Update README.md

Author: krzjoa

README.md

@@ -142,9 +142,6 @@
 ### PyTorch
 * [PyTorch](https://github.com/pytorch/pytorch) - Tensors and Dynamic neural networks in Python with strong GPU acceleration. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [pytorch-lightning](https://github.com/Lightning-AI/lightning) - PyTorch Lightning is just organized PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
-* [torchvision](https://github.com/pytorch/vision) - Datasets, Transforms, and Models specific to Computer Vision. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
-* [torchtext](https://github.com/pytorch/text) - Data loaders and abstractions for text and NLP. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
-* [torchaudio](https://github.com/pytorch/audio) - An audio library for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [ignite](https://github.com/pytorch/ignite) - High-level library to help with training neural networks in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [skorch](https://github.com/dnouri/skorch) - A scikit-learn compatible neural network library that wraps PyTorch. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [Catalyst](https://github.com/catalyst-team/catalyst) - High-level utils for PyTorch DL & RL research. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
@@ -172,19 +169,19 @@
 ### MXNet
 * [MXNet](https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
 * [Gluon](https://github.com/gluon-api/gluon-api) - A clear, concise, simple yet powerful and efficient API for deep learning (now included in MXNet). <img height="20" src="img/mxnet_big.png" alt="MXNet based">
-* [gluon-cv](https://github.com/dmlc/gluon-cv) - Provides implementations of the state-of-the-art deep learning models in computer vision. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
-* [gluon-nlp](https://github.com/dmlc/gluon-nlp) - NLP made easy. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
 * [Xfer](https://github.com/amzn/xfer) - Transfer Learning library for Deep Neural Networks. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
 * [MXNet](https://github.com/ROCmSoftwarePlatform/mxnet) - HIP Port of MXNet. <img height="20" src="img/mxnet_big.png" alt="MXNet based"> <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">
 
-### Others
+### JAX
 * [jax](https://github.com/google/jax) - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.
+
+### Others
+* [transformers](https://github.com/huggingface/transformers) - State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
 * [Tangent](https://github.com/google/tangent) - Source-to-Source Debuggable Derivatives in Pure Python.
 * [autograd](https://github.com/HIPS/autograd) - Efficiently computes derivatives of numpy code.
 * [Myia](https://github.com/mila-udem/myia) - Deep Learning framework (pre-alpha).
 * [nnabla](https://github.com/sony/nnabla) - Neural Network Libraries by Sony.
 * [Caffe](https://github.com/BVLC/caffe) - A fast open framework for deep learning.
-* [hipCaffe](https://github.com/ROCmSoftwarePlatform/hipCaffe) - The HIP port of Caffe. <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">
 
