Blog

How a Determined Entrepreneur Used Deep Learning to Grow His Business

December 23rd, 2021 | Community

Kai Saksela is the CEO of NL Acoustics, a Finnish technology startup that designs and manufactures AI products to analyze sounds. He took the Deep Learning Specialization primarily because he loves learning new skills and has been fascinated by the field for a long time. He also had a hunch that neural networks would help his company solve a core problem: providing customers guidance on what they should do when their equipment starts making strange noises. He spoke with us about how his hunch paid off and why AI plays a central role in his company’s growth.

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How Apala Guha Learned AI During Her Newborn Baby’s Naps

December 23rd, 2021 | Community

Apala Guha is a senior machine learning compiler engineer at Lightmatter, a Boston-area startup. Before that, she was a computer scientist who had always been interested in deep learning. When she quit her previous job at the beginning of 2020 to have a baby, she took advantage of the “time off” to take the Specialization.

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How Aleksandr Gontcharov Found Career Satisfaction With Deep Learning

December 23rd, 2021 | Community

Aleksandr Gontcharov is a software engineer at Microsoft. Early in his career, he moved from job to job, but none of them ever felt right. The Deep Learning Specialization helped him find his calling; he was hired for a machine learning role while still taking the courses. He spoke with us about why the Specialization was the spark that put his career in motion. 

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From Astrophysics to AI: Why Luciano Darriba Made the Switch

December 23rd, 2021 | Community

Luciano Darriba is an AI developer living in Buenos Aires, Argentina. In his former life, he was an astrophysicist. This wasn’t fulfilling him, so he took the Deep Learning Specialization in hopes of kickstarting a new trajectory. Less than a year later, he had a new job working at Baufest, a software services company.

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How We Won the First Data-Centric AI Competition: Synaptic-AnN

October 18th, 2021 | Community

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

October 18th, 2021 | Community

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

October 18th, 2021 | Community

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

October 18th, 2021 | Community

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

October 18th, 2021 | Community

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

October 18th, 2021 | Community

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

October 18th, 2021 | Community

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

October 18th, 2021 | Community

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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Expanding Access to Education

September 22nd, 2021 | Community

At DeepLearning.AI, we specialize in building high-quality learning experiences for people interested in machine learning and artificial intelligence. We aim to give everyone with an internet connection access to world-class education, and each time we launch a new course that delivers value for thousands of learners, we move one step closer to that goal. 

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