AI is one of the most important breakthroughs humanity is working on at the moment.Sundar Pichai, CEO (Google, Alphabet)
Machine Learning 🎰, Data Science 🧬 ? Ah, can you explain them in simple words? 🤔
Let me start with Machine Learning 🤖
So, computers are dumb (No offence)! They are not like humans who can learn from their experience and do better next time 😅 Nope, it’s not how they work.🙅♂️
We human 🙋🏻♂️ tend to make machines learn by providing them data so they could figure out a pattern and so could make a “logical guess”. 🤖
Data Science is simpler for me to explain! 😋
It’s nothing but to get lots of data 🏋️♀️ and then performing some maths to fetch some useful information from it. 📊
Ok, But why to Know about their Libraries and Frameworks for Projects ? 🙄
Libraries and Frameworks makes your life Easy, Believe Me 😎
Think them as ready made functions(functionality) written by someone 👨💻 so you don’t have to write them from scratch. 😓
This way you just have to import 😉 the functionality from the Github Repo I have provided with each of them and get Started !
So, Where Were We ? 🤔
Trending Machine Learning and Data Science Libraries and Frameworks
⊹While dealing with Machine Learning projects 🎰 or any tech which involves Complex Mathematical tasks, Matrix and multi-dimensional arrays play an important role ✅ in most of them.
Luckily, 😇 we found a library ml-matrix that provides operations like Transposing, Finding Mean, Covariance, Inversion, and obviously all basic arithmetic operations on Matrix. 🧮
Size: 380 kB
Weekly downloads ~18,028
Github Repo: Click Here 📦
🧬Working with a Neural Network can be very hard sometimes 🤯 but the brain module makes the work too much easier for Neural Network enthusiasts in their projects.
The best part 🙌 is you can train the data both in frontend (Browser) or Backend (Node.js).
You just have to use a method brain provides – train() where you have to pass an array of the training data. 🤹♀️
💁🏻♂️ It also supports streams in the newer versions where you could use pipe() to send the training data to your network.
Modules Update Frequency : Low
Weekly Download : ~(40-60)
Issues Frequency : Very Low
✅ Working Demo: http://harthur.github.io/brain/
Github Repo: Github Repo for Brain 📦
🚀Theano is a python library that helps in the optimization of compilers. It helps in mathematical computation ➗ and evaluating expressions at high speed. 🏎
Theano can be run both on CPU and GPU. It is worth noting that GPU provides 140 times 😲 computational power as compared to running a compiler on CPU architecture.
💁🏻♂️ Theano is very helpful during bugs as it is built to auto handle errors or any exceptions and contains in-built tools for unit testing for projects.
Modules Update Frequency : ~Once every year
Size: 2.8 MB
Documentation: Theano python library documentation
Github Repo: Github Repo for Theano 📦
📊 Matplotlib is very famous for data visualization. Just by providing a set of data, you could generate production-quality graphics.
Matplotlib is made using python👌 it has an interface just like we have for Matlab and is very user-friendly to use in projects. 😃
Histogram 📊 bar charts, scatter plots can be made very efficiently using Matplotlib.
Standard GUI toolkits like Tkinter, WxPython, GTK+ use it as they need an object-oriented API to work. ✅
Documentation: Data Visualization with Python
Github Repo: Github Repo for Matplotlib 📦
💈 KerasJS is an open-source library and is mostly used for neural networks and machine learning.
It can be used to train the data in any backend technology. It can be framed like another Tensorflow.js. ✅
While working with KerasJS in Node.js environment 🎯 it can only be run in CPU mode 🙌
Keras has support for high-level API which takes care of abstraction provided by backend frameworks.
Documentation: Trending Machine Learning Library KerasJS
✅ Working Demo: Checkout KerasJS Demo Here
Github Repo: KerasJS (A Machine Learning Library) 📦
🐼 It’s the most popular library for Data Analysis in Python Programming Language. ✅
Pandas provide a high-level abstraction ⛺️ over Numpy which is written in C.
The main data structure to be familiar with 🏌🏻♂️ while working with Pandas are: DataFrames and Series
- Series is just like a “list” we see in the python data structure, the difference being, here list is labeled with an index. 😃 ☝️
- DataFrames can be made using “dicts” in python and is simply a set of rows and columns. 😃 ✌️
Github Repo: Pandas Repo ( Data Analysis Python Library ) 📦
💁🏻♂️ PyTorch has multiple functions that it supports like Machine Learning, Computer Vision, or Natural Language Processing. ✅
The 😎 best thing about PyTorch is the ease of learning and using it in projects.
