Most Popular Machine Learning Frameworks You Should Know
Machine learning frameworks are more and more increasing in number. They are also constantly advancing. All frameworks are not created equal. Each of them is different from one another, and it takes time to learn all of them. However, learning and understanding one of the most popular frameworks is enough to start.
Since each framework is dissimilar, some of them are more mathematically oriented while others are focused on statistical than neural networks. Moreover, some frameworks offer a rich set of linear algebra tools while others are primarily focused on deep learning only. Your task is to find the most suitable option for your needs.
Before that, what is the machine learning framework actually? This is an interface, tool, or library that allows developers to build machine learning models quickly and easily. By using machine learning frameworks, you don’t need to get into the details of fundamental algorithms. They usually come with useful features too.
An ML framework typically offers a clear, short way for determining machine learning models. It utilizes a selection of pre-built, advanced components. If you use a good ML framework, it tends to be developer-friendly and easy to understand. Here are some of the best recommendations for ML frameworks.
ML Framework #1: TensorFlow
TensorFlow is known as one of the most excellent frameworks for machine learning. Many developers use this framework since it has an incredible support community and a lot of inbuilt features. It is excellent among the other learning structures and has attracted a few Goliaths including IBM, Twitter, Airbus, and others.
As one of the best machine learning frameworks, TensorFlow can do a lot of tasks including classifications, neural networks, regressions, etc. It is even capable of running both on GPUs and CPUs. This is an open-source software library which was created by Google Brain team and here are some of its advantages.
TensorFlow is an extremely flexible system available out there. It offers users a variety of models and versions. Moreover, it also has multiple versions of the identical model that can be served concurrently. This flexibility makes it easier to do manual migration when newer versions are presented.
TensorFlow can be run on various platforms. You can run the framework on CPUs, GPUs, servers, desktops, as well as mobile computing platforms. As one of the best open-source machine learning frameworks, it can organize a trained model on mobile as an element of your product.
ML Framework #2: Apache Spark
This is another open-source framework you can find out there. It is originally created at Berkeley’s lab and was firstly debuted on May 2014. It is mainly written in Java, R, Python, and Scala. Even though it was generated at Berkeley’s lab, it was donated to Apache Software Foundation afterward.
The fundamental of this project is the Spark core. It is quite complicated, but you don’t need to worry about it. The point is that Spark allows you to work with its RDD data structures. Everyone with adequate knowledge of big data will know how to use it.
As one of the most popular machine learning frameworks, Spark offers a variety of features. As a user, you can work with Spark SQL data frames too. Thanks to these features, this framework can create dense and complex label vectors to feed machine learning algorithms.
One of its benefits is simplicity. Spark framework is simple, especially for data scientists who are familiar with tools like Python and R. Moreover, it also has good scalability. It offers the ability to run identical ML code on both small and big machines. It also offers good compatibility.
ML Framework #3: Caffe
Caffe is also an open-source framework developed by UC Berkeley. Caffe itself stands for Convolutional Architecture for Fast Feature Embedding. It is a deep learning tool which is primarily written in CPP. It supports a lot of diverse architecture types focusing mostly on segmentation and image classification.
This is one of the best machine learning frameworks that offer a lot of benefits. This framework is primarily employed in academic research projects and to develop startup’s prototypes. Yahoo is one of the companies that use Caffe. In this case, it combines Caffe with Apache Spark to develop CaffeOnSpark.
Caffe is considered as the quickest methods to apply deep neural networks to an issue. It also supports both GPU and CPU based acceleration like TensorFlow. Plus, it also has a well-organized Mat lab and python interface. Users can easily switch between GPU and CPU as well.
As a quick framework, Caffe is ideal for research experiments as well as industry deployment. It is capable to process more than 60M images each day with a lone Nvidia K40 GPU. This means the framework can run 1ms/image for inference and 4ms/image for learning.
ML Framework #4: Torch
If you are looking for popular machine learning frameworks, you will encounter Torch. It is a logical figuring structure which provides wide support for machine learning calculations. This framework is based on Lua profound learning system and is employed by industry Goliaths like Google, Facebook, and Twitter.
Torch used CUDA and C/C++ libraries for handing and was originally created to scale the development of building models and present general adaptability. This is also an open-source library which offers an appropriate scientific computing framework. It is claimed to be the easiest machine learning frameworks by its creators.
There are a number of benefits offered by this framework. First of all, Torch is very flexible to employ and it offers a high level of efficiency and speed. More interestingly, it also provides a lot of pre-trained models. Aside from Torch, Scikit-Learn and Microsoft Cognitive Toolkit are the other popular ML frameworks to know.
In conclusion, a machine learning framework is useful to ease your way in implementing this technology. There are a lot of ML frameworks available out there and they come with their pros and cons. If you don’t know which one to choose, you can use the list of machine learning frameworks above as reference.