In this blog post, KAIST-AIPRLab, one of the winners of the Data-Centric AI Competition, describes techniques and strategies that led to victory. Participants received a fixed model architecture and a dataset of 1,500 handwritten Roman numerals. Their task was to optimize model performance solely by improving the dataset and dividing it into training and validation sets. The dataset size was capped at 10,000.
In this blog post, Johnson Kuan, one of the winners of the Data-Centric AI Competition, describes techniques and strategies that led to victory. Participants received a fixed model architecture and a dataset of 1,500 handwritten Roman numerals. Their task was to optimize model performance solely by improving the dataset and dividing it into training and validation sets. The dataset size was capped at 10,000.
In this blog post, Mohammad Motamedi, one of the winners of the Data-Centric AI Competition, describes techniques and strategies that led to victory. Participants received a fixed model architecture and a dataset of 1,500 handwritten Roman numerals. Their task was to optimize model performance solely by improving the dataset and dividing it into training and validation sets. The dataset size was capped at 10,000.
In this blog post, Divakar Roy, one of the winners of the Data-Centric AI Competition, describes techniques and strategies that led to victory. Participants received a fixed model architecture and a dataset of 1,500 handwritten Roman numerals. Their task was to optimize model performance solely by improving the dataset and dividing it into training and validation sets. The dataset size was capped at 10,000.
In this blog post, Pierre-Louis Bescond, one of the winners of the Data-Centric AI Competition, describes techniques and strategies that led to victory. Participants received a fixed model architecture and a dataset of 1,500 handwritten Roman numerals. Their task was to optimize model performance solely by improving the dataset and dividing it into training and validation sets. The dataset size was capped at 10,000.
In this blog post, GoDataDriven, one of the winners of the Data-Centric AI Competition, describes techniques and strategies that led to victory. Participants received a fixed model architecture and a dataset of 1,500 handwritten Roman numerals. Their task was to optimize model performance solely by improving the dataset and dividing it into training and validation sets. The dataset size was capped at 10,000.
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On April 13, DeepLearning.AI and Omdena assembled a panel of machine learning practitioners who shared their first-hand experience of going from non-traditional starting points to building a career in AI. For any aspiring machine learning engineers who missed the event, here’s a few of the speakers’ tips that we hope you’ll find helpful:
Title: Senior Deep Learning Engineer, Nvidia Location: Santa Clara, California Education: Bachelor of Technology, Electronics and Communication Engineering, Manipal University; MS, Electrical and Computer Engineering, Carnegie Mellon University Favorite areas: Natural language processing, autoML, and interpretable AI Favorite researchers: Christopher…
Kennedy Kamande Wangari got his first taste of AI just two years ago in Kenya. Since then, he has been sprinting towards a top-tier career by taking numerous online courses, working entry-level jobs, volunteering to grow his local AI community, and considering a startup. Next step: Earning a graduate degree. Kennedy shares lessons he learned from starting out at a breakneck pace and explains how he balances professional obligations with personal life.
Jade Abbott turned a childhood desire for a robotic best friend into a career training computers to understand human language. Having studied AI in school, she got her first job coding conventional software, but she found ways to apply machine learning to her work until that became central to her role. In the meantime, she founded an open source project to train NLP models on African languages. She spoke with us about her fascination with language, the importance of community, and how to incorporate wine into your learning practice.
Adrián Munguía spent almost two decades working to create AI-powered products in key science and engineering roles at Silicon Valley startups and Fortune 500 corporations globally. In 2019, he founded AI MEXICO with an aim to educate AI professionals and create AI solutions for companies.
Steffen Konrath studied business administration with a focus on marketing and computer science. He’s worked on numerous digital products and also created an AI community in Zurich called AI Suisse.He is fascinated with trying to find the truth beneath the surface and uses data for this purpose.
Yan Xu completed her PhD in electrical engineering and currently works as a senior scientist at PROS Inc in Houston to develop innovative machine learning algorithms for pricing and sales effectiveness in e-commerce. She creates machine learning applications to drive business growth across industries and spends her spare time speaking on AI and ML-related topics in public.
