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From coding in a notebook to a clickable demo: a practical playbook for GenAI fast prototyping
Neeraj Kumar is driven by a passion for exploring the intersection of artificial intelligence (AI) and robotics.
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Python for Data Analysis: Key Stats and Trends
Neeraj Kumar is driven by a passion for exploring the intersection of artificial intelligence (AI) and robotics.
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How We Won the First Data-Centric AI Competition: Synaptic-AnN
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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How We Won the First Data-Centric AI Competition: Innotescus
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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How We Won the First Data-Centric AI Competition: KAIST – AIPRLab
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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How I Won the First Data-centric AI Competition: Johnson Kuan
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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How I Won the First Data-centric AI Competition: Mohammad Motamedi
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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How I Won the First Data-centric AI Competition: Divakar Roy
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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How I Won the First Data-centric AI Competition: Pierre-Louis Bescond
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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How We Won the First Data-centric AI Competition: GoDataDriven
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 launches ‘AI for Everyone,’ a new Coursera program aimed at business professionals
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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Announcing “AI for Everyone”: a new course from deeplearning.ai
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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AI Guru Andrew Ng on the Job Market of Tomorrow
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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deeplearning.ai: Announcing new Deep Learning courses on Coursera
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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