 ## Reinforcement Learning
 * [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly [Gym](https://github.com/openai/gym)).
@@ -231,50 +228,6 @@
 * [GPyTorch](https://github.com/cornellius-gp/gpytorch) - A highly efficient and modular implementation of Gaussian Processes in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [sklearn-crfsuite](https://github.com/TeamHG-Memex/sklearn-crfsuite) - A scikit-learn-inspired API for CRFsuite. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 
-## Data Manipulation
-
-### Data Frames
-* [pandas](https://pandas.pydata.org/pandas-docs/stable/) - Powerful Python data analysis toolkit.
-* [polars](https://github.com/pola-rs/polars) - A fast multi-threaded, hybrid-out-of-core DataFrame library.
-* [koalas](https://github.com/databricks/koalas) - pandas API on Apache Spark. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
-* [Arctic](https://github.com/manahl/arctic) - High-performance datastore for time series and tick data.
-* [datatable](https://github.com/h2oai/datatable) - Data.table for Python. <img height="20" src="img/R_big.png" alt="R inspired/ported lib">
-* [pandas_profiling](https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects
-* [cuDF](https://github.com/rapidsai/cudf) - GPU DataFrame Library. <img height="20" src="img/pandas_big.png" alt="pandas compatible"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
-* [blaze](https://github.com/blaze/blaze) - NumPy and pandas interface to Big Data. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
-* [pandasql](https://github.com/yhat/pandasql) - Allows you to query pandas DataFrames using SQL syntax. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
-* [pandas-gbq](https://github.com/pydata/pandas-gbq) - pandas Google Big Query. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
-* [xpandas](https://github.com/alan-turing-institute/xpandas) - Universal 1d/2d data containers with Transformers .functionality for data analysis by [The Alan Turing Institute](https://www.turing.ac.uk/).
-* [pysparkling](https://github.com/svenkreiss/pysparkling) - A pure Python implementation of Apache Spark's RDD and DStream interfaces. <img height="20" src="img/spark_big.png" alt="Apache Spark based">
-* [modin](https://github.com/modin-project/modin) - Speed up your pandas workflows by changing a single line of code. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
-* [swifter](https://github.com/jmcarpenter2/swifter) - A package that efficiently applies any function to a pandas dataframe or series in the fastest available manner.
-* [pandas-log](https://github.com/eyaltrabelsi/pandas-log) - A package that allows providing feedback about basic pandas operations and finds both business logic and performance issues.
-* [vaex](https://github.com/vaexio/vaex) - Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second.
-* [xarray](https://github.com/pydata/xarray) - Xarray combines the best features of NumPy and pandas for multidimensional data selection by supplementing numerical axis labels with named dimensions for more intuitive, concise, and less error-prone indexing routines.
-
-
-### Pipelines
-* [pdpipe](https://github.com/shaypal5/pdpipe) - Sasy pipelines for pandas DataFrames.
-* [SSPipe](https://sspipe.github.io/) - Python pipe (|) operator with support for DataFrames and Numpy, and Pytorch.
-* [pandas-ply](https://github.com/coursera/pandas-ply) - Functional data manipulation for pandas. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
-* [Dplython](https://github.com/dodger487/dplython) - Dplyr for Python. <img height="20" src="img/R_big.png" alt="R inspired/ported lib">
-* [sklearn-pandas](https://github.com/scikit-learn-contrib/sklearn-pandas) - pandas integration with sklearn. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/pandas_big.png" alt="pandas compatible">
-* [Dataset](https://github.com/analysiscenter/dataset) - Helps you conveniently work with random or sequential batches of your data and define data processing.
-* [pyjanitor](https://github.com/ericmjl/pyjanitor) - Clean APIs for data cleaning. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
-* [meza](https://github.com/reubano/meza) - A Python toolkit for processing tabular data.
-* [Prodmodel](https://github.com/prodmodel/prodmodel) - Build system for data science pipelines.
-* [dopanda](https://github.com/dovpanda-dev/dovpanda) - Hints and tips for using pandas in an analysis environment. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
-* [Hamilton](https://github.com/DAGWorks-Inc/hamilton) - A microframework for dataframe generation that applies Directed Acyclic Graphs specified by a flow of lazily evaluated Python functions.
-
-### Data-centric AI
-* [cleanlab](https://github.com/cleanlab/cleanlab) - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
-* [snorkel](https://github.com/snorkel-team/snorkel) - A system for quickly generating training data with weak supervision.
-* [dataprep](https://github.com/sfu-db/dataprep) - Collect, clean, and visualize your data in Python with a few lines of code.
-
-### Synthetic Data
-
-* [ydata-synthetic](https://github.com/ydataai/ydata-synthetic) - A package to generate synthetic tabular and time-series data leveraging the state-of-the-art generative models. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
-
 ## Feature Engineering
 
 ### General
@@ -415,6 +368,9 @@
 * [Chaos Genius](https://github.com/chaos-genius/chaos_genius) - ML powered analytics engine for outlier/anomaly detection and root cause analysis
 
 ## Natural Language Processing
+* [torchtext](https://github.com/pytorch/text) - Data loaders and abstractions for text and NLP. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
+* [gluon-nlp](https://github.com/dmlc/gluon-nlp) - NLP made easy. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
+* [KerasNLP](https://github.com/keras-team/keras-nlp) - Modular Natural Language Processing workflows with Keras. <img height="20" src="img/keras_big.png" alt="Keras based/compatible">
 * [spaCy](https://spacy.io/) - Industrial-Strength Natural Language Processing.
 * [NLTK](https://github.com/nltk/nltk) - Modules, data sets, and tutorials supporting research and development in Natural Language Processing.
 * [CLTK](https://github.com/cltk/cltk) - The Classical Language Toolkik.
@@ -426,6 +382,7 @@
 
 
 ## Computer Audition
+* [torchaudio](https://github.com/pytorch/audio) - An audio library for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [librosa](https://github.com/librosa/librosa) - Python library for audio and music analysis.
 * [Yaafe](https://github.com/Yaafe/Yaafe) - Audio features extraction.
 * [aubio](https://github.com/aubio/aubio) - A library for audio and music analysis.
@@ -436,6 +393,9 @@
 * [madmom](https://github.com/CPJKU/madmom) - Python audio and music signal processing library.
 
 ## Computer Vision
+* [torchvision](https://github.com/pytorch/vision) - Datasets, Transforms, and Models specific to Computer Vision. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
+* [gluon-cv](https://github.com/dmlc/gluon-cv) - Provides implementations of the state-of-the-art deep learning models in computer vision. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
+* [KerasCV](https://github.com/keras-team/keras-cv) - Industry-strength Computer Vision workflows with Keras. <img height="20" src="img/keras_big.png" alt="MXNet based">
 * [OpenCV](https://github.com/opencv/opencv) - Open Source Computer Vision Library.
 * [scikit-image](https://github.com/scikit-image/scikit-image) - Image Processing SciKit (Toolbox for SciPy).
 * [imgaug](https://github.com/aleju/imgaug) - Image augmentation for machine learning experiments.
@@ -452,6 +412,50 @@
 * [scikit-posthocs](https://github.com/maximtrp/scikit-posthocs) - Pairwise Multiple Comparisons Post-hoc Tests.
 * [Alphalens](https://github.com/quantopian/alphalens) - Performance analysis of predictive (alpha) stock factors.
 
