Working AI: How a Determined Entrepreneur Used Deep Learning to Grow His Business
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Working AI: How a Determined Entrepreneur Used Deep Learning to Grow His Business

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.

Matt Struble, senior machine learning engineer for a major sportswear company, sits at his home workstation.
Breaking Into AI

A Career in AI Felt Impossible. Then He Took the Course That Changed Everything

Matt Struble is an engineer at a sportswear company, where he currently leads a team that’s developing a deep learning system for predicting shoe trends a year or two into the future. Before taking the Deep Learning Specialization, he was a computer programmer who watched data scientists from afar.

How Deep Learning Helped an IT Manager Find New Career Satisfaction After Age 40
Breaking Into AI

How Deep Learning Helped an IT Manager Find New Career Satisfaction After Age 40

Olivier Moulin is an IT manager for a large, multi-national medical technology company who has been working in technology for over 20 years. Early in his career, he made a tough decision to take a high paying job instead of pursuing a Ph.D. He spoke with us about how the Deep Learning Specialization helped him build the confidence to go back

How a New Mother Learned AI During Her Newborn Baby’s Naps
Breaking Into AI

How a New Mother Learned AI During Her Newborn Baby’s Naps

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.

How a Mathematician Found Career Satisfaction With Deep Learning
Breaking Into AI

How a Mathematician Found Career Satisfaction With Deep Learning

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. 

How an Astrophysicist Decided that Deep Learning Was His True Calling
Breaking Into AI

How an Astrophysicist Decided that Deep Learning Was His True Calling

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.

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

How We Won the First Data-Centric AI Competition: Innotescus
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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.

How We Won the First Data-Centric AI Competition: KAIST – AIPRLab
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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.

How I Won the First Data-centric AI Competition: Johnson Kuan
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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.

How I Won the First Data-centric AI Competition: Mohammad Motamedi
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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.

How I Won the First Data-centric AI Competition: Divakar Roy
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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.

How I Won the First Data-centric AI Competition: Pierre-Louis Bescond
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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.

How We Won the First Data-centric AI Competition: GoDataDriven
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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.

Expanding Access to Education
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Expanding Access to Education

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. 

2021 Pie & AI Ambassador Spotlight: José Morey & Rose Delilah Gesicho
Ambassador Spotlight

2021 Pie & AI Ambassador Spotlight: José Morey & Rose Delilah Gesicho

Jose Morey & Rose Delilah Gesicho share their Event Ambassador experience for the 2021 Pie & AI Ambassador Spotlight week. Read their stories!

2021 Pie & AI Ambassador Spotlight: Utkarsh Raj & Tharindu Adhikan
Ambassador Spotlight

2021 Pie & AI Ambassador Spotlight: Utkarsh Raj & Tharindu Adhikan

Utkarsh Raj & Tharindu Adhikan share their Event Ambassador experience for the 2021 Pie & AI Ambassador Spotlight week. Read their stories!

2021 Pie & AI Ambassador Spotlight: Vishwas Narayan & Ping Wang
Ambassador Spotlight

2021 Pie & AI Ambassador Spotlight: Vishwas Narayan & Ping Wang

Vishwas Narayan & Ping Wang share their Event Ambassador experience for the 2021 Pie & AI Ambassador Spotlight week. Read their stories!

2021 Pie & AI Ambassador Spotlight: Kareem Negm & Tamara Koliada
Ambassador Spotlight

2021 Pie & AI Ambassador Spotlight: Kareem Negm & Tamara Koliada

Kareem Negm & Tamara Koliada share their Event Ambassador experience for the 2021 Pie & AI Ambassador Spotlight week. Read their stories!

2021 Pie & AI Ambassador Spotlight: Robert Daniel Maria & Rohit Singh Rathaur
Ambassador Spotlight

2021 Pie & AI Ambassador Spotlight: Robert Daniel Maria & Rohit Singh Rathaur

Robert Daniel Maria & Rohit Singh Rathaur share their Event Ambassador experiences for the 2021 Pie & AI Ambassador Spotlight week. Read their stories!