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Machine Learning. This term is getting more and more exposition nowadays. But what is it exactly, and what are the best tools that will help you make the most out of it? These 2 questions will be answered in this article. First, let’s see what machine learning really is.

What is Machine Learning?

If you search around online, you’ll probably find different definitions for this. But the main idea is the following one: machine learning is basically the process of getting a computer to learn from things by itself. That is why AI (artificial intelligence) is present in the title.

To do this, you need to get a specific tool, that will let you create such programs. The most popular way of achieving success is by using an AI framework. Let’s refresh what a framework really is:

A framework is basically a way of work. It means that you can organize your working process and possibly even the environment the way you want, to give you a better development process. Efficiency is probably the most important aspect of a framework.

In this article, we’ll take a look at the best AI frameworks that you can use for machine learning. The tools we’ll cover are the following ones:

  1. TensorFlow
  2. Torch
  3. Theano
  4. Caffe
  5. Keras
  6. Microsoft CNTK
  7. Scikit-learn
  8. Azure ML Studio
  9. NET
  10. Spark MLlib
  11. Amazon Machine Learning

So, without any further ado, let’s jump in and start off with our first pick:

1. TensorFlow

This tool has many advantages that make it a good fit for those who need an efficient AI framework.

For example, the framework is developed by Google. This by itself is a big advantage. Why? Because you get extensive support and regular updates. This means that TensorFlow is keeping the pace with the modern industry of machine learning. You won’t have to worry about up to date features and new options, because they are coming out regularly.

It’s worth mentioning the support as well. Since the tool is backed up by Google, the support is also first grade. If any problem comes up, you can take a look in the huge community and you’ll surely find an answer.

Another pro that comes with using this framework is the flexibility. TensorFlow was built to be a modular system. This means that you can use parts of it on their own, but together as well. Portability is also an advantage of this tool. It works even on mobile systems if you don’t have a traditional desktop or laptop at hand.

2. Torch

Torch was first released in 2002, and it is more of a library than a traditional framework. That is because it consists of a multitude of algorithms that are used for machine learning.

Some of the features that Torch is making available are these:

  • N-dimensional arrays
  • Interface for C (programming language)
  • Linear algebra routines
  • GPU support is quick and efficient as well.
  • Speed and flexibility to develop your projects.

We have to mention the large community behind this tool as well. Torch is backed up by a large user base, that is always there to help out when needed. And another good aspect about this community is the fact that the members are actively improving the platform on GitHub. You can do that as well if you choose to use this library.

3. Theano

This tool isn’t the newest out there, but it’s still one of the most popular AI frameworks in use today. It is based on the Python programming language.

This is actually one of its greatest advantages. Python is a widely used language, and one of its best capabilities is machine learning and AI. So, if you already know Python (which is simple to learn), then getting used to Theano won’t be too complicated.

Because of the fact that it is a fairly old library, it is still an industry standard. Many other tools are in some way similar to this one. Maybe in their functionality or the build.

Something that Theano does really well, is the extensive code testing. After all, any new program needs to be at first well-tested, before releasing it to wide use. Theano is a leader in this area.

Besides these advantages, the framework also has the following features: it simplifies the handling of mathematical expressions, comes with symbolic differentiation and it’s working with GPUs too.

4. Caffe

This framework is a recent one, that was released in 2017. It was written in C++, so this makes it a good choice for many software engineers and programmers, C++ still being a widely used language.

Caffe is a framework best suited for the modeling of convoluted networks. If this is something you’ll do in the future, then Caffe can be your best bet for success.

Another advantage that comes with Caffe is the fact that it works seamlessly with a decent GPU. For example, the processing of tens of millions (literally) of images is a normal task it can do smoothly. And this is done only in 1 day. So, if speed is something you must have, then Caffe is still a good framework to achieve that.

Given the ability I just mentioned earlier, Caffe is a popular framework used extensively for visual recognition. It’s no surprise that it’s so good at this. After all, processing millions of images a day is a standard task for this platform.

5. Keras

Keras is the best candidate if you’re looking for a framework that is simple and easy to learn. This is one of its greatest advantages. Getting to know a new framework is essential if you want to be proficient using it.

Because Keras is simple and minimalistic, comes with another pro: it is lightweight. Any framework, no matter what type it is, it’s best when it is lightweight. Why is this true? Because being such, will grant you a better performance. After all, there are fewer resources needed to get the job done. So yes, Keras is fast as well.

The framework also supports the Python interface. I already mentioned above why it’s good to know Python. This framework is great at handling recurrent and convolutional networks as well, differently from Caffe, that only works best with convoluted networks.

The following areas are all well handled by Keras:

  • Classification
  • Generating text
  • Summarizing a text
  • Translations
  • It can even be used to develop speech recognition and other complex features.

If these are subjects that you’re interested in and want to learn, then Keras will be a good tool to make it all happen.

