We version our code, why not our data?
All machine learning solutions start with a good dataset. The author of “Deep Learning with Python” goes as far as stating Spending more effort and money on data collection almost always yields a much greater return
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The datasets used for training and benchmarking machine learning models are incredibly large and continue to grow rapidly. ImageNet, a
Generative Deep Learning Book - Chapter 5 - Autoregressive Models
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Generative Deep Learning Book - Chapter 4 - Generative Adversarial Networks (GANs)
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Generative Deep Learning Book - Chapter 3 - Variational Auto Encoders
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Generative Deep Learning Book - Chapters 1 & 2 - Intro
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Generative Deep Learning Book - Preface
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Command Line Tool to Inspect Parquet, CSV, and other DataFrames 🐂 🌾
When it comes to data, most data scientists know how to use pandas for exploratory data analysis. Spin up a
Blazing Fast Data Version Control with Oxen 🐂 🔥
As data scientists or machine learning engineers data is the key to what makes our products succeed. We are constantly running experiments, and the world that is constantly changing, this means we should be versioning
