I don’t have a perfect method but in this post I would like to share how I keep up with the relevant papers in deep learning and the subfields I’m interested in: NLP, specifically sarcasm detection and some computer vision.
Deep learning is moving so fast, that the only way to keep up is by reading directly from the people who publish these new findings. If you’re a technical person and want to learn about deep learning in 2021, you need to read papers. Formal education will only get you so far. Unfortunately, universities in general are slow to incorporate new material into their curriculums and only a few years ago did they start to teach deep learning. This is the case in Europe, I acknowledge it might be different in the US.
This "Deep Learning vs Machine Learning vs AI vs Data Science" video talks about the differences and relationship between Artificial Intelligence, Machine Learning, Deep Learning, and Data Science.
What is the difference between machine learning and artificial intelligence and deep learning? Supervised learning is best for classification and regressions Machine Learning models. You can read more about them in this article.
Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Simple explanations of Artificial Intelligence, Machine Learning, and Deep Learning and how they’re all different