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!

My Journey into AI – learning resources recommended by the speakers
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My Journey into AI – learning resources recommended by the speakers

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:

Working AI: Stoking GPU Clusters With Swetha Mandava
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Working AI: Stoking GPU Clusters With Swetha Mandava

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…

Heroes of NLP
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Heroes of NLP

Heroes of NLP is a video interview series featuring Andrew Ng, the founder of DeepLearning.AI, in conversation with thought leaders in NLP.   Watch Andrew lead an enlightening discourse around how these industry and academic…

Breaking Into AI: Juggling Work, Projects, and Personal Life With Kennedy Wangari
Breaking Into AI

Breaking Into AI: Juggling Work, Projects, and Personal Life With Kennedy Wangari

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.