Dipayan Dutta

While Gen AI engineers in India are rightly concerned about #hallucination in LLMs, yet most would rarely appreciate that genius folks in our own country evolved extraordinary measures to arrest hallucination thousands of years ago, which included sophisticated #guardrails which were to be 'assimilated' in the brain itself, impacting the way it perceived reality and 'generated' content (Isn't the brain one of the most hallucinating natural 'LLMs' after all ?). This was not just to ensure that facts were rendered correctly. The attempt was to prevent disasters of another kind and of a much more serious nature. After all, language in India was considered an important vehicle for connecting to the divine, and not merely for communicating with one another. And we often fail to realize this #opensource framework, so to say, was built upon by different intellectuals in different parts of the country, and India ended up having several wonderful languages in different regions, languages which look different, but have a lot in common. Did you not realize that no matter where in India we are from, names of most of us can be traced to this one language? https://lnkd.in/g4Njx4mG #llms #nlp #generativeai

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Hey everyone, I’m excited to share some major updates on my C++ Math Library for Machine Learning and AI! 🚀 Since my last post, I've made significant progress and implemented both Linear Regression and Logistic Regression. These additions are crucial for predictive modeling and classification tasks, providing a solid foundation for various ML applications. Linear Regression: This class fits a linear model to the input data, allowing us to make predictions based on the relationship between input features and target values. It’s all about finding the best-fitting straight line through our data points. Logistic Regression: This class is designed for binary classification tasks. It uses a logistic function to model the probability of a certain class or event, making it perfect for applications where we need to categorize input data. What's Next? I’m planning to implement a new class that will read training and testing data from files. This will include batch processing for training our models, enhancing efficiency and scalability. This step is essential for handling larger datasets and improving the performance of our models. Stay tuned for more updates as I continue to build and refine this library. If you have any tips, suggestions, or want to collaborate, feel free to reach out! Link to the Github repo: https://lnkd.in/dwcEx_cX #MachineLearning #ArtificialIntelligence #C++ #Programming #AI #ML #GitHub #LearningJourney #DataScience

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𝗟𝗹𝗮𝗺𝗮 𝟯 𝘂𝗻𝘃𝗲𝗶𝗹 𝗮 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝘁𝗿𝗮𝗱𝗲𝗼𝗳𝗳 𝘁𝗵𝗮𝘁 𝗳𝗲𝘄 𝗮𝗿𝗲 𝘀𝗲𝗲𝗶𝗻𝗴⬇️ 🦙Llama 3 just came out and to the surprise of many people, the L3-8B version can match the L2-70B one on several benchmarks. Several people also argued that the chinchilla paper (https://lnkd.in/dXaRqyea) was wrong because the llama 3 scaling is very far from chinchilla-optimal. 🤔 In my opinion, this is due to a misunderstanding of what the chinchilla paper is about and more generally of the effects of scale in deep learning. The bitter lesson (https://lnkd.in/d3sVYYgp) has become common knowledge in the field thanks to the GPT family that popularized it. We hear everywhere that "bigger models are better": Yes it's true, but in what sense? Many people have a mental model along the lines of "bigger models are more capable" meaning they would be able to achieve levels of performance that smaller models can't. ❌ This is not true with LLMs and is mainly because we train only once on tokens (most of them at least) and therefore overfitting is not possible. The 𝘳𝘦𝘢𝘭 advantage of bigger models over smaller ones is that they converge *faster*. In other words, you can reach a given level of NLL (modeling performance) with fewer training iterations. ❔ Then a natural question arises: Should I train a smaller model with more or a bigger model with fewer iterations? That's where the chinchilla paper brings a response. They say that the question they are answering is: "Given a fixed FLOPs budget, how should one trade-off model size and the number of training tokens?" (implying "to achieve the best possible NLL"). 🧐 Coming back to Llama 3, it's obvious that the Llama team is aware of this work and there is a reason why they didn't follow this recipe. My guess is that they are taking into account the inference cost in the balance (which is completely out of the scope of the chinchilla paper). When one "overtrains" an 8B model, one ends up with a worse "NLL to training compute" ratio, but also with a better "NLL to inference compute" ratio. ✅ Now if we think about it from a business perspective, it's a very powerful tradeoff because you can choose to buy performance either at a fixed cost (training) or at a variable one (inference). Hope this post helped you! #Llama3 #Meta #LLM #AI

