Best AI Stacks for 2020
Best AI Stacks for 2020
Artificial Intelligence is spreading across several industries. While the last decade saw AI rise beyond Sci-fi; this decade promises more spending on research, development, and deployment. There is no doubt that AI touches most aspects of our lives today and it is only set to grow in the coming years.
Talking about AI Stacks — they represent the infrastructure that sets perfectly with the user’s hardware architecture. Once the AI solutions are ready for deployment, the Stacks suggest what’s needed in terms of scalability, performance, and stability. Thus it is important to choose the appropriate stack as per your unique style and project.
The following are AI Stacks that have made headlines in 2020:
PyTorch
First released in 2016 and developed by Facebook, it was created with the objective of production optimization while making it simpler to write for Python programmers. Today, PyTorch is widely used in the research community. Written in C, PyTorch is based on Torch, a framework for doing fast computation. Tensor computation with GPU acceleration and deep neural networks are two of its important features. Glow, a machine learning compiler raises the performance of a deep learning framework.
* Special Features
The Torch backend has a C++ module for autodifferentiation.
It uses the eager mode computation by default. Thus a user can run a neural net while building it; making it easier to debug. This also lets a user construct neural nets with conditional execution.
TensorFlow 2.0
The Deep Learning framework Tensorflow 2.0 is open-source library for managing data workflow. It was built with production in mind. It is an end-to-end python ML library meant to perform high-level numerical computations. TensorFlow’s two of the main applications are neural networks and ML. Deep neural networks are used for NLP (Natural Language Processing), image recognition, recurrent neural networks, handwritten digit classification, PDE (Partial Differential Equation), and word embedding. TensorFlow supports different platforms like mobile, desktops, and servers.
* Special Features
It is easy to get on TensorFlow as there is extensive data, pertained models and, Google Colab notebooks.
With its established user base it offers a plethora of tools. JavaScript and Swift APIs for mobile development while TensorFlow Lite, used for IoT, lets you optimize and compress models for it.
Keras
This is an open-source platform for neural network and machine learning applications. Written in Python programming language, it is known for being user-friendly, extensible, and modular. It is designed for front-end applications for server and web products, thus it cannot be used on backend even for the front-end framework applications. Keras can operate on Microsoft Cognitive Toolkit, TensorFlow, PlaidML, MXNet, Theano, or Deeplearning4j software stacks.
* Special features
Various pre-labeled datasets like IMDB review sentiment classification, MNIST handwritten digit dataset, Reuter’s newswire topics classification; etc can be directly imported and loaded under Keras.
Many layers and parameters like evaluations metric, optimizers, and loss functions used for configuration, construction, evaluation and, training of neural networks are available.
Keras offers multiple methods for data pre-processing.
Scikit-learn
Scikit-learn remains a popular choice for clustering, regression, and classification algorithms. Built on NumPy, SciPy, and matplotlib, it is an open-source Python machine learning library. Latest Scikit-learn 0.23.0 version is packed with improvements to K-Means and Gradient Boosting and has a new generalized Linear Models. It is a preferred tool for data analysis and mining.
* Special Features
It is distributed under a BSD license. It supports supervised and unsupervised ML.
It is the most preferable library for machine learning. Its benefits are Decision boundary learning, Reduction of dimensionality, Advanced probability modeling, Decision tree pruning & induction, Unsupervised classification & clustering, Feature analysis & selection, and Outlier detection & rejection.
There are many machine learning and deep learning libraries present in the market. To figure out which one to choose, consider the application, preferred language, maturity of the framework, and support. If there is something that is still hard to comprehend feel free to get in touch with us.
