Machine learning can be categorise into two domains applied machine learning and research machine learning. Applied machine learning consists of application part of machine learning such as image recognition, object detection, image classification ,etc. Whereas the research part of machine learning consist of exploration of an existing topic or new topic and inventing new concepts such as transformers, GAN etc.. .
both of them needs to test the performance of the model which can be done with the help of machine learning frameworks which makes our works a lot easier, efficient, cheap and fast.
What Are machine learning Frameworks?
A machine learning framework is a tool which helps us to build our model fast without going into the details of each if we want to build neural networks we can import the keras library and write a few lines of code to build neural networks instead of the tedious process of building a neural network from scratch by writing thousands of lines of code.
machine learning frameworks consists of built in functions which makes our work a lot easier and precise. We don’t have to be an expert in machine learning to write code in a framework, any person who is new to machine learning and has some prior experience in coding can implement a machine learning model by utilising appropriate functions and libraries.
The code written in a machine learning framework can easily be optimize by increasing the accuracy of the model.
thus, and decreasing the loss function by utilizing appropriate gradient descent. A machine learning model also provides parallelization to distribute the computation process.
Different types of machine learning frameworks
1. Tensorflow:
It is an open source framework built and maintained by Google. however, It is mainly used for building deep learning models which incorporate neural networks. thus, by importing the keras library in tensorflow which can be use to build a neural network by writing a few lines of code.
It is in use for research and production. thus, It includes the concept of transfer learning.
2. Pytorch:
It is another open source machine learning framework built and maintained by facebook. Initially the framework was called Torch which was built in language other than python and then later on was built for python hence the name pytorch . It is mainly used for research based machine learning models. may implement models which consist of computer vision and natural language processing. It includes the concept of transfer learning.
3. Amazon sagemaker:
It is a framework built by amazon in 2017 which is used to create, train and deploy machine learning models in the cloud. The models can be deployed on embedded devices such as raspberry pire, microprocessors etc… and can also be deployed on devices such as browsers and mobile devices.
4. Theano:
It is one of the most popular framework for statistical machine learning models. It utilizes the python language for implementing machine learning frameworks. also regarded as a neural network library.
5. Sci-kit learn:
also known as sklearn is used for unsupervised learning. In unsupervised learning we do not give labels to our dataset instead the data is classified into clusters where each cluster contains similar data.The sklearn framework includes algorithms such as k-means algorithms which clusters the input data based on the similarities between each data item.
6. Microsoft cognitive toolkit:
It was built by microsoft to handle deep learning but it can be formulated to handle huge amounts of unstructured data for machine learning models. It’s useful for recurrent neural networks. It’s highly customizable and supports multi machine backends.
7. Keras:
Keras provides a high end API for tensorflow to build neural networks and it can be used for both machine learning and deep learning , it is easy to learn and implement.It can be used for computer vision , natural language processing and many other concepts in deep learning.
8. CAFFE:
It is a machine learning framework which is use for deploying machine learning models on mobile devices , it is in use by facebook to deploy machine learning models on mobile devices.
we have to be familiar with C++ in order to implement machine learning models in CAFFE.
Written By: Junaid Khan
Reviewed By: Vikas Bhardwaj
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