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Interested in learning how chatGPT, NLP, and LLMs generally work? Graham Neubig, of Carnegie Mellon University, has shared the slides and videos of his class on "Advanced natural language processing". Despite its comprehensiveness, it is easy to understand. https://lnkd.in/gcQQQn3r https://lnkd.in/gPJ9VbmN

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Around this time, last year I left Gartner to pursue a masters at Columbia University. It was a difficult choice, trading the stability of a well-paying job for the uncertainties and debt of full-time education. Many recent graduates and final-year students have reached out to me, facing the same dilemma I encountered two years ago. Here are some reasons why returning to school was worth it for me, along with some truth bombs. Starting with why it was worth: - while I was fortunate to work with an amazing team at Gartner, nothing compares to the learning environment of a top-tier university. I never expected to learn so much from my courses and super smart peers at Columbia University. - research infrastructure and rigour are extraordinary here. The professors and researchers I have worked with have taught me lot from research problem ideation to systematically solving a problem - skills rarely acquired in a revenue driven non-academic environment. - US capital markets do allow innovation to flow super fast, which keeps you close to the future tech and allows access to innovation centric use cases, teams and companies. Now, some truth bombs: - it’s not easy financially, dealing with savings or loans in INR and expenditures in USD. - US job markets can be fiercely tough… specially for foreigners who are fighting a constant visa and financial battle. You’ll end up doing leetcode for job/internship hunt despite of what you like :( - student life sucks - juggling with heavy academic commitments, relentless job hunt (including the ever-present LeetCode grind), part-time job and daily household chores (like cooking) can be overwhelming. Of course, these points come with a personal bias and it’s not fair to generalise. But happy to share more if anyone is interested :)

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Today's lecture for the CS25 Transformers course at Stanford was yet another fantastic presentation. Nathan Lambert provided an overview of the progress and challenges in aligning open language models since the release of ChatGPT. Here are some of my key takeaways and notes from the lecture: 1. ChatGPT's release in late 2022 brought a lot of interest and development in open-source language models and alignment techniques. 2. Reinforcement learning from human feedback (RLHF) has proven to be a cost and time effective tool for aligning language models. 3. Various instruction-tuned models, such as Alpaca, Vicuna, and Koala, emerged in the months following ChatGPT, paving the way for further discovery and experimentation. 4. Evaluation tools like Chatbot Arena, Alpaca Eval, and MT Bench were developed to assess the performance of open-source aligned models. 5. Direct Preference Optimization (DPO) emerged as a simpler alternative to RLHF, sparking debate between the two approaches. 6. Proximal Policy Optimization (PPO) has shown promising results compared to DPO in some experiments. 7. The open-source language model ecosystem continues to grow, with more companies and researchers releasing aligned models and datasets. 8. Data remains the biggest limitation to alignment, with a need for more diverse and human-written datasets. 9. Open models are unlikely to fully catch up to closed models, but the gap is narrowing, and there will always be a demand for both. 10. Current research directions include extending DPO, building smaller aligned models, developing better evaluation methods, and exploring model merging techniques.

