CommunityCollection of 89 Posts
2022 Pie & AI Ambassador Spotlight: Jean de Dieu Nyandwi
Why did you join the DeepLearning.AI community as an event ambassador? Before becoming an ambassador I took several of DeepLearning.AI’s specializations. I believed that becoming an ambassador would help me to meet more people in…
2022 Pie & AI Ambassador Spotlight: Deepak Sai Pendyala
Why did you join the DeepLearning.AI community as an event ambassador? Pie & AI reaches underserved communities and highlights the importance of data science, artificial intelligence, and machine learning. As an undergrad, I’m always enthusiastic…
Andrew Ng on How His Updated Machine Learning Specialization Can Help You Break Into AI
In the mid-2000s, AI was still just a curiosity to the world at large. At Stanford University, however, one of the most popular classes on campus was Andrew Ng’s CS229 machine learning course. Enrollment was frequently too large to fit in the classroom, yet he wanted even more people to be able to master machine learning.
So, working with a few students, he created an online Machine Learning course that could be taken by anyone with an internet connection and a desire to learn. The rest is history. Coursera launched in 2012 with Machine Learning as its flagship title. It was also the platform’s most popular, with almost 5 million enrollments.
This year, to celebrate the course’s 10 year anniversary, DeepLearning.AI and Stanford Online released a successor — the Machine Learning Specialization. Andrew spoke with us about how the new Specialization improves on the original, who should take it, and how it fits into the modern AI builder’s career arc.
Working AI: How an Accomplished Data Scientist Found Job Satisfaction
Kulsoom Abdullah pivoted from network security into a data science role after taking Andrew Ng’s original Introduction to Machine Learning course from Stanford in 2013. A self-described learning addict, she has taken more than 10 online courses, most recently the Deep Learning Specialization. Outside of work, she’s a fitness fanatic, and was the first competitive weightlifter to compete professionally for Pakistan — after winning a fight to change the dress code to allow religious head coverings. On top of all that, she’s a travel
Breaking Into AI: How a Machine Learning Engineer Turns Ideas Into Products
Lorenzo Ostano first encountered machine learning and data science as a business analyst shortly after graduating from college. He dove into learning, and soon landed work as a machine learning engineer. After working several years for a variety of consulting companies, he recently took a job as a software engineer. He spoke to us about why he believes traditional computing skills are important to deploying enterprise machine learning applications.
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.
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
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
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
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
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
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
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.