Okay, let's talk about the one thing which doesn't get that much attention in the data science realm: labeling your data. It's a painful process, and that may lead to its disregard in tutorials you found on the internet or bootcamps you joined. However, it's one of the most crucial components in the data pipeline, you know, garbage in garbage out. A bad label leads to a bad model and a bad production practice. A data-centric approach to machine learning recently has sparked this idea into a whole new research playground.
I recently started a new newsletter focus on AI education and already has over 50,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Deep learning is becoming increasingly important across different core scientific disciplines such as biology or physics. Obviously, mathematics is the foundation behind every deep learning method but could these be used to advance math research itself?
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Reinforcement learning (RL) has shown promise in creating complex logic in controlled settings. On the other hand, what are the prospects for using RL in a more complicated context like telecom networks? Let's learn the basics first. What is reinforcement learning, and how does it work? In machine learning, the three methodologies are reinforcement learning (RL), supervised learning, and unsupervised learning.
We are in the age of data. In recent years, many companies have already started collecting large amounts of data about their business. On the other hand, many companies are just starting now. If you are working in one of these companies, you might be wondering what can be done with all that data. What about using the data to train a supervised machine learning (ML) algorithm? The ML algorithm could perform the same classification task a human would, just so much faster!