and takes advantage of PyTorch's flexibility while minimizing the need for heavy coding and expensive hardware. What Makes torchtune Special? 📋 YAML Configurations: You can easily modify training parameters using YAML files without changing the code. 🧩 Modular Design: Experiment with different fine-tuning strategies and techniques with minimal coding. 💻 Memory Optimization: torchtune is designed to run efficiently on consumer-grade GPUs—no need for expensive hardware! 🤝 Seamless Integration: Works well with popular tools like Hugging Face Datasets and Weights & Biases, making it easy to add to your existing workflow. 👨‍🔧 How torchtune Works (In a Nutshell) 1. YAML Setup: Define your model and training parameters in a simple YAML file. 2. Easy Initialization: Load the configuration and start the training process with just a few commands. 3. Efficient Training: torchtune optimizes memory usage, making training smoother even on standard hardware. 4. Evaluate Quickly: Evaluate the model's performance right after training without needing to save it first. 🔎 Why Choose torchtune? 🛠 Easy to Adjust: YAML configurations make it simple to try out different setups without needing to touch the code. ✈ Flexible and Fast: Quickly iterate and experiment with various fine-tuning techniques. 💸 Cost-Effective: Designed to work even on less powerful GPUs, making it accessible for researchers, students, and professionals alike. You can go through in-detail about this torchtune, here is the GitHub link https://lnkd.in/dDW7gwiw

OpenAI o1: LLM trained with COT reinforcement learning to excel in complex reasoning. o1 doesn't just answer—it thinks before it speaks, crafting intricate chains of thought to arrive at solutions. More details: https://lnkd.in/grRatCz8 Here's what sets o1 apart: 💻 89th percentile on Codeforces programming challenges! 🏅 Top 500 student performance in the USA Math Olympiad qualifier! 🧪 Exceeds human PhD-level accuracy on physics, biology, and chemistry problems! But o1 is more than just a brainiac: 🛡️ Improved safety and alignment through integrated policies for model behavior. 🚀 Continuously improving performance with more train-time and test-time compute. #OpenAI #o1 #Reasoning #LLM #AI #MachineLearning #DeepLearning #ChatGPT #Codeforces #MathOlympiad #Science #Coding #Safety #Alignment #Innovation

- Conceptualized a profitability model using Aspen PIMS software to accurately forecast profits of individual feedstock for purchase decision-making, improving monthly gross margin by $720,000/month.- Conceptualized a model using Aspen PIMS to accurately forecast Olefins economic profits of blending feedstock purchases, resulting in a gross margin improvement of $1 Million/month.- Constructed a linear economics yields estimation interface using Visual Basic (VBA) that improved linear yield prediction accuracies by 20% for profitability forecasts of feedstock. - Investigated and identified salient feedstock characteristics that have the greatest economic impact to the process, resulting in a 10% improvement in profit estimation errors. Show less

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GRAPHRAG: NEW TOOL FOR COMPLEX DATA DISCOVERY NOW ON GITHUB The challenge and opportunity for LLMs is solving problems with data they haven't been trained on. This allows for new possibilities in data analysis, like identifying themes and concepts. In this post, XUAN ANH NGUYEN and Bien Vo from our engineering team provide insight into GraphRAG introduced by Microsoft Research, a significant advancement in enhancing LLM capabilities. - GraphRAG is a data pipeline and transformation suite designed to extract meaningful, structured data from unstructured text using the power of Large Language Models (LLMs). - RAG (Retrieval-Augmented Generation) combines retrieval-based and generation-based methods to continuously improve the performance and accuracy of language models that require up-to-date external knowledge. - Traditional RAG utilizes document retrieval through vector similarity search. - GraphRAG uses LLMs to extract information, build graphs, and summarize communities to provide context. - Process Overview: User Query → Document Retrieval (Search and Indexing) → Contextual Integration (Selection and Formatting) → Response Generation (Using Language Model) → Generated Response. For more details, check out the official announcement: https://lnkd.in/gvRsrPn3 https://lnkd.in/gvYHw49f #dwarves #software #graphrag #LMS — Dwarves Notes (https://memo.d.foundation/) combine our team’s collective know-hows, R&Ds, and operation approaches. Connect and learn alongside other tech fellows: - Discord: discord.gg/dwarvesv - Github: https://lnkd.in/gZZ2eZMu - Website: https://d.foundation