PyTorch can easily be integrated ✚ with your existing python project or even Numpy. Numpy and PyTorch are mostly the same but here you could also make 👉 computations on Tensors.
Building computation graphs dynamically and changing them is for what PyTorch is known.👍
Github Repo: PyTorch MachineLearning Library 📦
↪ TensorFlow was initially made by Google for its internal use but is now open-source.
🦾 It is a computational framework for making Machine Learning models. It provides various toolkits 🛠 that can be used at various levels of abstraction.
TensorFlow allows you to write code 👨💻 in whatever abstraction is best for you. For instance, you can write code in C++ and call the method from your Python Code. 😉
Not only this, but you can also mention where the code should run, whether it should be GPU or the CPU. 🙌
Module Update Frequency: Very High ✅
Github Repo: TensorFlow Repo 📦
🚀 The Scipy offers various modules like Ordinary Differential Equation (ODE), Fast Fourier transform, image optimization, integration interpolation linear algebra, special functions, and image processing.
The data structure used by Scipi is actually nothing but a multi-dimensional array provided by the NumPy module and therefore Scipy depends upon NumPy for array manipulation subroutine 🎯
↪ Also, it is worth noting that most of the new Data Science features are available in Scipy rather than Numpy.
Github Repo: Scipy ( Data Science Library) 📦
🎯The scikit-learn was initially developed for a project at Google. Scikit-learn is built on top of 2 python libraries – Scipy and Numpy and has no doubt become the most popular library for machine learning algorithms.
Scikit-learn has a large range of Supervised and Unsupervised algorithms that work on python.👍
Some of the major 😲 machine learning function which Scikit-learn provides includes preprocessing, dimensionality reduction, model selection, regression, clustering, and classification
Github Repo: TensorFlow Repo 📦
📍Numpy is not only a data handling library known for its capability to handle multidimensional data but also it is known for its speed of execution and vectorization capabilities.
Major features 👌 of Numpy are capabilities like transpose, reshape of a Matrix.
💁🏻♂️ Also helpful is boosting the performance 🏎 and handling garbage collection with ease in projects. The capability to vectorize operation again improves performance and parallelization capabilities.
👉 Some people do not like it’s dependency which is majorly upon C/C++.
Github Repo: Numpy Data Handling Library 📦
📊 In python, we can take the help of StatsModels to add statistics or Algorithms in the form of Classes and Functions. Its capabilities 🙌 include time series analysis, regression models and autoregression
StatsModels provides detailed Statistics 📈 which is more than Scikit-learn.
Why it is more popular 🤔 in the data science 🔬 world is because of its capabilities to go along with Pandas or Matplotlib.
But still, the downside of it is that it is not as well documented as Scikit, so beginners could face problems 🙄 while working with it.
Github Repo: StatsModel ( Data Science Library) 📦
💹 It is the most widely used library or algorithm which is not only used in the real world 🌍 but also seen so many times being used in various competitions.
XGBoost 🏎 provides a highly optimized and distributed experience. XGBoost enables parallel execution which is the major reason for its immense 🎯 performance improvement.
👉 It has capabilities to run over distributed frameworks like Hadoop with ease. Similarly, it also supports R, Java.
Github Repo: XGBoost Github Repo 📦
🔅 LightGBM can be said as another version of GBM(Gradient Boosting Machine) which is faster ⚡️ LightGBM is developed by Microsoft.
💁🏻♂️. It is similar to XGBoost in most aspects, barring a few around the handling of categorical variables and the sampling process to identify node splits.
LightGBM has also capabilities 💪 to utilize GBM and improve performance.
Github Repo: Light GBM Github Repo 📦
𐂷Explain Like I am 5(years old) 👼 is what it stands for. It is a classifier that provides debugging classifiers and provides an explanation of the prediction.
💁🏻♂️. To help understand the predictions it provides wrappers around different libraries like scikit-learn, xgboost, and some more.
Some algorithms like decision trees 🌲 are inherently explainable, yet not all of them 🚫 are hence ELI5 helps in explaining those!
⚡️ FastAI is similar to Keras. It is built on top of PyTorch. It is mostly used to get fast(as the name suggests) and accurate neural network. It provides consistent APIs and built-in support for 🏞 image/vision, text, etc.
Github Repo: FastAI ( Neural Network Library ) 📦
☕️Caffee is a (Convolutional Architecture for Fast Feature Embedding) deep learning framework.
Caffe is built by keeping speed, expression and modularity in mind 😇 .
Speed of Caffe 🚀 makes it a perfect choice for industry deployment and research experiments.
