- "Python vs. R: The Good, the Bad, and the Ugly"
The two most popular languages for Machine Learning and Data Science are Python and R. As with any computer language, you can't have it all: there are advantages and disadvantages, compromises, and sometimes a bit of ugliness. Clem will do an overview of the two languages so you can get a better feel for which one might be better for your use case, pitfalls and practicalities. Agenda: 6:00 pm - 6:20 pm Arrival and socializing 6:20 pm - 6:30 pm Opening words by Murat Baday, CEO of Magnimind Academy, the Meetup sponsors. 6:30 pm - 7:20 pm Clem Wang, "Python vs. R: The Good, the Bad, and the Ugly" 7:20 pm - 7:30 pm Q&A About Clem Wang: Clem Wang has been a Data Scientist for 15 years, working at both large companies like Yahoo and Microsoft, and a bunch of startups. He's used both Python and R professionally. In a previous life, he's been involved with QA'ing compilers and interpreters, so he has some insights in the inner workings of R and Python.
- An Introduction to Active Learning
The greatest challenge when building a high-performance model isn't about choosing the right algorithm or doing hyperparameter tuning: it is about getting high quality labeled training data. Without good data, no algorithm, even the most sophisticated one, will deliver the results needed for real-life applications. And with most modern algorithms (such as Deep Learning models) requiring huge amounts of data to train, things aren't going to get better any time soon. Active Learning is one of the possible solutions to this dilemma, but quite surprisingly, left out of most data science conferences and computer science curricula. By the end of this meetup, you will learn the importance of Active Learning and its application to make AI work in the real world. Agenda: 6:00 pm - 6:20 pm Arrival and socializing 6:20 pm - 6:30 pm Opening words by Murat Baday, CEO of Magnimind Academy, the Meetup sponsors. 6:30 pm - 7:50 pm Jennifer Prendki, "An Introduction to Active Learning" 7:50 pm - 8:00 pm Q&A About Jennifer Prendki: Jennifer Prendki is the founder and CEO of Alectio. The company is the direct product of her beliefs that good models can only be built with good data, and that the brute force approach that consists in blindly using ever larger training sets is the reason why the barrier to entry into AI is so high. Prior to starting Alectio, Jennifer was the VP of Machine Learning at Figure Eight, the company that pioneered data labeling. She has been Chief Data Scientist at Atlassian and Senior Manager of Data Science in the Search team at Walmart Labs. Jennifer has spent most of her career creating data-driven cultures, succeeding in sometimes highly skeptical environments. She is particularly skilled at building and scaling high-performance machine learning teams and is known for enjoying a good challenge. Trained as a particle physicist (she holds a PhD in particle physics from Sorbonne University), she likes to use her analytical mind not only when building complex models but also as part of her leadership philosophy. She is pragmatic yet detail-oriented. Jennifer also takes great pleasure in addressing both technical and nontechnical audiences alike at conferences and seminars and is passionate about attracting more women to careers in STEM.