Linmiao Zhang

📣 Machine learning developers: Check out this very technical webinar by our very own Milos Puzovic where he details how to get the most out of Llama on Arm-based AWS Graviton instances.. #onarm #ml Ed Miller

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Great to see Pytorch roadmap showing strong investment in composability as strategy for handling scaling. When it comes to scaling hardware customized high performance algorithms on specialized ML accelerators, composability still holds the key but is a challenge. At d-Matrix, we are building our compiler roadmap to address this challenge.

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✅ Master LLM #inference optimization with this getting-started guide that covers the most pressing challenges in inference along with practical solutions for developers. ➡️ https://nvda.ws/3zBFDU8

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In the final part of the article series on Deep Reinforcement Learning (DRL), we conclude the eleven-part series by overviewing some of the key extensions of Markov decision processes (MDPs). #data #analytics #ai #ml #datascience #designedanalytics #deeplearning #deepreinforcementlearning https://lnkd.in/gd_dATEP

Hello All! It is great to be back with another article in my series “Neural Networks Intuitions - 18. Generative Pretrained Transformer(GPT) Series". The past 1.5-2 years have been very much dominated by Large Language Models(LLMs) ever since the release of GPT-3.5 and GPT-4, their fantastic ability to solve wide range of natural language tasks — right from question answering, summarization, visual QA and even code generation. And anyone who has followed this recent trend would know about the talks regarding the emergence of AGI which led to two school of thoughts — one set of people who truly believe that Language Models can lead us to AGI, while the other set sharply denies this statement by arguing that although there are a few signs of reasoning, there is no true extrapolation as such and all of it comes down to the training data(and its scale). The latter group's criticism is that unless there is visibility into the training data distribution and having a test set(without contaminating the test data with train set) along with a proper evaluation methodology, it is unfair to say that these LLMs can actually lead to AGI. Now in order to truly validate some of these closed source GPT models(such as GPT-4, Gemini, Claude etc), we need to be aware of the training data distribution — which is highly unlikely. But another way to understand if LLMs or more specifically instructed tuned LLMs can actually generalize to novel distribution or not is by actually digging deep into the evolution of these architectures/algorithms — more specifically the evolution of GPT models. In this article, I will be going over a series of GPT papers - GPT-1, GPT-2, GPT-3 and InstructGPT, and try to understand what makes these models truly generalizable or universal. Do read and comment your thoughts/opinions :) https://lnkd.in/di8_VpJA #neuralnetworks #largelanguagemodels #GPT #incontextlearning #nlp

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Sign up now for our Introduction to Causal Inference course, where Mulusew J. Gerbaba Associate Research Scientist – Impact Evaluation at APHRC will dive into the methods of causal inference using observational data. You'll explore topics like potential outcomes, DAGs and causal graphs, target trial emulation, matching techniques, managing confounding and bias, causal structure learning, and quasi-experimental methods. Don't miss this opportunity to acquaint yourself with modern causal modeling methodology. Register today! https://lnkd.in/dcMnHVzX #MentalHealthDataPrizeAfrica #MentalHealthAwareness #DataForChange

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https://lnkd.in/gpPTGKM4 - enjoyed the latest short course from deeplearning.ai and Google. The course is good as a starting point on multi-modal prompts, but expect the model outputs to be a little more professional, like for ex: in the example of 'Interior design' - it would have been great if the model output gave the updated Interior with the new Furniture added, rather than a textual output, saying which Furniture does / does not fit in with the original design. I believe such systems are at use in many of the latest stores like IKEA. But overall a great course.

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