Devanshu Litoria

Don't miss Kartikay Khandelwal of Meta on stage at PyTorch Conference 2024, September 18-19 in San Francisco! Explore the list of keynote speakers: https://hubs.la/Q02NfBKt0. Register & join us next week in San Francisco! https://hubs.la/Q02NfwBx0 #PyTorchConf #PyTorch #PyTorchFoundation #AI #MachineLearning #ML #DeepLearning #OpenSource #OpenSourceSoftware #OpenSourceDevelopment #OpenSourceCommunity #OSS #LinuxFoundation #events #linux #SoftwareSupplyChainNews

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Paanduv Applications Launching Quant Pracar: Democratizing Quantum AI Education in India Register for software access (Free): https://lnkd.in/gYf-_JqC (Note: For a live demo join our webinar: https://bit.ly/3NvJczp) GET READY! to enter the exciting world of Quantum Artificial Intelligence (AI)! Paanduv Applications, a leader in scientific software development, has announced the launch of Quant Pracar, the educational software designed specifically for quantum machine learning. Quant Pracar's mission is to make quantum computing accessible to everyone. This innovative software offers students and professionals a unique opportunity to: -Engage with cutting-edge quantum machine learning algorithms: Dive deep into the world of quantum AI and explore its potential to revolutionize various fields. -Learn by doing: Upload your own datasets and train them using quantum machine learning techniques, gaining practical experience with this revolutionary technology. -Embrace open-source and cross-platform support: Quant Pracar's commitment to open-source development ensures everyone has access to this powerful tool. Additionally, its cross-platform compatibility makes it usable on a variety of devices. This launch marks a significant step towards democratizing quantum computing in India. By fostering widespread interest and equipping users with the necessary skills, Paanduv Applications aims to position India as a leader in quantum computing software development. Are you ready to embark on your quantum AI journey? Stay tuned for more information on the official release of Quant Pracar by Paanduv Applications! #quantum #AI #machinelearning #technology #innovation #engineering #quantumcomputing

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vGPU, MIG, & Time-Slicing! 3 ways to optimize the #GPU resources to meet the dynamic demands of computational tasks💻 Sameer Kulkarni explains & compares these methods in the blog post to help you select the right GPU optimization for your models👇 https://lnkd.in/gFQPX4b4

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[NEW COUPON] 2025 Fine Tuning LLM with Hugging Face Transformers for NLP Course Master Transformer models like Phi2, LLAMA; BERT variants, and distillation for advanced NLP applications on custom data Created by Laxmi Kant | KGP Talkie | 16.5 hours on-demand video course | 1 downloadable resource Fine Tuning LLM Course Overview Welcome to “Mastering Transformer Models and LLM Fine Tuning”, a comprehensive and practical course designed for all levels, from beginners to advanced practitioners in Natural Language Processing (NLP). This course delves deep into the world of Transformer models, fine-tuning techniques, and knowledge distillation, with a special focus on popular BERT variants like Phi2, LLAMA, T5, BERT, DistilBERT, MobileBERT, and TinyBERT. Get Udemy Coupon Code: https://lnkd.in/dr_KGEfw #LLM #Huggingface #NLP 

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🚀 Calling all TensorFlow enthusiasts! Explore our latest blog for comprehensive guidance on TensorFlow Quantization. From model conversion to TFLite to accuracy verification, optimize your models for mobile deployment effortlessly. 💡 📱 Don't miss out - start quantizing today: https://lnkd.in/gK3vC-5f #TensorFlow #Quantization #MobileOptimization #AI

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Struggling with integration in calculus? Dive into our latest blog post for a thorough guide on fundamental integration formulas. From rational and irrational functions to trigonometric, hyperbolic, exponential, and logarithmic integrals, we've got you covered. Perfect for JEE, NEET, and beyond! https://bit.ly/3Wo2muKK #Calculus #Integration #JainGroup #JainCollege

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💡 Optimizing your AI's retrieval process can drastically reduce errors and boost performance. In this article, Atita Arora details advanced strategies for optimizing RAG systems through an evaluation-based methodology. Here are some of the insights we had using Qdrant and Quotient AI: ✅ Low chunk relevance and context relevance make the model more likely to hallucinate. ✅ Retrieving more documents and reducing chunk size boosts model performance. ✅ Dynamic retrieval adapts to query variations, improving response accuracy. ✅ Changing LLMs or prompts affects response quality, requiring fine-tuning for better performance. Follow along with her four-step experiment and try a pipeline in various ways: https://buff.ly/3zaTnVG Github Notebook: https://lnkd.in/dbKGgFTT Workshop on YouTube: https://lnkd.in/dqGWkqhm

