Deci
¿Qué es Deci?
Deci es una plataforma de desarrollo de aprendizaje profundo que permite a los desarrolladores acelerar la inferencia en el borde o en la nube, llegar a la producción más rápido y maximizar el potencial del hardware.
¿Cómo usar Deci?
Para utilizar Deci, los desarrolladores pueden elegir entre una variedad de módulos de plataforma, incluyendo modelos base y modelos personalizados. Pueden entrenar sus modelos utilizando la biblioteca y las recetas personalizadas de Deci, optimizar e implementar sus modelos utilizando las técnicas de aceleración de Deci, y ejecutar la inferencia alojada en cualquier lugar. Deci también ofrece soluciones para casos de uso específicos en industrias como automotriz, venta al por menor inteligente, sector público, fabricación inteligente y análisis de video.
Características principales de Deci
Aceleración de la inferencia en el borde o en la nube
Producción más rápida
Maximización del potencial del hardware
Casos de uso de Deci
Optimización de modelos generativos de IA
Ejecución en dispositivos en el borde
Reducción de costos en la nube
Reducción del tiempo de desarrollo
Maximización de la utilización del centro de datos
FAQ de Deci
¿Cuál es el modelo de precios de Deci?
Deci Discordia
Aquí está el Discord de Deci: https://discord.com/invite/p9ecgRhDR8. Para obtener más mensajes de Discord, haga clic aquí(/es/discord/p9ecgrhdr8).
Deci Correo electrónico de soporte, contacto de servicio al cliente, contacto de reembolso, etc.
Más contacto, visite la página de contacto(https://deci.ai/contact/)
Deci empresa
Deci Nombre de la empresa: Deci .
Más información sobre Deci, visite la página acerca de nosotros(https://deci.ai/about/) .
Deci Iniciar sesión
Deci Enlace de inicio de sesión: https://auth.deci.ai/oauth/account/login
Deci Registrarse
Deci Enlace de registro: https://auth.deci.ai/oauth/account/sign-up
Precios de Deci
Enlace de precios de Deci: https://deci.ai/pricing/
Facebook de Deci
Enlace de Facebook de Deci: https://www.facebook.com/Deci-AI-112702343883824
Youtube de Deci
Enlace de Youtube de Deci: https://www.youtube.com/channel/UCOECbhIGxR8qrjD8dlKpRSw
Linkedin de Deci
Enlace de Linkedin de Deci: https://www.linkedin.com/company/deciai
Twitter de Deci
Enlace de Twitter de Deci: https://twitter.com/deci_ai
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Build your own RAG (retrieval augmented generation) AI Chatbot using Python | Simple walkthrough
Showcasing Retrieval Augmented Generation (RAG) for #chatbots and a step-by-step tutorial on how to build one for yourself or others. The tutorial uses #langchain and #openai #gpt #largelanguagemodels Blog Post - https://medium.com/databutton/why-your-next-ai-product-needs-rag-implemented-in-it-9ee22f9770c8 GitHub Code - https://github.com/avrabyt/RAG-Chatbot Related videos : Streamlit ChatUI - https://youtu.be/sWVfGIiWmaQ Build PDF Chatbot - https://youtu.be/daMNGGPJkEE Build ChatGPT like Chatbot - https://youtu.be/cHjlperESbg Build Simple Chatbot - https://youtu.be/BHwVRI9N8B0 Blog to get started with Databutton - https://medium.com/databutton/build-a-personal-search-engine-web-app-using-open-ai-text-embeddings-d6541f32892d Resources Databutton - https://databutton.com/login?utm_source=youtube&utm_medium=avra&utm_article=rag LangChain RAG document — https://python.langchain.com/docs/expression_language/cookbook/retrieval LangChain Blog on RAG — https://deci.ai/blog/retrieval-augmented-generation-using-langchain/ Retrieval augmented generation: Keeping LLMs relevant and current — https://stackoverflow.blog/2023/10/18/retrieval-augmented-generation-keeping-llms-relevant-and-current/ LamaIndex RAG concepts — https://gpt-index.readthedocs.io/en/latest/getting_started/concepts.html What is RAG from IBM — https://research.ibm.com/blog/retrieval-augmented-generation-RAG RAG based YC Startups — https://medium.com/databutton/some-ycombinator-rag-startups-cba3cca88274 RAG article from MetaAI - https://arxiv.org/abs/2005.11401?source=post_page-----9ee22f9770c8--------------------------------
Meta Llama 3 Fine tuning, RAG, and Prompt Engineering for Drug Discovery
PDF: https://www.chemicalqdevice.com/meta-llama-3-fine-tuning-rag Large language models such as Meta's newly released Llama 3 have demonstrated state-of-the-art performance on standard benchmarks and real-world scenarios. (1) To further improve domain-specific generative AI answers, Fine-tuning on a different dataset, Prompt Engineering, and Retrieval Augmented Generation (RAG) are utilized to improve Llama 3 utility. For enhanced usability, Llama 3 text-generations may need additional modifications, provide additional context, or use a specialized vocabulary. Fine-tuning is the process of further training the original pre-trained Llama 3 using domain-specific dataset(s). Prompt engineering doesn’t involve re-training Llama 