Tag: deep-learning

According to Warren Buffett, "you only know who is swimming naked when the tide goes out." The same is true for AI. The current AI tide is high, but what will happen when the tide goes out? In this post, I will share some of my thoughts on the future of AI.

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The title tells it all and transformers are here to stay. If you want to join the trend of shaping the future of NLP, this is the place to start.

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This is my first quarter review for 2023, which reviews my progress on my goals for the year and share some of my thoughts on the quarter.

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Not many models balance complexity and effectiveness as well as LDA. I like this model so much as it is perhaps the best model to start with when you want to learn about machine learning and deep learning models. Why? I will explain in this post.

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Training and tuning a deep learning model is a complex process. This post will cover the basics of how to initialize the parameters of a deep learning model.

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As key components of neural networks, activation functions are responsible for transforming the input data into the desired output. In this article, we will discuss the most common activation functions and their applications.

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A guide to building a full stack deep learning application with PyTorch in a small scale, from data collection to model saving without deploying to production.

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