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optimizing Large Language Models (LLMs) from a software engineering perspective can significantly impact latency and costs. with my recent time invested in building production-grade RAG solutions, here are a few key pointers I felt can be taken into consideration: - Long context prefill in the prompt passed to the LLM can increase latency and costs. opt for prompt compression or decomposition to mitigate this issue. - avoid using an agentic framework with multi-turn behavior, like self-iterating loops, or techniques like self-consistency prompting, to prevent higher latency and LLM costs. agentic frameworks like crewAI or autogen by ms can be overkill with higher inference time - if using vector embeddings, opt for a vector db with lower retrieval latency, such as sqlite-vss or redis. - implement semantic caching to reduce LLM API calls and lower response time. reducing output length can also aid in processing or decoding phases. you can also explore using YAML instead of JSON for structured output as YAML is less verbose and hence, fewer tokens, however, I still believe this is task-dependent. - tasks performed by LLMs that are not strictly sequential can be split into parallel calls. sequentially required steps can be merged into a single LLM call to avoid round-trip latency. - ensure proximity between LLMs and tools infrastructure-wise. Allowing third-party plugins to deploy on the infrastructure where large models run can further reduce latency. - using async programming paradigms ( for eg. concurrent API calls ) or parallel programming ( for eg. using multi-threading for parallel API requests ) can also help reduce the inference time and increase the throughput. do check LiteLLM (YC W23) though. - using query routing strategies like semantic router by Aurelio AI or Martian or Unify or RouteLLM by Anyscale and LMsys can also help boost cost efficiency and speed. I am yet to test these out though, but hopefully soon. some notes: - there is a tradeoff between throughput and time per output token: if we process let’s say multiple user queries concurrently, we'll have higher throughput compared to running the queries sequentially, but we'll take longer to generate output tokens for each user. - large language Models (LLMs) generate text in a two-step process: "prefill", where the tokens in the input prompt are processed in parallel, and "decoding", where text is generated one 'token' at a time in an autoregressive manner. Each generated token is appended to the input and fed back into the model to generate the next token. Generation stops when the LLM outputs a special stop token or when a user-defined condition is met (e.g., some maximum number of tokens has been generated). what are the other potential strategies for building production-grade rag solutions? #SoftwareEngineering #LLMOptimization #DataProcessing #GenAI #llms

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Clever one-liners are cool. I have a background in math, physics and machine learning. I've written my fair share of clever one-liners. Matrix rotations, optimization algorithms and dense formulas. All with cryptic variable names like x, y, z, theta, and epsilon. But while working as a software engineer, I don’t do that. I aim to keep my code self-explanatory using descriptive variable names. Because clever one-liners don't belong in production code. #softwareengineering

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Check out this highly recommended course for those interested in optimizing their models: - Introduction to Quantization - Symmetric and Asymmetric Quantization Modes - Hands-on Implementation from Scratch using PyTorch - covering Per Tensor, Per Channel (Axis), and Per Group techniques - Weights Packing: Exploring 2 or 4 Bits Quantization Methods https://lnkd.in/gMAuWXHm

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Most (maybe all?) LLM performance benchmarks like Artificial Analysis go in depth on *online* inference. *Batch* inference seems simpler since almost all companies run some form of embarrassingly parallel workloads. But batch inference is different from other map-reduce style workloads. The biggest differences are the use of GPUs and often mixed CPU + GPU compute. Today, tons of energy is going into improving systems for batch inference, and batch inference is just as important as online inference (companies have accumulated tons of data over the years, and if you want to use AI to extract insights or make decisions based on that data, you will need to process that data in a batch fashion). A bunch of companies are speaking at #RaySummit on their best practices for batch inference: Apple: "petabyte scale embedding generation" Roblox: "efficient batch inference at Roblox" Uber: "large scale generative AI batch prediction with Ray and vLLM" eBay: "pioneering next-gen AI platform" Handshake: "job description parsing with batch LLM inference" https://lnkd.in/deWkzHEC

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I’ve completed a project on NYC Taxi Trip Duration Prediction using various regression models, including Linear Regression, Random Forest, and Gradient Boosting. The goal was to analyze and predict the duration of taxi trips based on factors like pick-up and drop-off locations, timestamps, and passenger details. Key Highlights: - Data Analysis: Conducted thorough exploratory data analysis (EDA) to understand the dataset and visualize key trends. - Model Development: Implemented multiple regression models and optimized them for better accuracy, achieving an R² score of 0.70 with Gradient Boosting. -Performance Evaluation: Evaluated model performance using various metrics, ensuring reliable predictions. This project enhanced my skills in data analysis, machine learning, and model optimization. I’m looking forward to applying these learnings in future endeavors! You can check out the project on my GitHub: [https://lnkd.in/g8b_awJU] #DataScience #MachineLearning #Python #NYCTaxi #Project

