Zahra Sarmast

Curious about Curse of Dimensionality, Context Window Paradox, Lost in the Middle Problem in LLMs and more? Catch Jerry Liu, Co-founder and CEO of LlamaIndex, and Raja Iqbal, CEO and Chief Data Scientist at Data Science Dojo, simplifying these complex topics for you. Tune in to our Future of Data and AI Podcast now! https://hubs.la/Q02tGlJ30 #generativeai #ai #artificialintelligence #llms #rag #finetuning #chatgpt #llamaindex #data #podcast #technology

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TensorFlow vs. PyTorch: Finding the best Deep Learning framework for your AI journey 🚀 Which framework will boost your career goals? Whether you’re excited about diving into cutting-edge research with PyTorch or eager to create scalable AI models with TensorFlow, each option has its own perks! From how easy they are to use and the support you can get from the community to their flexibility and scalability, understanding these differences can really set you up for success in the fast-paced world of AI. I think Juliette Carreiro's article for @ironhack is a fantastic resource for anyone starting their journey in Data Science and Machine Learning! On another note, I’m still looking for a German-speaking Data Manager to join a leading company on an exciting automotive project in Frankfurt. If this sounds like a good fit for you or someone you know, please reach out here or at [email protected]. Happy to share more details! #DeepLearning #AI #DataScience #MachineLearning #TensorFlow #PyTorch #CareerInTech #Hiring #ironhack #redsofa #datamanager

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- MobileCLIP-S0 achieves comparable zero-shot performance to OpenAI's ViT-B/16, with a 4.8x speed improvement and 2.8x smaller size. - MobileCLIP-S2 outperforms SigLIP's ViT-B/16 in average zero-shot performance, boasting 2.3x faster speed, 2.1x smaller size, and trained with 3x fewer seen samples. - MobileCLIP-B(LT) achieves a remarkable zero-shot ImageNet performance of 77.2%, surpassing recent benchmarks like DFN and SigLIP with similar architectures, as well as OpenAI's ViT-L/14@336. Code: https://lnkd.in/gQfUpcQT Paper: https://lnkd.in/gHGux7MS

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DVA is not associated with this job posting Senior Data Scientist, Causal Inference USA https://lnkd.in/gcSzM63D Discord is looking for an experienced, proactive, and self-driven Senior Causal Inference Data Scientist to join our Experimentation Platform team. The Experimentation Platform puts data at the heart of the company’s decision-making and growth by enabling rapid and accurate product experimentation. Discord runs hundreds of experiments on any given day, turning experiment results into business decisions. At the same time, we are taking a fresh look at our platform, and planning to rebuild parts of it from scratch to take it to the next level. As a Causal Inference Data Scientist on this team, you will ensure that the statistical underpinnings of this rebuild are sound, that experimenters are able to design experiments with high rigor, and that Discord makes the best business decisions on the basis of these experiments. We are a small and quickly growing team; by joining us as an early team member, you will be presented with significant leadership opportunities as we evolve. Our team directly impacts the strategy and roadmaps that improve Discord for its more than 200M (and many more future) users! If helping make Discord an even better place to hang out with your friends sounds like an exciting challenge - we'd love to chat with you! Check out our blog about how our team informs strategy & innovation at Discord here! #employeeexperience #people #culture #hiring #talent #skills #passion #leadership #hr #humanresources #team #strategy #hrstrategy #business #team #careers #employment #jobs #hiring #job #jobsearch #recruitment #career #work #careers #recruiting #nowhiring #resume #jobhunt #business #jobseekers #jobopening #jobseeker #hiringnow #interview #jobsearching #vacancy #cfbr #education #jobinterview #jobopportunity #employmentopportunities

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Explore how algebraic functions are utilized in the real world 🌍 in CourseKata's "Algebra+Data Science" summer study group! 🕒 Mon/Wed/Fri, 2:15 - 3:15 PM PT 📅 July 15-26, 2024 🔗 Register: https://hubs.li/Q02ys5c80 Get inspired about algebra through data this summer!

