Logos converted

Author: Krzysztof Joachimiak

README.md

@@ -16,14 +16,6 @@
 
 > Probably the best curated list of data science software in Python
 
-[cp]: https://raw.githubusercontent.com/krzjoa/awesome-python-datascience/master/img/cupy.png 'CuPy based'
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-[mx]: https://raw.githubusercontent.com/krzjoa/awesome-python-datascience/master/img/mxnet.png 'MXNet based'
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-[r]: https://raw.githubusercontent.com/krzjoa/awesome-python-datascience/master/img/R.png 'R inspired/ported lib'
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-[gpu]: https://raw.githubusercontent.com/krzjoa/awesome-python-datascience/master/img/gpu.png 'GPU accelerated'
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 ## Contents
 * [Machine Learning](#ml)
 * [Deep Learning](#dl)
@@ -55,7 +47,7 @@
 * [scikit-learn](http://scikit-learn.org/stable/) - Machine learning in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [Shogun](http://www.shogun-toolbox.org/) - Machine learning toolbox.
 * [xLearn](https://github.com/aksnzhy/xlearn) - High Performance, Easy-to-use, and Scalable Machine Learning Package.
-* [cuML](https://github.com/rapidsai/cuml) - RAPIDS Machine Learning Library. <img height="20" src="img/sklearn_big.png" alt="sklearn"> ![alt text][gpu]
+* [cuML](https://github.com/rapidsai/cuml) - RAPIDS Machine Learning Library. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
 * [modAL](https://github.com/cosmic-cortex/modAL) - Modular active learning framework for Python3. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [Sparkit-learn](https://github.com/lensacom/sparkit-learn) - PySpark + scikit-learn = Sparkit-learn. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/spark_big.png" alt="Apache Spark based">
 * [mlpack](https://github.com/mlpack/mlpack) - A scalable C++ machine learning library (Python bindings).
@@ -108,7 +100,7 @@
 ### Extreme Learning Machine
 * [Python-ELM](https://github.com/dclambert/Python-ELM) - Extreme Learning Machine implementation in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [Python Extreme Learning Machine (ELM)](https://github.com/acba/elm) - A machine learning technique used for classification/regression tasks.
-* [hpelm](https://github.com/akusok/hpelm) - High performance implementation of Extreme Learning Machines (fast randomized neural networks). ![alt text][gpu]
+* [hpelm](https://github.com/akusok/hpelm) - High performance implementation of Extreme Learning Machines (fast randomized neural networks). <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
 
 <a name="ml-fm"></a>
 ### Kernel Methods
@@ -117,14 +109,14 @@
 * [tffm](https://github.com/geffy/tffm) - TensorFlow implementation of an arbitrary order Factorization Machine. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [liquidSVM](https://github.com/liquidSVM/liquidSVM) - An implementation of SVMs.
 * [scikit-rvm](https://github.com/JamesRitchie/scikit-rvm) - Relevance Vector Machine implementation using the scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [ThunderSVM](https://github.com/Xtra-Computing/thundersvm) - A fast SVM Library on GPUs and CPUs. <img height="20" src="img/sklearn_big.png" alt="sklearn"> ![alt text][gpu]
+* [ThunderSVM](https://github.com/Xtra-Computing/thundersvm) - A fast SVM Library on GPUs and CPUs. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
 
 <a name="ml-gbt"></a>
 ### Gradient Boosting
-* [XGBoost](https://github.com/dmlc/xgboost) - Scalable, Portable and Distributed Gradient Boosting. <img height="20" src="img/sklearn_big.png" alt="sklearn"> ![alt text][gpu]
-* [LightGBM](https://github.com/Microsoft/LightGBM) - A fast, distributed, high performance gradient boosting. <img height="20" src="img/sklearn_big.png" alt="sklearn"> ![alt text][gpu]
-* [CatBoost](https://github.com/catboost/catboost) - An open-source gradient boosting on decision trees library. <img height="20" src="img/sklearn_big.png" alt="sklearn"> ![alt text][gpu]
-* [ThunderGBM](https://github.com/Xtra-Computing/thundergbm) - Fast GBDTs and Random Forests on GPUs. <img height="20" src="img/sklearn_big.png" alt="sklearn"> ![alt text][gpu]
+* [XGBoost](https://github.com/dmlc/xgboost) - Scalable, Portable and Distributed Gradient Boosting. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
+* [LightGBM](https://github.com/Microsoft/LightGBM) - A fast, distributed, high performance gradient boosting. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
+* [CatBoost](https://github.com/catboost/catboost) - An open-source gradient boosting on decision trees library. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
+* [ThunderGBM](https://github.com/Xtra-Computing/thundergbm) - Fast GBDTs and Random Forests on GPUs. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
 