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Responsible engineers should avoid copying code directly from online sources, including LLMs or platforms like Stack Overflow, without fully reviewing, understanding, and adapting it for safe integration into their codebase. Thoughtful assessment is essential to ensure code behaves as expected and meets project requirements. That said, I believe LLMs can be a fantastic resource to accelerate learning and deepen understanding across a range of topics—as long as they’re used with careful consideration. #LLM #engineering #ChatGPT

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🚨ACL 2024 Outstanding Paper Award Alert 🚨 ➡️Paper Title: INDICLLMSUITE: A BLUEPRINT FOR CREATING PRE-TRAINING AND FINE-TUNING DATASETS FOR INDIAN LANGUAGES 🌟Few pointers from the paper 🎯Despite the considerable advancements in English LLMs, the progress in building comparable models for other languages has been hindered due to the scarcity of tailored resources. 👁In this paper Authors work aims to bridge this divide by introducing an expansive suite of resources specifically designed for the development of Indic LLMs, covering 22 languages, containing a total of 251B tokens and 74.8M instruction-response pairs. 🎯Recognizing the importance of both data quality and quantity, their approach combines highly curated manually verified data, unverified yet valuable data, and synthetic data. 🎯They built a clean, open-source pipeline for curating pre-training data from diverse sources, including websites, PDFs, and videos, incorporating best practices for crawling, cleaning, flagging, and deduplication. 🎯For instruction-fine tuning, they amalgamate existing Indic datasets, translate/transliterate English datasets into Indian languages, and utilize LLaMa2 and Mixtral models to create conversations grounded in articles from Indian Wikipedia and Wikihow. 🎯Additionally, they addressed toxicity alignment by generating toxic prompts for multiple scenarios and then generated non-toxic responses by feeding these toxic prompts to an aligned LLaMa2 model. 🎯 They hope that the datasets, tools, and resources released as a part of this work will not only propel the research and development of Indic LLMs but also establish an open-source blueprint for extending such efforts to other languages. 🏢Organization: 🛎️Nilekani Centre at AI4Bhārat🛎️ Indian Institute of Technology, Madras 🛎️Microsoft🛎️Sant Longowal Institute of Engineering and Technology 🛎️Indian Institute of Information Technology Design & Manufacturing Kancheepuram, 🛎️Maharaja Agrasen Institute Of Technology, Delhi 🛎️Sarvam ,🛎️ NICT 🧙Paper Authors: Mohammed Safi Ur Rahman Khan, Priyam Mehta , Ananth Sankar , Umashankar Kumaravelan, Sumanth Doddapaneni, Suriyaprasaad G, Varun Balan, Sparsh Jain, Anoop Kunchukuttan, Pratyush Kumar, Raj Dabre, Mitesh Khapra 1️⃣Read the Full Paper here: https://lnkd.in/gqwQST-r 2️⃣Code: https://lnkd.in/giWPVntx 🎉 🎉  Congratulations to all the authors 🎉 🎉 Find this Valuable 💎 ? ♻️REPOST and teach your network something new Follow me 👣, Naveen Manwani, for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements. #ACL2024 #Indicllms #llms #llama2 #mixtral

