Deep learning libraries are essentially sets of functions and routines written in a given programming language. A large set of deep learning libraries can make it quite simpler for data engineers, data scientists and developers to perform tasks of any complexity without having to rewrite vast lines of code.
Artificial intelligence (AI) has been rapidly gaining popularity in the modern era. This drastic growth has been attributed to the immense technological advancements in the field.
AI was once known to be the playground for geniuses and computer geeks, but due to the major advancement and development of different frameworks and libraries, this niche has become widely known and broken barriers of IT, leading to its use by completely different individuals and businesses.
This form of machine learning has completely changed the way business is conducted nowadays. Business leaders have to keep up with the latest business trends, thus have ventured into the use of artificial intelligence to solve the day-to-day problems and greatly increase their performance.
Due to phrases like ‘big data’ companies are impressed by the amount of data collection that can be done during business processes by these tools.
Big companies involved in massive data collection, such as Amazon, Facebook, and Netflix, need these systems to not only help them collect large amounts of data but to help them make brilliant predictions to greatly increase their profits. And predictive analytics is used not only in business to gain profit but in other areas including healthcare.
According to the experts from The App Solutions, machine learning with predictive analytics can reveal all the bad components into the human’s DNA and predict potentially dangerous diseases for this human. The possibilities of machine learning are endless.
With a lot of businesses looking to join in using these advancements in order to scale up their operations, it has become essential to take up both predictive analytics and machine learning.
Artificial intelligence integrated with proper deep learning frameworks has greatly increased the overall scale of what companies can obtain and achieve within their different domains.
There have been major advancements in machine learning, and this paradigm is evolving continuously. Developers are now working on making a key shift to mobile devices in order to make their application way smarter and more intelligent.
Deep learning is literally what allows the solving of complex problems. In layman’s terms, deep learning is simply machine learning on steroids. There are a number of layers used to process its features and each layer basically extracts pieces of valuable data.
Deep learning is unsupervised and works with a set of algorithms also used in machine learning. Deep learning is associated with artificial intelligence in such a way that computers learn to obtain different kinds of knowledge through a human approach as opposed to the way a human program it to perform.
Due to deep learning being basically a major key in performing tasks of great complexity, creating and putting them into use successfully has proven to be a rather difficult task for developers, data engineers and data scientists all over the world.
In recent times, there are a lot of frameworks at anyone’s disposal that allows us to develop different tools which are able to offer a better level of simplification along with abstraction of complex challenges in programming.
Each library or framework is created differently in order to suit different purposes. In this piece, we look at 12 of the best deep learning libraries so as to give you a better view of which libraries are well suited for the challenges you may be facing:
This is ideally one of the best deep learning libraries and is being used by large companies with the likes of Twitter, Airbus, and IBM among many others adopting this framework because of its highly flexible system architecture.
The most common and well-known use of this amazing framework is definitely Google Translate. Tensorflow boasts various capabilities, such as summarization/text classification, natural language processing, handwriting/image/speech recognition, tagging, and forecasting.
This framework is available on mobile devices and PC and can support various programming languages, such as C++, R language and Python, to build wrapper libraries and deep learning models.
This a framework that works with programming languages and interfaces like C++, C, MATLAB and Python in addition to the command-line interface. This framework is popular for its transposability and speed, plus its capabilities in building convolution neural networks.
One of the major advantages of using this framework C++ library is that it has capabilities of allowing its user to access networks from a deep net repository that are initially trained and can be used in an instance.
This is a toolkit developed by Microsoft that is used in deep learning and illustrates neural networks in a series of certain computational steps using a directed graph.
This toolkit helps its users simply combine various types of neural networks and boasts capabilities for distributed training, great performance, and flexibility.
On the other hand, it bears no simply readable source code and consistently lacks in open source coding compared to VisualDeepLearning4J and distributed deep learning feature in JVM.
This framework is popularly used for training distributed deep learning networks and is capable of processing large amounts without any setbacks. This toolkit can also be integrated with Spark and Hadoop and implement algorithms into machine learning from scratch.
Because this toolkit was built with java instead of Python, the user has to build explicit classes by solely adding matrices together.
This is basically a Python-built machine learning an open-source framework that is used for data mining, visualization, and analysis. This framework is especially good and well known for regression, classification, dimensionality, preprocessing, clustering, reduction as well as the model selection, just to mention a few processes.
It is structurally built on SciPy, Matplotlib, and NumPy. Thanks to the use of Python it works much faster than R language and performs far much better.
This library is also Python-built and has functionalities of optimizing, defining, evaluating and manipulating any mathematical expressions using a computerized algebra system.
