What is happening at DeepLearning.AI | Read more about company updates, news, and events we are leading for the AI community
At DeepLearning.AI, we recently partnered with CrewAI to build the course Design, Develop, and Deploy Multi-Agent Systems with CrewAI. In it, instructor João Moura (Co-founder and CEO of CrewAI) shows how developers can move beyond...
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DeepLearning.AI welcomed 1200 attendees (and a total audience of more than 1400) to its developer conference in Manhattan. We packed Convene Brookfield Place to capacity for speakers and representatives from Google, Anthropic, Amazon, Vercel, Groq,...
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With exclusive courses, tools, and community, DeepLearning.AI Pro is the one membership that keeps you at the forefront of AI.
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Neeraj Kumar is driven by a passion for exploring the intersection of artificial intelligence (AI) and robotics.
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Neeraj Kumar is driven by a passion for exploring the intersection of artificial intelligence (AI) and robotics.
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In this blog post, Synaptic-AnN, 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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In this blog post, Innotescus, 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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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.
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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.
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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.
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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.
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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.
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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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Andrew Ng, a computer scientist who led Google’s AI division, Google Brain, and formerly served as vice president and chief scientist at Baidu, is a veritable celebrity in the artificial intelligence (AI) industry. After leaving Baidu, he debuted...
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Dear Friends, I am excited to announce the newest course from deeplearning.ai, “AI for Everyone.” It will be available on Coursera in early 2019. AI is not only for engineers. This non-technical course...
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The co-founder of Google’s deep-learning research team on the promise of a conditional basic income, the need for a skills-based education system and what CEOs don’t understand about artificial intelligence Sentient artificial intelligence may take...
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Dear Friends, I have been working on three new AI projects, and am thrilled to announce the first one: deeplearning.ai, a project dedicated to disseminating AI knowledge, is launching a new sequence of Deep Learning courses on Coursera....
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