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https://economictimes.indiatimes.com/jobs/c-suite/not-just-techies-companies-want-c-suite-leaders-too-to-have-ai-skills/articleshow/112931591.cms?from=mdr Growing relevance of AI, even beyond techies

https://github.com/rasbt/LLMs-from-scratch/tree/main

https://github.com/anthropics/anthropic-cookbook

Thanks, Aman. 😊 Hello, All! I'm pursuing academics in AI and Data Science under incumbent faculties of Harvard University and University of Pennsylvania. I blog at the Confluence ( www.kunalsconduit.wordpress.com ) I look forward to newer learnings from and contributing towards neuralnets.ai

I have written a blog series on Model Quantization here, where we have explored following topics Binary Representation of floating point numbers Bit Precision - how do CPUs and GPUs comprehend numbers Methods used for quantization and scaling range selection Types of quantization Do check it out, and let me know your thoughts !!!

For those who are short on GPU resources, https://ollama.com/ offers a vast library of quantized versions of top LLMs. Multiple quantization levels of each model are available. Tutorials to use Ollama with google colab: https://pub.towardsai.net/running-ollama-on-google-colab-free-tier-a-step-by-step-guide-9ef74b1f8f7a https://github.com/SkkJodhpur/Gen-ai/blob/main/Models/Ollama_3_A_Step_by_Step_Guide/Ollama_3_A_Step_by_Step_Guide.ipynb https://digitalitskills.com/running-ollama-on-google-colab-and-using-llm-models-for-free/

A non exhaustive list of resources to learn advanced NumPy: https://scipy-lectures.org/advanced/advanced_numpy/ https://medium.com/analytics-vidhya/advanced-numpy-218584c60c63 https://aaltoscicomp.github.io/python-for-scicomp/numpy-advanced/ https://www.youtube.com/watch?v=1NfIZD1xBxQ

The sequel tutorial from W3 school is a very good tutorial to learns basic and intermediate topics on sql. It covers intro, basic commands, table joins, databases etc. The tutorial is handy for those who want to get started as well as those who want to revise a concept.

VideoBERT: A Joint Model for Video and Language Representation Learning - ICCV 2019 - Self-supervised learning has become increasingly important to leverage the abundance of unlabeled data available on platforms like YouTube. Whereas most existing approaches learn low-level representations, the paper proposes a joint visual-linguistic model to learn high-level features without any explicit supervision. In particular, the authors build upon the BERT model to learn bidirectional joint distributions over sequences of visual and linguistic tokens, derived from vector quantization of video data and off-the-shelf speech recognition outputs, respectively. VideoBERT is used n numerous tasks, including action classification and video captioning. VideoBERT can be applied directly to open-vocabulary classification, and confirm that large amounts of training data and cross-modal information are critical to performance. Furthermore, it outperform the state-of-the-art on video captioning, and quantitative results verify that the model learns high-level semantic features.

Andrej Karpathy completed his PhD from Stanford. He is one of the founders of openAI. His Youtube have many informative videos - from basics to advanced topics. Youtube channel : https://www.youtube.com/@AndrejKarpathy/featured

A non exhaustive list of tutorials for beginners https://www.tensorflow.org/tutorials/quickstart/beginner - Train and evaluate an image classifier https://www.datacamp.com/tutorial/tensorflow-tutorial - detailed tutorial by data camp which teaches basics including installing the library and training your own model https://www.geeksforgeeks.org/tensorflow/ - list of many TF functions https://www.simplilearn.com/tutorials/deep-learning-tutorial/tensorflow - linear regression in TF. Also explains what is a tensor.

My name is Corey and I have an interest in learning A.I., Machine Learning, and Blockchain Development. I obtained my CompTia A+ last year, so I'm definitely a freshman in this space. I'm currently taking a Web Development (Learning Javascript) class and plan to begin this roadmap to A.I. after I complete that. I look forward to gaining insight from you all. Thanks for creating this platform.

#AITends #GenAITrends

Welcome, @lodini! Looking forward to seeing you around on NeuralNets!

https://github.com/openai/openai-cookbook

https://github.com/langchain-ai/langchain

A Next-Generation Model for Real-Time Object Segmentation in Images and Videos. Meta has announced the release of SAM 2, a unified model for real-time promptable object segmentation in images and videos. This model can segment any object in any video or image, even if it has not been seen before, and can be used in a wide range of applications, from video editing to medical research. SAM 2 is the next generation of the Meta Segment Anything Model (SAM), which was released last year and has been used in various applications, including image segmentation and video editing. Do check out here for more information. Try it out for yourself here.

Learn the mathematical concepts behind ML. This book by Deisenroth, Faisal, and Ong is not intended to cover advanced machine learning techniques but instead aims to provide the necessary mathematical skills to read those other books. link: https://mml-book.github.io/