5 AI/ML Papers That Instantly Make You Smarter
We’ve curated 5 groundbreaking AI/ML papers that have shaped modern machine learning and deep learning. If you want to level up your understanding of AI fundamentals, these papers are a must-read:
Attention Is All You Need (2017) – The paper that introduced Transformers, revolutionizing NLP and powering models like ChatGPT and Bard. This is why AI can now understand and generate human-like language.
Batch Normalization (2015) – Struggling with vanishing gradients? This paper introduced a technique to speed up training and stabilize deep networks, making large-scale models feasible.
Deep Residual Learning (2015) – Ever wondered how deep networks work so well? ResNets solved the vanishing gradient problem, allowing for the training of ultra-deep models.
Denoising Autoencoders (2008) – The foundation of self-supervised learning, enabling models to learn useful representations from unlabeled data—a core element in modern AI.
The Lottery Ticket Hypothesis (2019) – Turns out, your massive neural network has a smaller, more efficient “winning ticket” that can perform just as well with fewer parameters.
Pro Tip: Understanding these papers not only deepens your AI knowledge but also gives you a competitive edge in interviews and real-world applications.
You can find all research papers here:
https://arxiv.org/
“It takes time, persistence, and patience, but if you stay curious and keep showing up, you’ll get there!”
Tuned In - I’ll be sharing lots of Data Science projects I did back when I was in university. Also lots of Data Science, AI/ML related topics that you can use as an reference and use it before your interview.
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