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Compute Goes Brrr: Revisiting Sutton’s Bitter Lesson for AI
"It's just about having more compute." Wait, is that really all there is to AI? As Richard Sutton's 'bitter lesson' sinks in for more AI researchers, a debate has stirred that considers a potentially more subtle relationship between advancements in AI based on ever-more-clever algorithms and massively scaled computational power.
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 Building Neural Networks with PyTorch in Google Colab
Combining PyTorch and Google's cloud-based Colab notebook environment can be a good solution for building neural networks with free access to GPUs. This article demonstrates how to do just that.
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Autograd: The Best Machine Learning Library You’re Not Using?
If there is a Python library that is emblematic of the simplicity, flexibility, and utility of differentiable programming it has to be Autograd.
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A Deep Dive Into the Transformer Architecture – The Development of Transformer Models
Even though transformers for NLP were introduced only a few years ago, they have delivered major impacts to a variety of fields from reinforcement learning to chemistry. Now is the time to better understand the inner workings of transformer architectures to give you the intuition you need to effectively work with these powerful tools.
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Exploring GPT-3: A New Breakthrough in Language Generation
GPT-3 is the largest natural language processing (NLP) transformer released to date, eclipsing the previous record, Microsoft Research’s Turing-NLG at 17B parameters, by about 10 times. This has resulted in an explosion of demos: some good, some bad, all interesting.
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Deep Learning for Signal Processing: What You Need to Know
Signal Processing is a branch of electrical engineering that models and analyzes data representations of physical events. It is at the core of the digital world. And now, signal processing is starting to make some waves in deep learning.
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Recurrent Neural Networks (RNN): Deep Learning for Sequential Data
Recurrent Neural Networks can be used for a number of ways such as detecting the next word/letter, forecasting financial asset prices in a temporal space, action modeling in sports, music composition, image generation, and more.
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Deep Learning in Finance: Is This The Future of the Financial Industry?
Get a handle on how deep learning is affecting the finance industry, and identify resources to further this understanding and increase your knowledge of the various aspects.
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The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)
Natural language processing has made incredible advances through advanced techniques in deep learning. Learn about these powerful models, and find how close (or far away) these approaches are to human-level understanding.
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 The Most Important Fundamentals of PyTorch you Should Know
PyTorch is a constantly developing deep learning framework with many exciting additions and features. We review its basic elements and show an example of building a simple Deep Neural Network (DNN) step-by-step.
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Building Neural Networks with PyTorch in Google Colab
Autograd: The Best Machine Learning Library You’re Not Using?
The Most Important Fundamentals of PyTorch you Should Know