Keras VS xgboost

The unchallenged leader in AI development is still Python. and Keras, and robust community support.

>Chollet, a French computer scientist and one of the industry’s sharpest skeptics

I feel like this description really buries the lede on Chollet's expertise. (For those who don't know, he's the creator of and lead contributor[0] to Keras)

[0]https://github.com/keras-team/keras/graphs/contributors

Keras API reference

Star on GitHub ⭐ - Keras

At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck.

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The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models.

Keras

Boosting Boosting is not a separate ML model but a technique that combines multiple weak learners to create a single model that can generate highly accurate predictions. Xgboost is a common boosting model that supports distributed training, resulting in faster training. According to research by Intel, Xgboost can be more effective than a neural network-based approach for tabular data. In addition, Xgboost is faster to train and doesn’t require as much data as neural networks need.

Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive documentation and community support, making it easy to learn and apply new techniques without needing specialized training or expensive software licenses.

You can find the problem outlined here: https://github.com/dmlc/xgboost/issues/5826. u/hcho3 diagnosed the problem and corrected it as of XGB version 1.2.0.

Reference: https://github.com/dmlc/xgboost/issues/2031

By the way, most of the time XGBoost works just as well for projects, would not recommend applying deep learning to every single problem you come across, it's something Stanford CS really likes to showcase when it's well known (1) that sometimes "smaller"/less complex models can perform just as well or have their own interpretive advantages and (2) it is well known within ML and DS communities that deep learning does not perform as well with tabular datasets and using deep learning as a default to every problem is just poor practice. However, if you do (god forbid) get language, speech/audio, vision/imaging, or even time series models then deep learning as a baseline is not the worst idea.