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If your interviewer asks "What are some shortcomings of KMeans" ...here's what to answer👇 . . Knowing an algorithm is just 20% knowledge. The rest 80% comes from understanding the practical limitations. Here are a few limitations of KMeans that many overlook: - It does not account for cluster variance (the spread in 2D, for instance) - It can only produce globular clusters (no oval shapes in 2D, only circular) - It only relies on distance-based measures to assign data points to clusters ↳ To understand better, consider two clusters in 2D, with A and B. Cluster A has a higher spread than B. ↳ Now consider a line that is mid-way between centroids of A and B → A .... | .... B. ↳ Although A has a higher spread, even if a point is even slightly right to the mid line, it will get assigned to cluster B. ↳ Ideally, however, cluster A should have a larger area of influence. - It performs a hard assignment. Thus, there are no probabilistic estimates of each data point belonging to a cluster. These limitations often make KMeans a non-ideal choice for clustering. --- I often find Gaussian Mixture Models (GMM) to be quite a superior algorithm in this respect. As the name suggests, they can cluster a dataset that has a mixture of many Gaussians. So I like to think of them as a generalized twin of KMeans. The primary difference is that: - KMeans learns centroids. - GMM learn a distribution. For instance, in 2 dimensions: - while KMeans can only create circular clusters - A GMM can create oval-shaped clusters. Their effectiveness over KMeans is evident from the image below. - KMeans just relies on distance and ignores the distribution of each cluster - GMM learns the distribution and produces better clustering. How does it work? In a gist, it uses expectation maximization (EM), which is an iterative optimization technique to estimate the parameters of statistical models. The core idea behind EM is as follows: - Make a fair guess about the parameters - E-step: Compute the posterior probabilities of the unobserved variable using current parameters. - Define the “expected likelihood” function using the posterior probabilities. - M-step: Update the current parameters by maximizing the “expected likelihood”. - Use the updated parameters to recompute the posterior probabilities, i.e., Back to E-step. - Repeat until convergence. I covered this in detail here: https://lnkd.in/dpKJzb9v -- If you want to learn AI/ML engineering, I have put together a free PDF (530+ pages) with 150+ core DS/ML lessons. Get here: https://lnkd.in/gi6xKmDc -- 👉Over to you: What are some other shortcomings of KMeans?

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📢 Starting a thread on Fine-tuning Large Language Models (LLMs) 📢 1/n ● Large Language Models (LLMs) have revolutionised computational systems' ability to understand and generate human language. Building upon traditional language models, LLMs address limitations in handling rare words, overfitting, and capturing intricate linguistic patterns. ● Fine-tuning is a crucial process that adapts pre-trained LLMs to specific tasks or domains. This involves further training the model on a smaller, targeted dataset, refining its pre-existing knowledge and enhancing performance on specific tasks with reduced data and computational requirements. ● The fine-tuning process typically involves a seven-stage pipeline: dataset preparation, model initialisation, training environment setup, fine-tuning, evaluation and validation, deployment, and monitoring and maintenance. Each stage is essential for tailoring the LLM to meet specific requirements and ensuring optimal performance. ● There are different types of fine-tuning: unsupervised fine-tuning, supervised fine-tuning (SFT), and instruction fine-tuning via prompt engineering. The choice of method depends on the availability of labeled data and the specific task requirements. ● Parameter-Efficient Fine-Tuning (PEFT) techniques have emerged to address the computational challenges associated with fine-tuning large models. PEFT methods, such as LoRA, QLoRA, and adapters, allow for efficient fine-tuning with fewer parameters, reducing computational costs and mitigating overfitting. ● Evaluation and validation are critical for assessing the performance of fine-tuned LLMs. Metrics such as cross-entropy, perplexity, and accuracy are used to measure the model's ability to generalize and perform the target task effectively. ● Deployment of fine-tuned LLMs involves making them operational and accessible for specific applications. This includes setting up the necessary infrastructure, developing APIs, and optimizing model performance for inference. ● Monitoring and maintenance are essential to ensure the ongoing performance and reliability of deployed models. Continuous monitoring of metrics, retraining on updated data, and addressing potential biases or security vulnerabilities are crucial for maintaining model effectiveness and ethical considerations. ● Various platforms and frameworks facilitate LLM fine-tuning, such as Hugging Face, OpenAI, Google, and NVIDIA. These platforms provide tools for data preparation, model selection, hyperparameter tuning, and deployment, streamlining the fine-tuning process. ● Fine-tuning LLMs raises ethical considerations regarding bias, fairness, accountability, and transparency. Addressing these concerns is vital for responsible AI development, ensuring that models are deployed ethically and their outputs are trustworthy. Stay tuned for upcoming posts 📬 where I go through these in detail 📤 .

