Active Learning: Why Smart Labeling is the Future of Data Annotation | Alectio
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- Опубликовано: 8 окт 2024
- Get the slides: www.datacounci...
ABOUT THE TALK:
Today, with always more data at their fingertips, Machine Learning experts seem to have no shortage of opportunities to create always better models. Over and over again, research has proven that both the volume and quality of the training data is what differentiates good models from the highest performing ones. But with an ever-increasing volume of data, and with the constant rise of data-greedy algorithms such as Deep Neural Networks, it is becoming challenging for data scientists to get the volume of labels they need at the speed they need, regardless of their budgetary and time constraints.
To address this “Big Data labeling crisis”, most data labeling companies offer solutions based on semi-automation, where a machine learning algorithm predicts labels before this labeled data is sent to an annotator so that he/she can review the results and validate their accuracy. There is a radically different approach to this problem which focuses on labeling “smarter” rather than labeling faster.
Instead of labeling all of the data, it is usually possible to reach the same model accuracy by labeling just a fraction of the data, as long as the most informational rows are labeled. Active Learning allows data scientists to train their models and to build and label training sets simultaneously in order to guarantee the best results with the minimum number of labels.
ABOUT THE SPEAKER:
Jennifer Prendki is currently the VP of Machine Learning at Figure Eight, the essential human-in-the-loop AI platform for data science and machine learning teams. She has spent most of her career creating a data-driven culture wherever she went, 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 non-technical audiences at conferences and seminars, and is passionate about attracting more women to careers in STEM.
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Well-Explained lecture. Must watch for beginners.
can you tell me what is the name of this course,
and what is name of job.
I know it's pretty off topic but do anyone know of a good place to watch newly released tv shows online ?
Thank you
Brilliant presentation. Thank you.
will data annotation carries scope in future?
Is there any scope in data annotation
can you tell me what is the name of this course,
and what is name of job.
brilliant lady
In 17:06 she did a comparison between the old way and active learning what did she meant about the old way??
I think she meant approaches other than active learning that require a whole labeled dataset beforehand. Like basic ML approaches
She is awesome ☺️
Good Info . Speaker is just great.
can you tell me what is the name of this course,
and what is name of job.