Original article can be found here (source): Artificial Intelligence on Medium
Best Python Libraries for Machine Learning and Deep Learning
While there are a lot of languages to pick from, Python is among the most developer-friendly Machine Learning and Deep Learning programming language, and it comes with the support of a broad set of libraries catering to your every use-case and project.
The revolution is here! Welcome to TensorFlow 2.0.
TensorFlow is a fast, flexible, and scalable open-source machine learning library for research and production.
TensorFlow is one of the best library available for working with Machine Learning on Python. Offered by Google, TensorFlow makes ML model building easy for beginners and professionals alike.
Using TensorFlow, you can create and train ML models on not just computers but also mobile devices and servers by using TensorFlow Lite and TensorFlow Serving that offers the same benefits but for mobile platforms and high-performance servers.
Some of the essential areas in ML and DL where TensorFlow shines are:
● Handling deep neural networks
● Natural Language Processing
● Partial Differential Equation
● Abstraction capabilities
● Image, Text, and Speech recognition
● Effortless collaboration of ideas and code
Core Task: Build Deep Learning models
To understand how to accomplish a specific task in TensorFlow, you can refer to the TensorFlow tutorials.
Keras is one of the most popular and open-source neural network libraries for Python. Initially designed by a Google engineer for ONEIROS, short for Open-Ended Neuro Electronic Intelligent Robot Operating System, Keras was soon supported in TensorFlow’s core library making it accessible on top of TensorFlow. Keras features several of the building blocks and tools necessary for creating a neural network such as:
● Neural layers
● Activation and cost functions
● Batch normalization
Keras extends the usability of TensorFlow with these additional features for ML and DL programming. With a helpful community and a dedicated Slack channel, getting support is easy. Support for the convolutional and recurrent neural network also exists along with standard neural networks. You can also refer to other example models in Keras and Computer Vision class from Stanford.
Core Task: Build Deep Learning models
Getting Started with Keras —
Developed by Facebook, PyTorch is one of the few machine learning libraries for Python. Apart from Python, PyTorch also has support for C++ with its C++ interface if you’re into that. Considered among the top contenders in the race of being the best Machine Learning and Deep Learning framework, PyTorch faces touch competition from TensorFlow. You can refer to the PyTorch tutorials for other details.
Some of the vital features that set PyTorch apart from TensorFlow are:
● Tensor computing with the ability for accelerated processing via Graphics Processing Units
● Easy to learn, use and integrate with the rest of the Python ecosystem
● Support for neural networks built on a tape-based auto diff system
The various modules PyTorch comes with, that help create and train neural networks:
● Tensors — torch.Tensor
● Optimizers — torch.optim module
● Neural Networks — nn module
Pros: very customizable, widely used in deep learning research
Cons: fewer NLP abstractions, not optimized for speed
Core task: Developing and training deep learning models
Keras vs Tensorflow vs PyTorch | Deep Learning Frameworks Comparison
Scikit-learn is another actively used machine learning library for Python. It includes easy integration with different ML programming libraries like NumPy and Pandas. Scikit-learn comes with the support of various algorithms such as:
● Dimensionality Reduction
● Model Selection
Built around the idea of being easy to use but still be flexible, Scikit-learn is focussed on data modelling and not on other tasks such as loading, handling, manipulation and visualization of data. It is considered sufficient enough to be used as an end-to-end ML, from the research phase to the deployment. For a deeper understanding of scikit-learn, you can check out the Scikit-learn tutorials.
Core Task: Modelling
Pandas is a Python data analysis library and is used primarily for data manipulation and analysis. It comes into play before the dataset is prepared for training. Pandas make working with time series and structured multidimensional data effortless for machine-learning programmers. Some of the great features of Pandas when it comes to handling data are:
● Dataset reshaping and pivoting
● Merging and joining of datasets
● Handling of missing data and data alignment
● Various indexing options such as Hierarchical axis indexing, Fancy indexing
● Data filtration options
Pandas make use of DataFrames, which is just a technical term for a two-dimensional representation of data by offering programmers with DataFrame objects.
Core task: Data manipulation and analysis
Google Trends — Pandas Interest Over Time
NLTK stands for Natural Language Toolkit and is a Python library for working with natural language processing. It is considered as one of the most popular libraries to work with human language data. NLTK offers simple interfaces along with a wide array of lexical resources such as FrameNet, WordNet, Word2Vec and several others to programmers. Some of the highlights of NLTK are:
● Searching keywords in documents
● Tokenization and classification of texts
● Recognition on voice and handwriting
● Lemmatizing and Stemming of words
NLTK and its suite of packages are considered a reliable choice for students, engineers, researchers, linguists and industries that work with language.
Core Task: Text processing
● Spark MLlib
MLlib is Apache Spark’s scalable machine learning library
Developed by Apache, Spark MLlib is a machine learning library that enables easy scaling of your computations. It is simple to use, quick, easy to set up and offers smooth integration with other tools. Spark MLlib instantly became a convenient tool for developing machine learning algorithms and applications.
The tools that Spark MLlib brings to the table are:
Some of the popular algorithms and APIs that programmers working on Machine Learning using Spark MLlib can utilize are:
● Dimensional Reduction
● Basic Statistics
● Feature Extraction
Theano is a powerful Python library enabling easy defining, optimizing and evaluation of powerful mathematical expressions. Some of the features that make Theano a robust library for carrying out scientific calculations on a large-scale are:
● Support for GPUs to perform better in heavy-duty computations compared to CPUs
● Strong integration support with NumPy
● Faster and stable evaluations of even the trickiest of variables
● Ability to create custom C code for your mathematical operations
With Theano, you can achieve the rapid development of some of the most efficient machine learning algorithms. Built on top of Theano are some of the well known deep learning libraries such as Keras, Blocks and Lasagne. For more advanced concepts in Theano, you can refer to the Theano tutorial.
A flexible and efficient library for deep learning
If your field of expertise includes Deep Learning, you will find MXNet to be the perfect fit. Used to train and deploy deep neural networks, MXNet is highly scalable and supports quick model training. Apache’s MXNet not only works with Python but also with a host of other languages including C++, Perl, Julia, R, Scala, Go and a few more.
MXNet’s portability and scalability let you take from one platform to another and scale it to the demanding needs of your project. Some of the biggest names in tech and education such as Intel, Microsoft, MIT and more currently support MXNet. Amazon’s AWS prefers MXNet as its choice of preferred deep learning framework.
The NumPy library for Python concentrates on handling extensive multi-dimensional data and the intricate mathematical functions operating on the data. NumPy offers speedy computation and execution of complicated functions working on arrays. Few of the points in favor of NumPy are:
● Support for mathematical and logical operations
● Shape manipulation
● Sorting and Selecting capabilities
● Discrete Fourier transformations
● Basic linear algebra and statistical operations
● Random simulations
● Support for n-dimensional arrays
NumPy works on an object-oriented approach and has tools for integrating C, C++ and Fortran code, and this makes NumPy highly popular amongst the scientific community.
Core task: Data cleaning and manipulation
Google Trends — Numpy Interest Over Time
Python is a truly marvelous tool of development that not only serves as a general-purpose programming language but also caters to specific niches of your project or workflows. With loads of libraries and packages that expand the capabilities of Python and make it an all-rounder and a perfect fit for anyone looking to get into developing programs and algorithms. With some of the modern machine learning and deep learning libraries for Python discussed briefly above, you can get an idea about what each of these libraries has to offer and make your pick.