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🚀 Hey everyone, exciting news! We're diving deep into the world of AI/ML research and would love your input. 🤖 Join our vibrant WhatsApp group and let's explore the future together: https://lnkd.in/dK_GRYTt 🌟 Don't miss out on this awesome opportunity! Share it with your college groups and let's spark some amazing discussions! 🌐 #AI #ML #ResearchJourney

Say hello to GPT-4o, new flagship model which can reason across audio, vision, and text in real time: https://lnkd.in/gm-eFHU4 Text and image input rolling out today in API and ChatGPT with voice and video in the coming weeks. join our community - https://lnkd.in/gGCdX6rv #ai #ml #gpt4o #foduu #api #chatgpy #artificalinteligent #machinelearning

Top 10 machine Learning algorithms for beginners 👇👇 1. Linear Regression: A simple algorithm used for predicting a continuous value based on one or more input features. 2. Logistic Regression: Used for binary classification problems, where the output is a binary value (0 or 1). 3. Decision Trees: A versatile algorithm that can be used for both classification and regression tasks, based on a tree-like structure of decisions. 4. Random Forest: An ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of the model. 5. Support Vector Machines (SVM): Used for both classification and regression tasks, with the goal of finding the hyperplane that best separates the classes. 6. K-Nearest Neighbors (KNN): A simple algorithm that classifies a new data point based on the majority class of its k nearest neighbors in the feature space. 7. Naive Bayes: A probabilistic algorithm based on Bayes' theorem that is commonly used for text classification and spam filtering. 8. K-Means Clustering: An unsupervised learning algorithm used for clustering data points into k distinct groups based on similarity. 9. Principal Component Analysis (PCA): A dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information. 10. Gradient Boosting Machines (GBM): An ensemble learning method that builds a series of weak learners to create a strong predictive model through iterative optimization.

Distilling Diffusion Models into Conditional GANs project page: https://lnkd.in/gMQ4VYMJ abs: arxiv.org/abs/2405.05967 join Our AI Community - https://lnkd.in/gGCdX6rv Diffusion distillation treated as a paired image-to-image translation problem (noise → img). Train a simple conditional GAN with an LPIPS (perceptual) loss. However, the LPIPS loss is modified to work well with latents from the diffusion model + apply differentiable augmentations → Ensembled-LatentLPIPS Achieves better SDXL one-step distillation than SDXL-Turbo and SDXL-Lightning

It's finally here fellas! 🔥 Idefics2 is one of the strongest open-source vision-language models for its size. To celebrate its release, created a demo notebook showcasing how to fine-tune it for document AI use cases (e.g. receipt image -> JSON) https://lnkd.in/gcuE-Ts7 join our community - https://lnkd.in/gGCdX6rv

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New MoE alert! 🔔 DeepSeek V2 Chat just got released, a 236B parameter Mixture of Experts Model with a 128k context window and 21B active parameter. 🏎️⚡️ TL;DR 🧮 236B parameters with 21B activated during generation 👨🏫  160 experts with 6 active in generation 🚀 Matches Mixtral 8x22B on English benchmarks 🪟 Based & Chat model with 128k context 🔠 Trained on 8.1 trillion tokens 🌱 For inference in bf16 8x 80GB GPUs 📜 Custom License, commercially useable 🤗 Available on @huggingface ❌ Trained for English and Chinese language https://lnkd.in/gxcGui2x join our community - https://lnkd.in/gGCdX6rv

Did you know? By 2025, IT executives expect that 30% of business workloads will reside in the cloud and 28% will be in the public cloud. This statistic highlights the growing importance of cloud computing for businesses of all sizes. Cloud computing can offer a number of benefits, including: Increased agility and scalability Reduced costs Improved disaster recovery Enhanced collaboration follow FODUU - Web Design Company India join our community - https://lnkd.in/gGCdX6rv

Hermes 2 Pro on Llama-3 8B just released. 🔥 @NousResearch released their first Llama-3 fine-tune, outperforming Meta's Llama 3 8B instruct. 🤯 Hermes Pro is optimized for Function Calling and Structured Output capabilities using a dedicated token for tool call. TL;DR: 🧠 based on Llama 3 8B base ✨ Trained on an updated and cleaned OpenHermes 2.5 Dataset (not released) 🥇 Outperforms Llama 3 8B Instruct on AGIEval, GPT4All Suite, TruthfulQA, and BigBench. 💬 Supports multi-turn function calling and system prompts 🔡 New special tokens (<tools>, <tool_call>, <tool_response>) 🔨 90% on the function calling evaluation from @FireworksAI_HQ 🔥 Scored 84% on structured JSON output evaluation 🤔 Released under Apache 2.0 🤗 Available on @huggingface Model: https://lnkd.in/gZ5CxAsJ join our AI community- https://lnkd.in/gGCdX6rv The model was built by @teknium1, @intrstllrninja, @theemozilla, @karan4d, and @huemin_art.

Another open-source library related to training from the 🤗folks, and this time, we're targeting speaker diarization models 🎤 As always the focus is on simplicity and compute accessibility 🔮 https://lnkd.in/g5hT3HMm https://lnkd.in/gQX2DPDC