xgboost VS Keras

Compare xgboost vs Keras and see what are their differences.

xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow (by dmlc)
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xgboost Keras
11 80
25,827 61,338
0.6% 0.4%
9.7 9.9
5 days ago 5 days ago
C++ Python
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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xgboost

Posts with mentions or reviews of xgboost. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-05-05.

Keras

Posts with mentions or reviews of Keras. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-06-13.
  • Using Google Magika to build an AI-powered file type detector
    4 projects | dev.to | 13 Jun 2024
    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.
  • Side Quest #3: maybe the real Deepfakes were the friends we made along the way
    3 projects | dev.to | 20 May 2024
    def batcher_from_directory(batch_size:int, dataset_path:str, shuffle=False,seed=None) -> tf.data.Dataset: """ Return a tensorflow Dataset object that returns images and spectrograms as required. Partly inspired by https://github.com/keras-team/keras/blob/v3.3.3/keras/src/utils/image_dataset_utils.py Args: batch_size: The batch size. dataset_path: The path to the dataset folder which must contain the image folder and audio folder. shuffle: Whether to shuffle the dataset. Default to False. seed: The seed for the shuffle. Default to None. """ image_dataset_path = os.path.join(dataset_path, "image") # create the foundation datasets og_dataset = tf.data.Dataset.from_generator(lambda: original_image_path_gen(image_dataset_path), output_signature=tf.TensorSpec(shape=(), dtype=tf.string)) og_dataset = og_dataset.repeat(None) # repeat indefinitely ref_dataset = tf.data.Dataset.from_generator(lambda: ref_image_path_gen(image_dataset_path), output_signature=(tf.TensorSpec(shape=(), dtype=tf.string), tf.TensorSpec(shape=(), dtype=tf.bool))) ref_dataset = ref_dataset.repeat(None) # repeat indefinitely # create the input datasets og_image_dataset = og_dataset.map(lambda x: tf.py_function(load_image, [x, tf.convert_to_tensor(False, dtype=tf.bool)], tf.float32), num_parallel_calls=tf.data.AUTOTUNE) masked_image_dataset = og_image_dataset.map(lambda x: tf.py_function(load_masked_image, [x], tf.float32), num_parallel_calls=tf.data.AUTOTUNE) ref_image_dataset = ref_dataset.map(lambda x, y: tf.py_function(load_image, [x, y], tf.float32), num_parallel_calls=tf.data.AUTOTUNE) audio_spec_dataset = og_dataset.map(lambda x: tf.py_function(load_audio_data, [x, dataset_path], tf.float64), num_parallel_calls=tf.data.AUTOTUNE) unsync_spec_dataset = ref_dataset.map(lambda x, _: tf.py_function(load_audio_data, [x, dataset_path], tf.float64), num_parallel_calls=tf.data.AUTOTUNE) # ensure shape as tensorflow does not accept unknown shapes og_image_dataset = og_image_dataset.map(lambda x: tf.ensure_shape(x, IMAGE_SHAPE)) masked_image_dataset = masked_image_dataset.map(lambda x: tf.ensure_shape(x, MASKED_IMAGE_SHAPE)) ref_image_dataset = ref_image_dataset.map(lambda x: tf.ensure_shape(x, IMAGE_SHAPE)) audio_spec_dataset = audio_spec_dataset.map(lambda x: tf.ensure_shape(x, AUDIO_SPECTROGRAM_SHAPE)) unsync_spec_dataset = unsync_spec_dataset.map(lambda x: tf.ensure_shape(x, AUDIO_SPECTROGRAM_SHAPE)) # multi input using https://discuss.tensorflow.org/t/train-a-model-on-multiple-input-dataset/17829/4 full_dataset = tf.data.Dataset.zip((masked_image_dataset, ref_image_dataset, audio_spec_dataset, unsync_spec_dataset), og_image_dataset) # if shuffle: # full_dataset = full_dataset.shuffle(buffer_size=batch_size * 8, seed=seed) # not sure why buffer size is such # batch full_dataset = full_dataset.batch(batch_size=batch_size) return full_dataset
  • Library for Machine learning and quantum computing
    4 projects | dev.to | 27 Apr 2024
    Keras
  • My Favorite DevTools to Build AI/ML Applications!
    9 projects | dev.to | 23 Apr 2024
    As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.
  • Release: Keras 3.3.0
    1 project | news.ycombinator.com | 22 Apr 2024
  • Getting Started with Gemma Models
    4 projects | dev.to | 15 Apr 2024
    After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow.
  • Keras 3.0
    4 projects | news.ycombinator.com | 28 Nov 2023
    All breaking changes are listed here: https://github.com/keras-team/keras/issues/18467

    You can use this migration guide to identify and fix each of these issues (and further, making your code run on JAX or PyTorch): https://keras.io/guides/migrating_to_keras_3/

  • Keras 3: A new multi-back end Keras
    1 project | news.ycombinator.com | 27 Nov 2023
  • Can someone explain how keras code gets into the Tensorflow package?
    2 projects | /r/tensorflow | 24 Jul 2023
    I'm guessing the "real" keras code is coming from the keras repository. Is that a correct assumption? How does that version of Keras get there? If I wanted to write my own activation layer next to ELU, where exactly would I do that?
  • How popular are libraries in each technology
    21 projects | dev.to | 21 Jun 2023
    Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks.

What are some alternatives?

When comparing xgboost and Keras you can also consider the following projects:

Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

MLP Classifier - A handwritten multilayer perceptron classifer using numpy.

scikit-learn - scikit-learn: machine learning in Python

tensorflow - An Open Source Machine Learning Framework for Everyone

Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

mlpack - mlpack: a fast, header-only C++ machine learning library

catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.

pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]

InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
www.influxdata.com
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the 6th most popular programming language
based on number of metions?