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This paper introduces MediTab, a scalable framework for predicting medical outcomes from heterogeneous tabular data by leveraging large language models (LLMs) to consolidate, enrich, and refine the data. 1️⃣ Medical data is often sourced from various origins, each with distinct schemas and limited data points, making it difficult for traditional predictors to generalize across different datasets. These predictors are usually trained on small, manually curated datasets that struggle with the variability and complexity of broader medical data during inference. 2️⃣ MediTab scales predictors to diverse tabular inputs without requiring fine-tuning, using a "learn, annotate, and refine" pipeline. 3️⃣ The approach consolidates data across different schemas into a unified format using natural language descriptions. 4️⃣ It achieves significant improvements over traditional supervised methods in patient and trial outcome predictions. 5️⃣ MediTab demonstrates strong zero-shot and few-shot learning capabilities, outperforming XGBoost in multiple tasks. ✍🏻 Zifeng Wang, Chufan Gao, Cao (Danica) Xiao, Jimeng Sun. ArXiv. April 30, 2024. DOI: 10.48550/arXiv.2305.12081 GE HealthCare

- Developed a pilot plant model of an industrial power plant encompassing CLOU technology using biomass as feed on Aspen Plus Software- Assessed the economic feasibility of the pilot plant using current policy pricing instruments - Conducted market research of technology pricing by surveying various companies through phone interviews.- Optimized process efficiency of the plant- Acutely assessed probably safety risks (HAZOP) of the pilot plant to ensure that the simulation design on Aspen Plus performed optimally and safely for industrial application.- Presented a compelling argument to not implement the CLOU - biomass plant to the director of the course and the Dean of Chemical Engineering- Awarded the "Most Inspiring Student" Award by the American Institute of Chemical Engineers (AIChE) Show less

In this tutorial, you’ll learn to summarize a complete book considering the price and the contextual limit of the model. #AIEngineering #LargeLanguageModels #DataScience by Usama Jamil thanks to MyScale

• Selected as an honours student under the world's top water membrane research group• Fabricated novel anti-fouling membranes by optimizing surface hydrophilicity and pore distribution, generating an unprecedented amount of carbon clean power (9.6W/m2 membrane area) using industrial wastewater and saltwater feeds.

“I know Titus in the capacity as Instructor for two Masters' courses in the MITB programme. I am very impressed with his attitude towards learning (he is keen to learn new concepts even though it's optional materials and not tested in the exams) and delivering every piece of assignment/project with excellence. This is notwithstanding the fact that he has a full-time job at the same time. Apart from his strong work ethics, which is evident from the anonymous peer-evaluations done as part of group projects, Titus is also a genuine person who is willing to do things for the benefit of others rather than himself. He readily accepts invitations to participate in events such as Seniors sharing, in which he shares his experience as an MITB student, and poster exhibition during MITB's 15th anniversary event. With his strong learning ability and mindset for excellence, Titus will be a valuable team member to any organization.”

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👉🏼 Large language models reduce public knowledge sharing on online Q&A platforms 🤓 R Maria Del Rio-Chanona 👇🏻 https://lnkd.in/eBwEnauA 🔍 Focus on data insights: - 📉 A 25% decrease in activity on Stack Overflow was observed within six months of ChatGPT's release, indicating a possible shift in user engagement. - 🌍 This reduction was significantly greater in regions with access to ChatGPT compared to Russian and Chinese counterparts where access is limited. - 🧑‍💻 The decline was consistent across posts related to widely used programming languages, emphasizing a broad impact across technical topics. 💡 Main outcomes and implications: - 🚫 The study suggests that LLMs like ChatGPT may be displacing not only low-quality or duplicate content but also original contributions from skilled users. - 🔍 There was no significant change in the quality of posts as measured by peer feedback, indicating that the reduction in quantity does not correlate with a drop in content quality. - 📊 The findings raise concerns about the sustainability of public knowledge-sharing platforms and the potential long-term effects on the availability of human-generated data for future model training. 📚 Field significance: - 📈 The research highlights the need to understand the dynamics between AI tools and human contribution to online knowledge bases. - 🤖 It calls into question the balance between leveraging LLMs for efficiency and preserving essential human-generated data. - 🛠️ Insights from the study could inform future discussions on designing platforms that encourage both AI use and active human participation. 🗄️: [#large_language_models #data_insights #public_knowledge #AI_impact #Stack_Overflow #ChatGPT #user_engagement #content_creation]