+
+## Data Manipulation
+
+### Data Frames
+* [pandas](https://pandas.pydata.org/pandas-docs/stable/) - Powerful Python data analysis toolkit.
+* [polars](https://github.com/pola-rs/polars) - A fast multi-threaded, hybrid-out-of-core DataFrame library.
+* [koalas](https://github.com/databricks/koalas) - pandas API on Apache Spark. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+* [Arctic](https://github.com/manahl/arctic) - High-performance datastore for time series and tick data.
+* [datatable](https://github.com/h2oai/datatable) - Data.table for Python. <img height="20" src="img/R_big.png" alt="R inspired/ported lib">
+* [pandas_profiling](https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects
+* [cuDF](https://github.com/rapidsai/cudf) - GPU DataFrame Library. <img height="20" src="img/pandas_big.png" alt="pandas compatible"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
+* [blaze](https://github.com/blaze/blaze) - NumPy and pandas interface to Big Data. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+* [pandasql](https://github.com/yhat/pandasql) - Allows you to query pandas DataFrames using SQL syntax. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+* [pandas-gbq](https://github.com/pydata/pandas-gbq) - pandas Google Big Query. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+* [xpandas](https://github.com/alan-turing-institute/xpandas) - Universal 1d/2d data containers with Transformers .functionality for data analysis by [The Alan Turing Institute](https://www.turing.ac.uk/).
+* [pysparkling](https://github.com/svenkreiss/pysparkling) - A pure Python implementation of Apache Spark's RDD and DStream interfaces. <img height="20" src="img/spark_big.png" alt="Apache Spark based">
+* [modin](https://github.com/modin-project/modin) - Speed up your pandas workflows by changing a single line of code. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+* [swifter](https://github.com/jmcarpenter2/swifter) - A package that efficiently applies any function to a pandas dataframe or series in the fastest available manner.
+* [pandas-log](https://github.com/eyaltrabelsi/pandas-log) - A package that allows providing feedback about basic pandas operations and finds both business logic and performance issues.
+* [vaex](https://github.com/vaexio/vaex) - Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second.
+* [xarray](https://github.com/pydata/xarray) - Xarray combines the best features of NumPy and pandas for multidimensional data selection by supplementing numerical axis labels with named dimensions for more intuitive, concise, and less error-prone indexing routines.
+
+### Pipelines
+* [pdpipe](https://github.com/shaypal5/pdpipe) - Sasy pipelines for pandas DataFrames.
+* [SSPipe](https://sspipe.github.io/) - Python pipe (|) operator with support for DataFrames and Numpy, and Pytorch.
+* [pandas-ply](https://github.com/coursera/pandas-ply) - Functional data manipulation for pandas. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+* [Dplython](https://github.com/dodger487/dplython) - Dplyr for Python. <img height="20" src="img/R_big.png" alt="R inspired/ported lib">
+* [sklearn-pandas](https://github.com/scikit-learn-contrib/sklearn-pandas) - pandas integration with sklearn. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+* [Dataset](https://github.com/analysiscenter/dataset) - Helps you conveniently work with random or sequential batches of your data and define data processing.
+* [pyjanitor](https://github.com/ericmjl/pyjanitor) - Clean APIs for data cleaning. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+* [meza](https://github.com/reubano/meza) - A Python toolkit for processing tabular data.
+* [Prodmodel](https://github.com/prodmodel/prodmodel) - Build system for data science pipelines.
+* [dopanda](https://github.com/dovpanda-dev/dovpanda) - Hints and tips for using pandas in an analysis environment. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+* [Hamilton](https://github.com/DAGWorks-Inc/hamilton) - A microframework for dataframe generation that applies Directed Acyclic Graphs specified by a flow of lazily evaluated Python functions.
+
+### Data-centric AI
+* [cleanlab](https://github.com/cleanlab/cleanlab) - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
+* [snorkel](https://github.com/snorkel-team/snorkel) - A system for quickly generating training data with weak supervision.
+* [dataprep](https://github.com/sfu-db/dataprep) - Collect, clean, and visualize your data in Python with a few lines of code.
+
+### Synthetic Data
+
+* [ydata-synthetic](https://github.com/ydataai/ydata-synthetic) - A package to generate synthetic tabular and time-series data leveraging the state-of-the-art generative models. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+
 ## Distributed Computing
 * [Horovod](https://github.com/uber/horovod) - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [PySpark](https://spark.apache.org/docs/0.9.0/python-programming-guide.html) - Exposes the Spark programming model to Python. <img height="20" src="img/spark_big.png" alt="Apache Spark based">