If you are a developer who is dealing with deep learning, then you have a lot of complex numerical tasks at hand. This library is well suited for these kinds of tasks, especially numerical tasks, concerning matrix operations, function definitions and symbolic variables in addition to GPU or CPU machine code compilation.
This is one of the earliest versions of deep learning libraries. This means that it is a well-built system and has to be used with other libraries often if you are looking to get a higher level of abstraction.
This is an open-source framework that is used for scientific computing and has capabilities of supporting machine learning algorithms.
Torch deep learning framework is powered by LuaJIT. This scripting language allows its users to code using C language while interacting with the framework; it also has an underlying CUDA/C implementation.
Some of its most prominent features include a powerful N-dimensional array, neural network, linear algebra routines, efficient and quick GPU support, and energy-based models. In addition to this, it supports Android and iOS for mobile devices.
There are certain minor disadvantages to using this framework as loading of data from directories often proves to be difficult to its users and the use of LuaJIT, which is a new language, makes this framework less accessible.
This platform is basically used by developers who do not possess advanced programming skills yet. If you are looking to venture into machine learning and lack professional skills, then this is the platform for you.
Of course, this platform requires its user to have basic knowledge of machine learning so as to be able to gain certain results from this platform.
This platform is cloud-based and handles the deploying of predictive models as analytics solutions. It can be used to run algorithms, create recommender systems and test machine learning models just to mention a few cases. However, the downside of this platform is its unintuitive UI and poor performance during the writing of codes.
This is an open source deep learning library created for JVM. It is popular and good at training distributed deep learning networks and processing large amounts of data without lagging.
It is flexible, since it can be integrated with Spark and Hadoop, and is capable of implementing algorithms of machine learning from scratch. Since it has been built on Java, this framework cannot rely on codebases the way other libraries do, and its development costs are often much higher.
In addition to this, because it is a Java-built framework its users have to create explicit classes on their own so as to add matrices together unlike Python that does not require its users to create explicit classes.
This is a neural network (open source) library built using Python. If you are a developer looking for a framework to help you perform easy and fast experiments, then this is the right one for you.
This framework works on recurrent neural networks and convolutional neural networks and is capable of running on both GPU and CPU. This framework is quite straightforward and easy to use for developers that are familiar with deep learning.
The platform also has its disadvantages that can be quite depressing since transcending surface-level customization might be frustrating, and the data processing tools are quite a large burden.
This is considered to be a very scalable library used for machine learning. It can be used for various languages including Scala, Python, R language, plus Java.
This library is quite efficient since it interoperates with NumPy, R and Python libraries. Apache’s Spark can be easily integrated with Hadoop workflows.
It supports machine learning algorithms including regression, clustering, and classification. This is an impressively fast and powerful library in relation to the processing of large amounts of data.
This is a framework used for deep learning in Julia, built on C++ framework similar to Caffe.
This framework can be used to train deep/shallow neural networks with efficient implementations of stochastic common layers and gradient solvers with unsupervised initial training using auto-encoders though this feature is optional.
The best features of this framework include a high-level interface, compatibility, modular architecture and portability with speed among many others.
This is a Python-built framework in deep learning that is very dynamic, powerful and intuitive when it comes to neural networks. Chainer deep learning framework was designed by the run strategy.
If we compare other types of the framework using the run strategy, this framework has other unique features, such as that the user is able to execute arbitrary control statements by modifying the networks as it runs.
This framework supports both multi-GPU and CUDA computation. Chainer is often used for machine translation, sentiment analysis, speech recognition among others. It carries this out using CNNs and RNNs.
It has become quite evident in recent years that deep learning has led to a lot of cases of practical use of artificial intelligence and machine learning.
The breaking down of tasks is by far the easiest and most efficient way of help we can get from machines that have been made possible thanks to the discovery of deep learning.
At that, which deep learning framework do you think will solve all your day-to-day business problems and maximize your profits in your business ventures? By now, you have understood quite well how the different deep learning frameworks work, and you certainly have your answer.
However, if you are just a developer or you want to get started, then Chainer or Tensorflow Python-based deep learning frameworks are the most suitable.
In case you are looking for more resources, and speed integrated with coherence, initially trained models should always be primarily considered when picking a suitable or the best deep learning framework that is ideal for your business.
So, there you go! These are twelve of the most efficient thus the best frameworks used in deep learning by data scientists, developers and data engineers all over the world. We hope this piece covered all your search queries concerning this form of machine learning. If you liked the post, feel free to leave your comment or question below.
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