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Quotient AI helps developers measure the quality of their RAG systems - so they know how to improve them. Read the latest case study with Qdrant 👇

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Benchmarking Federated Learning for Large Language Models with FedLLM-Bench Quick read: https://lnkd.in/g2qf5YBp Paper: https://lnkd.in/gV4JCdYG

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Using Depthwise Separable Convolutions in Tensorflow https://zurl.co/rrST

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A Survey of RAG and RAU: Advancing Natural Language Processing with Retrieval-Augmented Language Models Quick read: https://lnkd.in/gnGqk44W Paper: https://lnkd.in/g8-VftbX #llms #largelanguagemodels #ai

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⌛ Don't miss Agriya Khetarpal's session on Python in WebAssembly via Pyodide at #PyConIndia2024.📊 Discover how the PyData ecosystem is evolving for the web! ➡️ https://in.pycon.org/2024/ #SciPy #OpenSource

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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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🔍 Ever wondered how your computer stores all your data? This insightful read "Crafting Magical Memory: Building RAM from Mystical Bits" by Vinod M, guides you through building your own RAM from scratch. And uncover the magic behind the technology. 🔗 Here: https://hshno.de/J6gmor9 Want your article to be featured on Hashnode & get the recognition it deserves? Follow this basic criteria ➡️ https://hshno.de/MogP7o0 #RAM #computerstorage #developers #programming

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Join Aakash Kaushik and Priya S as they take the stage to discuss 'Tune Assistant Makes LLMs Smarter' at PyCon India 2024. See you at the session! Tune AI | PyCon India 2024

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Explore KL Divergence and its role in measuring the inefficiency of using one probability distribution to approximate another. Whether in data compression or reinforcement learning, this concept is fundamental. Check out Saankhya Mondal's latest article for insights. #MachineLearning #Programming

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Can we parallelise model training like transformers yet scale linearly to long sequence length just like RNN? 🤔 Yup, its possible with SSMs, infact they can do inference at computation cost of O(1). 🤯 First of all, how does it even compare with the most advanced Transformer capabilities like, - Self-Attention Mechanisms - Multi-Head Attention - Quadratic Scaling, etc. All of these have been outperformed on the new benchmarks across various domains such as language, audio, and genomics by Mamba. 🏆 Your model might be excelling with the above methods, but does it handle million-length sequences efficiently? Or process long sequences without breaking a sweat? Mamba efficiently integrates a selective state space model. What it effectively does for sequence processing is: ⚡ - Dynamically selects relevant information while discarding irrelevant data. - Operates with linear time complexity, ensuring fast and efficient processing. - Utilizes a hardware-aware parallel algorithm that maximizes GPU efficiency. - Incorporates simplified end-to-end neural network architecture without attention or MLP blocks. And during sequence modeling, - The input is processed through a selective mechanism, allowing the model to focus on the most relevant parts. - The state space parameters (such as Δ, B, and C) are dynamically adjusted based on the input, enhancing content-aware processing. - Efficient recurrent mode computation ensures faster training and inference with lower memory usage. But this approach doesn't just stop at sequence processing; it extends to real-world applications across different domains. For example: - Language modeling with Mamba outperforms Transformers of similar and even larger sizes in both pretraining and downstream evaluations. - Audio modeling with Mamba provides better quality and efficiency, making strides in speech recognition and generation. - Genomic sequence analysis with Mamba achieves state-of-the-art performance, handling long DNA sequences with ease. Mamba is not just an architectural improvement; it's a revolution in sequence modeling. I'll link the research paper down in comments, and you can explore its capabilities further. For more such detailed explanations, follow me. See ya! . . . #Mamba #SequenceModeling #AI #NeuralNetworks #Efficiency #StateSpaceModel #LanguageModeling #AudioModeling #Genomics #DeepLearning

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Revised Evaluation of LLM Compression Using Distance Metrics Researchers from Microsoft Research, India, propose an advanced evaluation framework incorporating distance metrics like KL-Divergence and percentage flips alongside traditional accuracy metrics. https://is.gd/MDzsSN #ai #aitechnology #artificialintelligence #llm #llmcompression #machinelearning

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