3, but is the process of "designing and refining the input given to a model to guide and influence the kind of output you want." RAG "combines prompt engineering with context retrieval from external data sources to improve the performance and relevance of LLMs." (2) The seminar will detail how to use Drug Discovery related datasets with the three LLM techniques mentioned above. The cover image depicts cancer drug candidate RTx-152 and residing Protein and DNA interactions, in Separate research. Fried, W., et al. Nature Communications. April 05, 2024. (A) 1) Meta AI: https://ai.meta.com/blog/meta-llama-3/ 2) Deci AI: https://deci.ai/blog/fine-tuning-peft-prompt-engineering-and-rag-which-one-is-right-for-you/ A) Nature Communications: https://www.nature.com/articles/s41467-024-46593-1 -CEO Kevin Kawchak
How to train a YOLO-NAS Pose Estimation Model on a custom dataset step-by-step
Inside my school and program, I teach you my system to become an AI engineer or freelancer. Life-time access, personal help by me and I will show you exactly how I went from below average student to making $250/hr. Join the High Earner AI Career Program here 👉 https://www.nicolai-nielsen.com/aicareer (PRICES WILL INCREASE SOON) You will also get access to all the technical courses inside the program, also the ones I plan to make in the future! Check out the technical courses below 👇 ___________________________________________________________ In this video 📝 we are going to take a look at how you can train a YOLO-NAS pose estimation model on a custom dataset. I'll walk you through every single step in the training pipeline, all the way from setting up and annotating your dataset, how to export it and use it in a Google Colab notebook. Then we will set up our data loaders and some configurations before starting the training of the custom model. At the end, we are of course going to take a look at inference with our best trained model. Colab Notebook: https://colab.research.google.com/drive/1OGX-SpoMX4gFiRck2XWMv3JPCH5Vqgiq?usp=sharing Deci models: https://deci.ai/foundation-models/ YOLO-NAS Pose Github repo: https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS-POSE.md Tiger Dataset and Config file: https://drive.google.com/drive/folders/1ZPHRV3x7d21r2ORt5qGajvx_RIMjCEyG?usp=share_link If you enjoyed this video, be sure to press the 👍 button so that I know what content you guys like to see. ___________________________________________________________ 🛠 Freelance Work: https://www.nicolai-nielsen.com/nncode ___________________________________________________________ 💻💰🛠 High Earner AI Career Program: https://www.nicolai-nielsen.com/aicareer 👨💻 Learn Software Engineering instead with my good friend Tech with Tim: https://coursecareers.com/a/NicolaiAI?course=software-dev-fundamentals 📈 Investment Course: https://www.nicos-school.com/p/investment-course ⚙ Real-world AI Technical Courses: (https://www.nicos-school.com) 📗 OpenCV GPU in Python: https://www.nicos-school.com/p/opencv-gpu-course 📕 YOLOv7 Object Detection: https://www.nicos-school.com/p/yolov7-custom-object-detection-with-deployment 📒 Transformer & Segmentation: https://www.nicos-school.com/p/transformer-and-segmentation-course 📙 YOLOv8 Object Tracking: https://www.nicos-school.com/p/yolov8-object-tracking-course 📘 Research Paper Implementation: https://www.nicos-school.com/p/research-paper-implementation 📔 CustomGPT: https://www.nicos-school.com/p/customgpt-course ___________________________________________________________ 📞 Connect with Me: 🌳 https://linktr.ee/nicolainielsen 🌍 My Website: https://www.nicolai-nielsen.com/ 🤖 GitHub: https://github.com/niconielsen32 👉 LinkedIn: https://www.linkedin.com/in/nicolaiai 🐦 X/Twitter: https://twitter.com/NielsenCV_AI 🌆 Instagram: https://www.instagram.com/nicolaicodes/ ___________________________________________________________ 🇦🇪 Want to move to Dubai and get visa? - Book a free consultation: https://www.genzoneconsulting.com/meetings/schedule-session/nny ___________________________________________________________ 📷 My camera calibration software, CharuCo Boards, and Checker boards Link to webshop: https://camera-calibrator.com ___________________________________________________________ Timestamps: 0:00 Intro 0:19 YOLO-NAS Pose Documentation 3:46 Annotate Dataset 9:19 Imports 10:23 Sneak-peak Inference 12:03 Setup Trainer 12:32 Download the Dataset 13:40 Configuration File Structure 16:11 COCO Dataset Structure 17:26 Utility functions 18:51 Dataloaders 20:29 Keypoint Transformation 20:53 Setup Transform 21:40 Train Dataloaders 22:09 Instantasing The Model 22:47 Training Parameters 24:14 Training The Model 25:48 Training Results 28:55 Inference Custom Model 31:10 Outro Tags: #YOLO-NAS-Pose #CustomPoseEstimation #supergradients #yolonas #poseestimation
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