larq VS data-science-ipython-notebooks

Compare larq vs data-science-ipython-notebooks and see what are their differences.

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. (by donnemartin)
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larq data-science-ipython-notebooks
2 1
692 26,532
0.3% -
7.5 0.0
17 days ago about 2 months ago
Python Python
Apache License 2.0 GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

larq

Posts with mentions or reviews of larq. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-09.
  • Running CNN on ATmega328P
    1 project | /r/embedded | 1 Apr 2022
    You quantize the model parameters i.e., don't just send the model in which uses floating point math instead change it to fixed point. This has 2 advantages 1) a pure size reduction and 2) most low power MCU's don't have float point multipliers but do have single cycle fixed point multipliers. This is a classic DSP trick used for a long time. The real research aspects come-in as you start dropping below 8-bit; even coming down to single-bit in some cases(see Larq)
  • Simplifying AI to FPGA deployment, looking for opportunities
    3 projects | /r/FPGA | 9 Mar 2022
    It is a difficult question. I work almost exclusively with open source, so I'm not much use to give you advice. Maybe you can see how Plumerai handles things -- they have some stuff proprietary, but they've also open-sourced their BNN Larq stuff: https://github.com/larq/larq

data-science-ipython-notebooks

Posts with mentions or reviews of data-science-ipython-notebooks. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2020-12-27.

What are some alternatives?

When comparing larq and data-science-ipython-notebooks you can also consider the following projects:

finn-examples - Dataflow QNN inference accelerator examples on FPGAs

manjaro-linux - Shell scripts for setting up Manjaro Linux for Python programming and deep learning

model-optimization - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

BirdNET - Soundscape analysis with BirdNET.

nngen - NNgen: A Fully-Customizable Hardware Synthesis Compiler for Deep Neural Network

fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.

kmodes - Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data

sports-betting - Collection of sports betting AI tools.

PMapper - A tool for quickly evaluating IAM permissions in AWS.

data-science - :bar_chart: Path to a free self-taught education in Data Science!

listenbrainz-server - Server for the ListenBrainz project, including the front-end (javascript/react) code that it serves and all of the data processing components that LB uses.

pandas_flavor - The easy way to write your own flavor of Pandas