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Text embeddings have become crucial for transforming textual data into dense vector representations (with context), enabling efficient processing across various modalities like text, images, and audio. This article provides a good summary on some of these (not all) embeddings available for text https://lnkd.in/gKp4ta9a 1. Gensim Word Embeddings (Free) Why Use It: Specializes in NLP with open-source models like Word2Vec and FastText, promoting transparency and ease of custom training. 2. Facebook Embeddings (Free) Why Use It: Trained on extensive corpora and supports over 100 languages, making it versatile for global applications. 3. AllenNLP Embeddings (Free) Why Use It: Provides NLP-focused embeddings like BERT and ELMo, with tools for fine-tuning and visualization. 4. OpenAI Embeddings (Paid) Why Use It: Comprehensive training on massive datasets allows for effective semantic understanding, making it suitable for advanced NLP tasks. Ideal for tasks requiring zero-shot learning. 5. MultiLingual BERT (Free) Why Use It: Handles multilingual datasets effectively, offering robust support for multiple languages. Perfect for applications requiring consistent performance across different languages, such as global customer support systems. 6. DistilBERT Base Uncased (Free) Why Use It: Smaller and faster than BERT, maintaining near-comparable performance with reduced resource usage. 7. MPNet V2 (Free) Why Use It: Excels in text similarity tasks with a Siamese architecture, providing accurate similarity measurements. 8. HuggingFace Embeddings (Free/Paid) Why Use It: Offers versatility with a wide range of embeddings (text, image, audio) and the ability to fine-tune on custom data. HF also provides wrappers to almost all open sourced models. 9. Longformer Base 4096 (Free) Why Use It: Supports long texts up to 4096 tokens, making it unique in handling lengthy documents without truncation. 10. RoBERTa (2022) (Free) Why Use It: Offers updated text representations with comprehensive training on recent data. 11. GTE-Base (Free) Why Use It: Offers a good balance of performance and resource requirements with a 768-dimensional embedding. 12. GTE-Large (Free) Why Use It: Provides high-quality embeddings with more detailed representations, useful for nuanced text analysis. Best for applications requiring in-depth text understanding, such as detailed content recommendations. 13. GTE-Small (Free) Why Use It: Optimized for speed with a smaller model size, making it efficient for applications needing quick processing. Useful for real-time systems where fast text similarity searches are critical. 14. E5-Small (Free) Why Use It: Combines compact size with good performance, making it suitable for fast, general-purpose text embedding tasks.

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AI tip of the day: How do you know what LLM suits your GenAI project? 🤔 There is a tradeoff between LLM quality, speed (latency), and price - the LLM holy trinity. 😇 I used to struggle to compare models and synthesize this information until I came across Artificial Analysis. The site provides an independent analysis of various providers' AI models. 🚀 Check it out, it will save you a lot of time and money! 🙌

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Learn how you can convert a PyTorch model, trained on the web, to a mobile-optimized format. Madhur Zanwar describes the process using a few lines of code. He also discusses his journey and how one approach didn't yield satisfactory results while trying to run it in a React Native app. #machinelearning #pytorch #computervision #edgeml #reactnative #objectdetection #yolov5

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This is a really insightful piece by Mauricio Anderson from X3M about orchestrating the training of online ML models with Apache Airflow! 🚀 Check out the article to learn how X3M leverages Airflow for efficient and scalable ML model training below! #ApacheAirflow #MachineLearning #DataScience #X3M #AI #TechInnovation

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Explore how companies can benefit from causal inference to improve strategies and outcomes. Read more from Aleksander Molak now! #MachineLearning #AI

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🚀 We are excited to share the latest publication in the realm of statistical modeling, authored by Huiling Liu, Xinmin Li, Feifei Chen, Wolfgang Härdle, and Hua Liang! Their comprehensive study, published by Springer Nature, provides a detailed comparison of goodness-of-fit (GoF) tests for logistic regression models. Check out the full study for in-depth insights and applications to real-world datasets. Kudos to the authors for this significant contribution! https://lnkd.in/djemECrX #Research #LogisticRegression #GoodnessOfFit #StatisticalModeling #SpringerNature

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"In this article, I’ll show how a simple change — literally adding one line of code — can transform a traditional ML model (like Random Forest, LightGBM, CatBoost, etc.) into a reliable tool for answering causal questions." Samuele Mazzanti is back with a new post on causal ML models.

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An easy step-by-step guide to getting started with Neural Networks for Time Series Forecasting by Lucas See. Check it out now! #TimeSeries #Forecasting #NeuralNetworks

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In this week's blog post, Data Science Fellow Sharon H. Green, PhD, MPH writes about how to create powerful visualizations for propensity score matching in R. If you're new to causal inference and trying to learn what tools are available to you, this is a great blog post to check out! Subscribe to our Medium for weekly blog posts on all topics data science, social science, and social justice. https://lnkd.in/epgPtduB

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Read more about how we did in MLCommons' latest MLPerf™ Tiny v1.2 benchmark suite. We'll also be demoing our next-gen Core 3 architecture at this week's tinyML Summit in Burlingame hosted by the tinyML Foundation. #tinyML #edgeAI #tinymlSummit #MLPerf #MLCommons

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Exploring non-linear relationships in regression? Transformations can lead to trillions of potential models, but is there a better way? Samuele Mazzanti's article discusses how gradient-boosting models can provide effective solutions with ease. #Regression #DataScience

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