6. Microsoft CNTK

This framework is a contender to Google’s TensorFlow and other more popular AI frameworks. This is no surprise because it handles all kinds of processes related to machine learning. For example, it’s very good at implementing neural networks like convolutional and recurrent ones and LSTMs (Long Short Term Memory Networks).

The 3 main goals that were set when CNTK was created, were for the final product to be: flexible, efficient and performant. Did they manage to achieve these goals?

Pretty much all benchmark tests that have been run for CNTK, came out with good results. This means that the framework is granting a high performance for every process it handles at a certain time.

It is efficient and flexible as well, because it handles really well all kinds of tasks, like speech recognition, image recognition and the processing of it, text generation and training of production systems.

Another great thing about this framework is the fact that it works perfectly on Windows and Linux as well. This is especially good news for Linux users.

7. Scikit-learn

One of the best things about this framework is the fact that it’s an open source platform. This is because it has one of the most active communities out there. There’s a good reason for why this is a pro in the case of Scikit: solving issues.

Anyone who’s using some type of framework, especially an AI framework, will at some point stumble upon something that is hard to solve or answer. In such a situation, the community could be your best help. You just have to take a look through the forums and you’ll probably find your solution. But even if you don’t, there’s always the possibility to open a new thread and ask yourself. You should get an answer pretty quickly.

If something goes wrong and you don’t find the answer, there’s another resource that might help you: the detailed documentation that comes with Scikit.

Besides these 2 pros, Scikit is also fast and assures high performance. The API is clean but complex and efficient for all types of needs.

8. Azure ML Studio

This one is a little bit different from the other items on this list. That is because, this is a tool that can be purchased as well, but it has a free version too. It functions differently too. In what way? Let’s see!

Basically, this is a tool that works interactively to create predictive analytics. So, it is machine learning, by predicting an outcome based on the data it receives. It works like a drag-and-drop tool, where the user can create models, that will predict an outcome based on what the program receives as input data.

Azure has a built-in library of AI algorithms that can enhance the predictions a user creates. These algorithms will help the created program to come up with more accurate results based on the data it receives.

A great feature that a lot of people seem to like about this tool, is the fact that it works in the cloud. This means that there is no need to install any software on your desktop or PC. You can utilize it wherever you have a laptop/PC at hand with an internet connection.

9. Accord.NET

Accord.NET is a framework used for image processing and machine learning. It is originally coded in C#, which is a good thing, considering that C# is widely used by many applications and software. The framework is made up of multiple libraries used for scientific computing, like machine learning.

The part that supports machine learning comes with the following features:

  • Support for vector machines
  • Gaussian mixture models as well as algorithms
  • Use of K-means
  • Decision Trees and Naïve Bayesian models

I mentioned that Accord.NET is good at handling images. This is area includes the possibility to work with face detection and processing objects which will be transformed into image streams. The same is true for audio. It can process audio signals by transforming and filtering them for machine learning programs and statistical applications.

10. Spark MLlib

This framework also works as a library like many other tools on our list. It consists of a multitude of algorithms that can be used for classification, clustering, regression, and collaborative filtering. The most liked aspects of MLlib are its simplicity and easy integration with other tools.

Due to the fact that other apps are also compatible with MLlib, users can take care of certain operations faster and more efficiently.

For a lot of time now, data scientists have used Python and R (programming language) for handling certain aspects of their work that are strictly related to machine learning. But unfortunately, even these languages have their limitations. That is why MLlib comes with a powerful engine that aims to solve the problems that Python and R can’t. And this is done in an interactive way, on a bigger scale than with simply using Python.

Something great about this framework is the possibility to choose which language you want to use when working with the tool. You can choose between Python, R, Scala, and Java.

11. Amazon Machine Learning (AWS for short)

This is the last framework we’ll mention in this article. Being last doesn’t mean that it’s the worst, not by far. That is because AWS is great at handling many processes that work with machine learning.

For example, using this tool, you can add ‘’intelligence’’ to all types of applications. This means, that you can configure applications to support speech and language analysis, chatbot functions and computer vision.

This tool is also efficient because you can use it in sync with other AI frameworks, such as TensorFlow and Caffe. This can widen up the working area for AWS, quite a bit. You can use the functions from TensorFlow and Caffe, together with AWS, thus bringing you an even more powerful machine learning environment.

AWS has built-in a tool called Amazon Sagemaker. This helps the users build their machine learning models more efficiently and deploying them too. If you plan on using Amazon Machine Learning, then Sagemaker is a must along with the framework.


In this article, we’ve seen what machine learning is and how it’s used. We’ve also refreshed what a framework is in general, so you’d have a better understanding of the article at hand.

The main part of the article was the list of 10+ AI frameworks for machine learning. Now, you will surely be able to research the one that fits your needs.

To pick the framework that is best for you, just think about the goals you want to achieve with a certain tool. This way, you’ll know which framework is best suited to achieve your specific goals.

Written by Mark Bynum

Mark Bynum is tech writer and WordPress enthusiast from Atlanta. He loves playing with latest tech gadgets and testing various products and services. When he is AFK, you can find him hiking somewhere in the woods.

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