Looking to optimize your RAG retrieval system but short on time? We've got you covered! 🙌 Our team has created a video summary of our article on evaluation of RAG Retrieval Chunking Methods. In just one minute, you'll discover insights and pro tips you need to boost your RAG retrieval performance. 📈 🔍 Key highlights: - The top-performing chunking method that outshined the competition by 10% - The surprising effectiveness of the TinyBERT reranker - Why sentences are the ideal units for chunking - Actionable strategies for maximizing your RAG retrieval pipeline Whether you're a seasoned professional or just starting out, this video is packed with valuable information to help you stay ahead of the curve. 🌟 📹 Watch the video: https://buff.ly/4b2CqtQ 📄 Read the full article: https://buff.ly/4aZt5TF #RAGRetrieval #DataScience #MachineLearning #Efficiency #Optimization #Superlinked

👉🏼 Dual Causes Generation Assisted Model for Multimodal Aspect-Based Sentiment Classification 🤓 Rui Fan 👇🏻 https://lnkd.in/eC5m_4qv 🔍 Focus on data insights: - The proposed MDCA method tracks underlying causes behind expressed sentiments, providing reasoning cause (RC) and direct cause (DC) to explain emotions. - Utilizes large language models (LLMs) and visual-language models to construct MABSC datasets with RC and DC. - Implements a multitask learning framework leveraging cause data to train a generative model for sentiment prediction. 💡 Main outcomes and implications: - MDCA model achieves state-of-the-art performance in MABSC benchmark datasets. - Small fine-tuned model shows superior adaptability to MABSC compared to larger models like ChatGPT and BLIP-2. 📚 Field significance: - Advances in understanding sentiment analysis in multimodal data. - Enhances accuracy and interpretability of sentiment prediction models. 🗄️: #Multimodal #SentimentAnalysis #DataInsights #GenerativeModel

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QLoRA: Efficient fine-tuning of quantized LLMs It cuts memory use while fine-tuning huge LMs, using techniques like: • 4-bit NormalFloat (NF4) Quantization: Simplifies data by breaking it into chunks, like resizing images without losing quality, scaling data to fit a new range. • Double Quantization: Simplifies data storage with fewer bits, keeping accuracy. It quantizes the quantization constants, saving about 3 GB for a 65B model. • Paged Optimizers: These memory managers move data to the CPU when GPU memory is low and retrieve it when needed. This prevents crashes or slowdowns from memory spikes. Models trained on QLoRA can achieve 97.8% of ChatGPT's performance. Can something be better at reducing memory use than QLoRA? Well, it's QDoRA. Read about QDoRA and DoRA methods in our AI 101 episode: https://lnkd.in/eM7Eswgb #AI #LoRA #QLoRA #ML

Taking your first steps in deep-learning model optimization? You won't want to miss Minh Chien Vu's practical guide to weight quantization and its effects on model size and performance.

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Taking your first steps in deep-learning model optimization? You won't want to miss Minh Chien Vu's practical guide to weight quantization and its effects on model size and performance.

An insightful post about Meta's approach to making ML more robust #AI #ML #MLEngineering #DataScience #MachineLearning #ArtificialIntelligence #DeepLearning

Take predictive analysis with a pinch of salt because there is a wide degree of error for domains that are vulnerable to exogenous variables. For example you can go to a restaurant everyday, but one day you meet the love of your life there and this event cannot be forecasted. Likewise it’s impossible to forecast mass layoffs or the explosion or large language models. Instead, keep the predictive scope narrow and do sufficient exploratory data analysis to ensure that you’re not detecting spurious correlations.

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Accelerate Your Data Journey! Gain the skills to solve real-world business challenges with cutting-edge techniques today. From anomaly detection to recommender systems, master the tools used by industry leaders like Amazon and Netflix. To find out more, visit https://lnkd.in/gyKDcESA NUS Computing #technology #machinelearning #business #innovation #unsupervisedlearning #supervisedlearning #data #RapidMiner #AI

One of the ways to iterate rapidly is to use AutoML models on the final clean dataset (with engineered features, etc.). There are many tools such as AWS Sagemaker, DataRobot, SAS, KNIME, Orange, etc. the aim is to just get a sensing of what class of models appear to work better. Then pick that class and work on understanding feature importance and hyperparameter tuning