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𝐌𝐚𝐡𝐚𝐫𝐚𝐬𝐡𝐭𝐫𝐚 & 𝐔.𝐒. 𝐄𝐱𝐢𝐭 𝐏𝐨𝐥𝐥𝐬: 𝐂𝐚𝐧 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐊𝐞𝐞𝐩 𝐔𝐩? 🗳️📊 The Maharashtra exit polls landed yesterday, and last week, the U.S. polls were trending everywhere. But here’s the real question: 𝐖𝐡𝐲 𝐝𝐨𝐞𝐬 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐦𝐨𝐝𝐞𝐥𝐥𝐢𝐧𝐠, 𝐞𝐯𝐞𝐧 𝐰𝐢𝐭𝐡 𝐜𝐮𝐭𝐭𝐢𝐧𝐠-𝐞𝐝𝐠𝐞 𝐀𝐈, 𝐬𝐭𝐢𝐥𝐥 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐞 𝐰𝐢𝐭𝐡 𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧𝐬? 𝐖𝐡𝐲 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 𝐅𝐚𝐥𝐥 𝐒𝐡𝐨𝐫𝐭: ❓ How can models adapt when up to 10% of voters decide on election day? ❓ What happens when exit polls sample only 5-10% of voters, often skewed toward urban and affluent groups? ❓ Can we avoid overfitting to outdated coalition patterns when history suddenly changes? ❓ How do we manage voter data with hundreds of variables while avoiding overwhelming complexity? 𝐇𝐨𝐰 𝐖𝐞 𝐂𝐚𝐧 𝐃𝐨 𝐁𝐞𝐭𝐭𝐞𝐫: ✨ 𝐇𝐲𝐛𝐫𝐢𝐝 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐞𝐬:: Bayesian models paired with ML to better manage uncertainty. ✨ 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬:Real-time adjustments with reinforcement learning. ✨ 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Combine exit polls, social media sentiment, and even economic indicators. ✨ 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐀𝐈: Build trust using tools like SHAP and LIME to interpret predictions. 𝐓𝐡𝐞 𝐑𝐨𝐚𝐝 𝐀𝐡𝐞𝐚𝐝 🚀: Elections are unpredictable, but that’s what makes refining predictive modelling so exciting. By addressing biases, embracing real-time adaptability, and leveraging advanced methods, we can better understand voter dynamics. Also, read my recent article experimenting with U.S. election predictions on Towards AI - "https://lnkd.in/gZ7h-2cD " 𝐖𝐡𝐚𝐭 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐨𝐫 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐜𝐨𝐮𝐥𝐝 𝐫𝐞𝐝𝐞𝐟𝐢𝐧𝐞 𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐟𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠? 𝐃𝐫𝐨𝐩 𝐲𝐨𝐮𝐫 𝐢𝐝𝐞𝐚𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐜𝐨𝐦𝐦𝐞𝐧𝐭𝐬 𝐛𝐞𝐥𝐨𝐰! 💬 🔔 Love Machine Learning insights? Follow Sanjay Nandakumar for more thought-provoking posts, cutting-edge ideas, and deep dives into ML applications! 🚀 💬 React ❤️ | Comment 💭 | Repost 🔄 #ElectionForecasting #PredictiveModeling #MachineLearning #DataScience #AI #Statistics #DeepLearning #ArtificialIntelligence #ExitPolls #Maharashtra #MaharashtraEletion2024 #USA #USElections2024 #Trump

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100 𝐌𝐨𝐬𝐭 𝐈𝐧𝐟𝐥𝐮𝐞𝐧𝐭𝐢𝐚𝐥 𝐏𝐞𝐨𝐩𝐥𝐞 𝐢𝐧 𝐀𝐈 (𝐓𝐢𝐦𝐞 𝐌𝐚𝐠𝐚𝐳𝐢𝐧𝐞) Surprises in this list [1] Elon Musk missing in the list [2] Anil Kapoor in the list. #timemagazine #ai #generativeai #nlproc #llms #top100 #influential #people

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