 <a name="dl"></a>
 ## Deep Learning
@@ -172,13 +164,13 @@
 
 <a name="dl-mxnet"></a>
 ### MXNet
-* [MXNet](https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler. ![alt text][mx]
-* [Gluon](https://github.com/gluon-api/gluon-api) - A clear, concise, simple yet powerful and efficient API for deep learning (now included in MXNet). ![alt text][mx]
-* [MXbox](https://github.com/Lyken17/mxbox) - Simple, efficient and flexible vision toolbox for mxnet framework. ![alt text][mx]
-* [gluon-cv](https://github.com/dmlc/gluon-cv) - Provides implementations of the state-of-the-art deep learning models in computer vision. ![alt text][mx]
-* [gluon-nlp](https://github.com/dmlc/gluon-nlp) - NLP made easy. ![alt text][mx]
-* [Xfer](https://github.com/amzn/xfer) - Transfer Learning library for Deep Neural Networks. ![alt text][mx]
-* [MXNet](https://github.com/ROCmSoftwarePlatform/mxnet) - HIP Port of MXNet. ![alt text][mx] <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">
+* [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/R_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/R_big.png" alt="MXNet based">
+* [MXbox](https://github.com/Lyken17/mxbox) - Simple, efficient and flexible vision toolbox for mxnet framework. <img height="20" src="img/R_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/R_big.png" alt="MXNet based">
+* [gluon-nlp](https://github.com/dmlc/gluon-nlp) - NLP made easy. <img height="20" src="img/R_big.png" alt="MXNet based">
+* [Xfer](https://github.com/amzn/xfer) - Transfer Learning library for Deep Neural Networks. <img height="20" src="img/R_big.png" alt="MXNet based">
+* [MXNet](https://github.com/ROCmSoftwarePlatform/mxnet) - HIP Port of MXNet. <img height="20" src="img/R_big.png" alt="MXNet based"> <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">
 
 <!--a name="dl-cntk"></a-->
 <a name="dl-chainer"></a>
@@ -217,14 +209,14 @@
 <a name="dm-cont"></a>
 ### Data Containers
 * [pandas](https://pandas.pydata.org/pandas-docs/stable/) - Powerful Python data analysis toolkit.
-* [cuDF](https://github.com/rapidsai/cudf) - GPU DataFrame Library. <img height="20" src="img/pandas_big.png" alt="pandas compatible"> ![alt text][gpu]
+* [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">
 * [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. ![alt text][r]
+* [datatable](https://github.com/h2oai/datatable) - Data.table for Python. <img height="20" src="img/R_big.png" alt="R inspired/ported lib">
 * [koalas](https://github.com/databricks/koalas) - pandas API on Apache Spark. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
 * [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 which efficiently applies any function to a pandas dataframe or series in the fastest available manner.
@@ -234,7 +226,7 @@
 * [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. ![alt text][r]
+* [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">
@@ -285,7 +277,7 @@
 * [FairML](https://github.com/adebayoj/fairml) - FairML is a python toolbox auditing the machine learning models for bias. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [L2X](https://github.com/Jianbo-Lab/L2X) - Code for replicating the experiments in the paper *Learning to Explain: An Information-Theoretic Perspective on Model Interpretation*.
 * [PDPbox](https://github.com/SauceCat/PDPbox) - Partial dependence plot toolbox.
-* [pyBreakDown](https://github.com/MI2DataLab/pyBreakDown) - Python implementation of R package breakDown. <img height="20" src="img/sklearn_big.png" alt="sklearn">![alt text][r]
+* [pyBreakDown](https://github.com/MI2DataLab/pyBreakDown) - Python implementation of R package breakDown. <img height="20" src="img/sklearn_big.png" alt="sklearn"><img height="20" src="img/R_big.png" alt="R inspired/ported lib">
 * [PyCEbox](https://github.com/AustinRochford/PyCEbox) - Python Individual Conditional Expectation Plot Toolbox.
 * [Skater](https://github.com/datascienceinc/Skater) - Python Library for Model Interpretation.
 * [model-analysis](https://github.com/tensorflow/model-analysis) - Model analysis tools for TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
@@ -298,7 +290,7 @@
 * [Netron](https://github.com/lutzroeder/Netron) - Visualizer for deep learning and machine learning models (no Python code, but visualizes models from most Python Deep Learning frameworks).
 * [FlashLight](https://github.com/dlguys/flashlight) - Visualization Tool for your NeuralNetwork.
 * [tensorboard-pytorch](https://github.com/lanpa/tensorboard-pytorch) - Tensorboard for pytorch (and chainer, mxnet, numpy, ...).
-* [mxboard](https://github.com/awslabs/mxboard) - Logging MXNet data for visualization in TensorBoard. ![alt text][mx]
+* [mxboard](https://github.com/awslabs/mxboard) - Logging MXNet data for visualization in TensorBoard. <img height="20" src="img/R_big.png" alt="MXNet based">
 
 <a name="rl"></a>
 ## Reinforcement Learning
@@ -317,7 +309,7 @@
 
 <a name="bayes"></a>
 ## Probabilistic Methods
-* [pomegranate](https://github.com/jmschrei/pomegranate) - Probabilistic and graphical models for Python. ![alt text][cp]
+* [pomegranate](https://github.com/jmschrei/pomegranate) - Probabilistic and graphical models for Python. <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
 * [pyro](https://github.com/uber/pyro) - A flexible, scalable deep probabilistic programming library built on PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [ZhuSuan](http://zhusuan.readthedocs.io/en/latest/) - Bayesian Deep Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [PyMC](https://github.com/pymc-devs/pymc) - Bayesian Stochastic Modelling in Python.
@@ -339,7 +331,7 @@
 * [hsmmlearn](https://github.com/jvkersch/hsmmlearn) - A library for hidden semi-Markov models with explicit durations.
 * [pyhsmm](https://github.com/mattjj/pyhsmm) - Bayesian inference in HSMMs and HMMs.
 * [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">
-* [MXFusion](https://github.com/amzn/MXFusion) - Modular Probabilistic Programming on MXNet ![alt text][mx]
+* [MXFusion](https://github.com/amzn/MXFusion) - Modular Probabilistic Programming on MXNet <img height="20" src="img/R_big.png" alt="MXNet based">
 * [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">
 
 <a name="gp"></a>