Everyone wants to learn Artificial Intelligence but don’t know how to start? Today we are going to discuss about best 5 Artificial Intelligence Frameworks that will help you in your learning.
TensorFlow is an open-source software library developed at Google Brain along with the DeepMind Technologies Group. It provides a framework for machine learning models built using various algorithms including neural networks. In addition to that, TensorFlow aims to make machine learning accessible to everyone.
What is tensorflow?
Tensorflow is a free software library created by Google Research that helps developers build data-driven applications using machine learning (ML). TensorFlow enables developers to build ML models that work well in practice without being experts in programming, math, statistics, or computer science.
Is tensorflow good for beginners?
Yes! Tensors are a mathematical concept that allow us to represent any complex object. A tensor can be thought of as a multi-dimensional array where each entry represents some value associated with a dimensions.
Can I use tensorflow outside of google?
Yes! Any company with an open source policy can take our core package and modify it to meet their own requirements. To get a full understanding of how to do this we recommend reading the documentation provided here.
PyTorch is an open-source deep learning library created at Facebook and designed specifically for research purposes. Its core idea is simple – get access to powerful tools without needing to know how they work under the hood.
PyTorch is a framework designed to make deep learning accessible to everyone. It provides a simple interface for defining models, training them, running inference, exporting results, and interfacing with CUDA/cuDNN libraries.
Why did you create PyTorch?
I created pytorch because I wanted to make it easier to train neural networks without having to deal with low level details (such as setting up the correct CUDNN version). PyTorch doesn’t require any knowledge about CUDA programming.
How does PyTorch compare to TensorFlow, Keras, etc.?
PyTorch is built using python and GPU-accelerated tensor algebra operations. All layers are defined as ‘numpy’ arrays. A single layer consists of several matrices arranged in depthwise fashion (i.e., each matrix contains copies of input data along two dimensions). These numpy arrays can be combined together at various points in the network, producing complex structures. This makes defining a neural network much simpler than if we were working with tensors directly. In addition, PyTorch supports preprocessing layers, where users have access to preprocessed inputs that do not need to go through a series of convolutional or pooling operations.
Is PyTorch free software / Open Source?
Yes, PyTorch is open source under the MIT License. See LICENSE file for more information.
Keras Keras is a Python library for building and evaluating deep learning models. It helps simplify the process of designing and training complex models by providing useful abstractions for data layers, model architecture, optimization algorithms, and metrics evaluation. Since its first release in 2015, it’s received over 1 million downloads on Github.
What is Keras?
Keras is a deep learning library for Python. It supports both TensorFlow and Theano backends. Both are capable of running on CPU or GPU devices.
Why use KERAS?
The Keras API makes implementing neural networks simple by providing familiar layers and activation.
Can I use Keras for my project?
Yes! You can use Keras for almost anything. There are even Keras projects out there that have been trained to recognize cats.
MXNet is a high-level API and runtime system for Machine Learning (ML) applications. It aims to provide a general solution for ML practitioners, researchers and engineers who need to build neural network models. MXNet is written entirely in C++ and supports distributed/parallel computing via the MPI protocol.
What MXNet is?
Mxnet is a toolkit developed at Microsoft Research for deep learning research. MXNet is based on Python and C++ and supports both CPU and GPU computation.
What does Mxnet do?
It provides tools to convert tensorflow models to MXNet-compatible format, run inference, build model checkpoints, etc. MXNet is designed to support both CPU and GPU computing.
Can I use TensorFlow instead of MXNet?
Yes, if TensorFlow was built before August 2019 (with some restrictions).
Which frameworks are supported?
MXNet now supports PyTorch, TensorFlow, caffe, Theano, and pybind11.
Chainer is a lightweight, modular and extensible deep learning platform for Python. It consists of two parts – Chainer and ChainerX. The latter is an extension to Chainer that includes additional features to help developers build even more advanced neural networks.
What is Chainer?
Chainer is a general purpose framework built upon a modular design based around a core library, API, and runtime library. All components work together to provide a high performance environment for training neural networks.
What does Chainer do?
Chainer provides three layers of abstraction: (1) A Core Library
(3) A Runtime Infrastructure.
By separating these different layers, we provide the opportunity for developers to build applications without knowledge of the underlying details of how those applications run.
Why should I learn Chainer?
- You want to build deep learning models and apply them to real world problems
- You want to deploy your models at scale
- You want to have fun building cool stuff
In this article, we have discussed 5 Best Artificial Intelligence Frameworks. These Frameworks will Help you to learn a lot in Artificial Intelligence. If you like this article share it with your friends and for further queries just comment below.