Deci
Qu'est-ce que Deci ?
Deci est une plateforme de développement d'apprentissage profond qui permet aux développeurs d'accélérer l'inférence sur le cloud ou sur le périphérique, d'atteindre plus rapidement la production et de maximiser le potentiel du matériel.
Comment utiliser Deci ?
Pour utiliser Deci, les développeurs peuvent choisir parmi une gamme de modules de la plateforme, y compris les modèles fondamentaux et les modèles personnalisés. Ils peuvent entraîner leurs modèles en utilisant la bibliothèque et les recettes personnalisées de Deci, optimiser et déployer leurs modèles en utilisant les techniques d'accélération de Deci, et exécuter l'inférence auto-hébergée n'importe où. Deci propose également des solutions pour des cas d'utilisation spécifiques dans des secteurs tels que l'automobile, le commerce de détail intelligent, le secteur public, la fabrication intelligente et l'analyse vidéo.
Fonctionnalités de base de Deci
Accélération de l'inférence sur le cloud ou sur le périphérique
Production plus rapide
Maximisation du potentiel du matériel
Les cas d'utilisation de Deci
Optimisation des modèles d'IA générative
Exécution sur les appareils périphériques
Réduction des coûts du cloud
Réduction du temps de développement
Maximisation de l'utilisation du centre de données
FAQ de Deci
Quel est le modèle de tarification de Deci ?
Discorde Deci
Voici le Discord Deci : https://discord.com/invite/p9ecgRhDR8. Pour plus de messages Discord, veuillez cliquer ici(/fr/discord/p9ecgrhdr8).
Deci E-mail d'assistance, contact du service client et contact de remboursement, etc.
Plus de contacts, visitez la page Contactez-nous(https://deci.ai/contact/)
Société Deci
Deci Nom de l'entreprise : Deci .
Pour en savoir plus sur Deci, veuillez visiter la la page À propos de nous(https://deci.ai/about/) .
Deci Connexion
Lien de connexion Deci : https://auth.deci.ai/oauth/account/login
Deci S'inscrire
Deci Lien d'inscription : https://auth.deci.ai/oauth/account/sign-up
Deci Tarifs
Lien de tarification Deci : https://deci.ai/pricing/
Deci Facebook
Lien de Facebook Deci : https://www.facebook.com/Deci-AI-112702343883824
Deci Youtube
Lien de Youtube Deci : https://www.youtube.com/channel/UCOECbhIGxR8qrjD8dlKpRSw
Deci Linkedin
Lien de Linkedin Deci : https://www.linkedin.com/company/deciai
Deci Twitter
Lien de Twitter Deci : https://twitter.com/deci_ai
Deci Avis (0)
Analyse de Deci
Deci Analyse du trafic du site Web
Trafic géographique
Top 1 Régions
Analyse des utilisateurs Discord Deci
Latest user counts
Deci Lancer les intégrations
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Écoute des médias sociaux
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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