👉 It was primarily used/designed for 🏙 image classification and related tasks, though it supports other architectures including LSTMs and Fully Connected ones as well.
Github Repo: Caffe Deep Learning Framework Github Repo 📦
🕸Gluon is developed by AWS/Microsoft which is a high-level deep learning library, It is currently made available by Apache MXNet which allows ease of use of AWS and Microsoft clouds.
💁🏻♂️ Gluon is developed to be fast, friendly 👯♀️ , and consistent.
It is made to improve speed 🚀 flexibility and accessibility of deep learning technology for all developers 👩💻
Github Repo: Gluon Deep Learning Library 📦
🗣Apache MXNet is a flexible and efficient library for Deep Learning. It is useful for flexible research prototyping and production.
😃 👉 It is one of the most used libraries when it comes to image related use cases.
It requires a lot of boilerplate code 😟 but on the positive note, its performance covers its downsides 😄
Apache MXNet provides around 8 different language bindings including Scala, C++, R, PERL 🙌
Github Repo: Apache MXNet ( Deep Learning Library ) 📦
🎙The Natural Language ToolKit or NLTK offers different Natural Language Processing Tasks. Since 2001, it has provided a lot of features 👏
The list of features includes 👉 POS taggers, n-gram analyzers, tokenization (it provides different tokenizers), collocation parsers, and many more.
💁🏻♂️ NLTK utilizes years of research into linguistics and machine learning to provide such kinds of features. 😁
Github Repo: NLTK ( Linguistic | Machine Learning Library ) 📦
📝Gensim is particularly made for unsupervised topic modeling tasks apart from NLP tasks.
🎯 It includes functionality 🤺 like word representations using fastText and word2vec.
Gensim can handle 👍 large volumes of data using streaming and out of memory algorithms implemented ✅
🤔 What sets it apart from other NLP libraries is the robustness and efficient implementations.
Github Repo: Gensim | Library 📦
🔉 Spacy is a multi-language( English, German, French, Portuguese, etc.) Natural Language Processing library.
↪️ It has tokenizers and Named Entity Recognizers for various languages. Now, if you are searching NLP for production you can💁🏻♂️ choose Spacy as compared to NLTK(used mostly for academic purposes) ☑️
👉 Now, Spacy not only for NLP features but it also exposes deep learning based approaches ✅ and this enables it to use it with other tech like keras, Tensorflow, Scikit-learn, and many more.
Github Repo: Spacy NLP Library | Deep Learning Library 📦
🌊 Seaborn is a high-level visualization library that is made on top of Matplotlib.
💁🏻♂️. Whatever you could do with Matplotlib, Seaborn would provide it with ease (Seaborn is easier than Matplotlib) ✅
It provides capabilities 🤺 to perform the handling of categorical variables, regression analysis, and aggregate statistics. 🧮
Github Repo: Seaborn Visualization Library 📦
💁🏻♂️. It provides basically 2 modes of operation. The first mode is a high-level mode where complex plots ፨ are generated and a low-level mode.
Low level mode provides more ways 😉 of customization !
The only downside being its 🌅 visualization interface is different from others so making it difficult for migration ⏳
Github Repo: Bokeh | Visualization Library 📦
💹Plotly is the most famous production-ready visualization platform which has its wrapper present for mostly all of the languages like R, Matlab, Julia.
💁🏻♂️ Plotly provides online plotting, visualizations, statistical tools for developers 👨💻
In case you want to convert ➿ your ggplot or matplotlib to interactive visualization then Plotly is the best solution 😎 for that.
Github Repo: Plotly Visualization Library 📦
🧰Cognitive Toolkit by Microsoft is a deep learning tool that describes neural networks as a series of computation steps ✓
In this directed graph 𐂷 the leaf node represents the initial value, while other nodes represent other operations upon their inputs ✔️
It helps developers to combine ➕ different model types such as feed-forward, Convolutional Neural Networks 🧬
👉 It can be included in C++, Python, C# program, or as a standalone machine learning tool.
Github Repo: CNTK Machine Learning Tool 📦
😋Lasagne is a lightweight library that can be used to build and train neural networks in Theano.
🧐 It is designed on Six principles:
To learn about them 🔎 in detail click here
Github Repo: Lasagne | Neural Network 📦
📜NoLearn contains a number of wrappers and abstractions around existing neural network libraries in which most notably is Lasagne.
Also some more machine learning utility modules are included with that✓
All code in NoLearn is written to be compatible with the Scikit-Learn✓
WARNING: Avoid to use in production, documentation seems to be a little outdated and support is also not too great.
Github Repo: NoLearn Neural Network Library Wrapper 📦
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