Don’t forget about our #AICodingWorkshop happening tomorrow at PHX Ventures (51 W 3rd St STE 105, Tempe) at 5 PM! We're teaming up with Global Career Network to empower Arizona’s tech community by offering a hands-on session designed to help developers kickstart their journey into AI. What to Expect: 🧠 Master BERT for Bias Detection: Dive deep into training BERT models to detect biases in text—a crucial skill in today’s AI landscape. This semi-technical workshop provides a practical coding experience and offers a sneak peek into the NLP techniques we'll explore in our upcoming research paper, GUS-Net (Sept. 2024). 💻 Hands-On Learning: We’ll guide you through the entire training process using a comprehensive notebook, supported by detailed explanations in a related article. Join us for this unique opportunity to learn, innovate, and contribute to Arizona's growing tech ecosystem. See you there! Sing up here - https://lnkd.in/gxmVmkg8
Ethical Spectacle Research’s Post
More Relevant Posts
-
Eleven days of time series - day 11 (the season finale of Time Series with Konrad): Transfer learning is a research problem in ML that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. IT has enabled progress in areas with limited data availability in both CV and NLP domains. Several modern applications of machine learning are built around TL, so it's only natural that people would start thinking about using this approach to time series: while less obvious to formulate (what does it mean to learn time series features across domains), the idea of transfer learning for time series has huge potential. In this notebook we explore the idea in some more detail.
-
-
Day 43 of my 50-Day PyTorch + Transformers NLP Challenge: Datasets Library (Part 2) Today’s Progress: - Basic Data Cleaning with the `datasets` Library: - Learned how to perform basic data cleaning tasks such as changing column names and converting all text to lowercase. - These tasks can also be accomplished using the Pandas library. For more details, see the attached PDF of the notebook. Resources: - [Hugging Face NLP Course](https://lnkd.in/dH7rcMpz) I'm thrilled to continue this journey and share my progress with you. Feedback and suggestions are always welcome! #PyTorch #DeepLearning #MachineLearning #AI #50DayChallenge
-
Experienced Senior UI Developer | Architecting, Designing, and Developing with React JS, Redux, Angular, Node.js, and MERN Stack Development | Proficient in Cloud Technologies
Just finished the course “TensorFlow: Working with NLP” by Jonathan Fernandes! Check it out: https://lnkd.in/gVw8ByDF #tensorflow #naturallanguageprocessing.
Certificate of Completion
linkedin.com
-
Just finished the course “TensorFlow: Working with NLP” by Jonathan Fernandes! Check it out: https://lnkd.in/dKd-6JbP #tensorflow #naturallanguageprocessing.
Certificate of Completion
linkedin.com
-
Just finished the course “TensorFlow: Working with NLP” by Jonathan Fernandes! Check it out: https://lnkd.in/gKyeZk_y #tensorflow #naturallanguageprocessing.
Certificate of Completion
linkedin.com
-
Just finished the course “TensorFlow: Working with NLP” by Jonathan Fernandes! Check it out: https://lnkd.in/g_Ps-HDC #tensorflow #naturallanguageprocessing.
Certificate of Completion
linkedin.com
-
Just finished the course “TensorFlow: Working with NLP” by Jonathan Fernandes! Check it out: https://lnkd.in/diwqzj3W #tensorflow #naturallanguageprocessing.
Certificate of Completion
linkedin.com
-
Just finished the course “TensorFlow: Working with NLP” by Jonathan Fernandes! Check it out: https://lnkd.in/gAgg_qVy #tensorflow #naturallanguageprocessing.
Certificate of Completion
linkedin.com
-
Just finished the course “TensorFlow: Working with NLP” by Jonathan Fernandes! Check it out: https://lnkd.in/eSkJWsiK #tensorflow #naturallanguageprocessing.
Certificate of Completion
linkedin.com
-
Researcher @ NTNU, Norway | ERCIM Fellow | PhD @ NITK | Federated Learning | Edge AI
🌟 Day 24 of #30DaysOfFLCode 🌟 Today, I dived into the fascinating world of Personalized Federated Learning (pFL) by exploring the research paper: "FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy" by Jianqing Zhang et al. The paper introduces FedCP, a novel approach that separates global and personalized feature information using a conditional policy at the sample level. This fine-grained personalization leads to improved performance across diverse domains like computer vision and NLP. The standout feature? FedCP's robustness to client dropouts, a common challenge in federated settings. Key takeaway: FedCP achieved up to 6.69% improvement over 11 state-of-the-art methods, showcasing its potential in privacy-conscious and collaborative learning environments. Check out their work here: https://lnkd.in/gZTGKn4Q GitHub repo: https://lnkd.in/gFVQbUVN #FederatedLearning #AIResearch #pFL #PrivacyTech
arxiv.org
