What is Deep Learning Tools?
Deep learning is a subset of machine learning in AI that contains networks that are equipped to learn from unsupervised data. It is inspired by the functioning of the human brain for processing information and decision-making purposes. Also, it is popularly known as deep neural learning or deep neural network. It makes use of algorithms that help in development of models that are capable of predicting better outcomes, which helps the decision making process for any business.
Deep learning applications are used in different types of industries. One of the examples is: Automated Driving. Research is in progress that makes use of deep learning to detect pedestrians, signs, and traffic lights. This will not only help in leveraging the power of artificial intelligence but also ensure that there are lesser road accidents.
How the tools does helps for?
Deep Learning tools rely on predictive modeling and statistics, which helps collect, interpret, and analyze massive amounts of data. These tools help seamlessly to detect objects, translate languages, recognize speech, and make decisions accordingly. For instance, if you create an application using deep learning tools or programs without the need for human supervision. It draws data and lays out ways by which you can interact with the file system and directory on any computer.
H20.ai is a cutting-edge end-to-end Artificial Intelligence hybrid cloud that aims at democratizing AI for everyone. The open-source leader in Artificial Intelligence is developed from the ground up by using Java as its core technology. H20.ai has been efficiently integrated with several other products such as Apache Hadoop and Spark. Further, giving an exponential rate of flexibility to users and customers across the world. The platform allows almost anyone to implement machine learning and predictive analytics to solve resilient business problems.
H20.ai makes use of an open-source framework equipped with a seamless web-based Graphic User Interface feature quite a seamless interface. The tool offers significant scalability that is ideal for real-time data scoring. The standard support for data-agnostic strengthens all file types and shared databases.
TensorFlow is a deep learning tool that was written in highly-optimized C++ and CUDA (Nvidia’s language for programming GPUs) and provides an interface to languages like Python, Java, Go. It is an open-source library that was developed by the tech giant, Google, for the smooth running of deep learning applications.
TensorFlow makes it fairly easy for beginners and even experts to create machine learning models for mobile, web, desktop, and cloud.
It is also used to create large-scale neural networks with multiple layers. If you want to solve deep learning or machine learning problems like Classification, Perception, Understanding, Discovering, Prediction and Creation, TensorFlow is the right deep learning tool for you.
Keras is a high-level neural network API that is capable of running on top of TensorFlow or Theano. It is written in Python and was developed mainly on enabling faster experimentation. Keras deep learning library allows the user for easier and faster prototyping with the use of modularity, minimalism and easy extensibility.
Keras is a deep learning tool that supports recurrent networks and convolutional networks individually as well as in the combination of the two. It also supports multi-input and multi-output training. It follows best practices for reducing cognitive load by offering consistent and simple APIs. Furthermore, it minimizes the number of user actions that are needed for common use cases and provides a clear feedback upon detection of any error.
4. Torch and PyTorch
Torch is a notable open-source scientific computing framework, machine learning library, and a significant script language based on the multi-paradigm Lua programming language. As an efficient open-source program, Torch provides numerous algorithms used in deep learning, and it uses LuaJIT as its underlying scripting language. Additionally, Torch also features C/CUDA implementation that makes use of GPU while supporting machine learning. Torch’s N-dimensional array features with several routines to transpose, slice, and index are regarded as the best-in-class. Torch works with Android, iOS, and other operating systems because of the excellent GPU support and is deeply embedded in its library.
Sadly, since 2018, no further development was done to Torch, and as bad as it sounds, the open-source machine learning library has helped bring PyTorch into existence. On the other hand, PyTorch was developed by FAIR or Facebook’s AI Research lab. The new machine learning library is generally used for NLP or Natural Language Processing and the interdisciplinary scientific study, Computer Vision. Here, the NLP application is the subset of artificial intelligence, computer science, and linguistics. In contrast, Computer Vision deals with how computers use videos and digital images to obtain a high level of understanding. PyTorch is written in C++, CUDA, and Python.
Some of the notable examples of the deep learning software built with PyTorch as the foundation include Pyro by Uber, Transformers from HuggingFace, Tesla Autopilot, Catalyst, and PyTorch Lightning. Additionally, PyTorch also features two notable features such as:
Deep neural networks that are built on algorithm differentiation systems.
Tensor computing with strong acceleration through GPU.
Theano is a Python library that is used to evaluate mathematical operations including multi-dimensional arrays. It is mainly used in building deep learning projects and is way faster on a GPU than a CPU. With its use, it is possible to attain speed that rivals self-made C implementations for problems that involve large amounts of data.
Caffe is a deep learning tool whose framework is built with expression, speed and modularity in consideration. Developed by the Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors, it is also an open source deep learning tool.
Its expressive architecture allows for application and innovation to propagate. Caffe is used in academic research, startup prototypes and large-scale industrial applications in speech, vision, and multimedia. Caffe has more performance than TensorFlow by 1.2 to 5 times, based on the internal benchmarking in Facebook.
Scikit-learn is one of the top open-source frameworks ideal for getting started with machine learning. It has high-level wrappers which enable users to play around with multiple algorithms and explore the wide range of classification, clustering, and regression models.
For the curious mind, scikit-learn can also be a great way to gain deeper insight into the models simply by unwrapping the code and following the dependencies. Scikit-learn’s documentation is highly detailed and easily readable by both beginners and experts.
Scikit-learn is great for ML solutions with a limited time and resource allotment. It is strictly machine learning-focused and has been an instrumental part of predictive solutions from popular brands over the last few years.
Catalyst is another PyTorch framework built specifically for deep learning solutions. Catalyst is research-friendly and takes care of engineering tasks such as code reusability and reproducibility, facilitating rapid experimentation.
Deep learning has always been considered as complex and Catalyst enables developers to execute deep learning models with a few lines of code. It supports some of the top deep learning models such as ranger optimizer, stochastic weight averaging, and one-cycle training.
Catalyst saves source code and environment variables to enable reproducible experiments. Some other notable features include model check pointing, callbacks, and early stopping.
9. Neural Designer
The professional application is written in C++, and it is developed by Artelnics, a start-up based out of Spain. The proprietary software tool can run on OS X, Microsoft Windows, and Linux. Neural Designer is based on Deep Learning neural networks, which also serves as a primary area for artificial intelligence research. The application discovered the unforeseeable patterns, further predicting the actual trends from the data sets via neural networks. The European Commission reportedly chose Neural Designer as a disruptive technology in 2015.
Over the years, Neural Designer has become the most to-go application on desktops to facilitate data mining. Moreover, the application utilizes neural networks in mathematical models by mimicking the human brain’s functionalities—Neural Designer aids in building computational models that act on the central nervous system. Deep architectures are embedded within the application to solve pattern recognition, function, autoencoding problems.
10. Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit is a free, open-source, easy-to-use, commercial-grade toolkit that enables users to learn deep learning systems precisely like the human brain. With Microsoft Cognitive Toolkit, data scientists could curate systems such as Convolutional Neural Network (CNN) image classifiers and feed-forward neural network series prediction systems. The Cognitive Toolkit provides an extraordinary scaling capability with optimum speed, accuracy, and top-notch quality. Users can harness the true power of artificial intelligence present within massive datasets via deep learning.
The toolkit further describes the neural networks as a sequential computational step directed through a graph. Specific applications and products from Microsoft such as Cortana, Skype, Xbox, and Bing use Microsoft Cognitive Toolkit to generate industry-level AI.
11. PyTorch Lightning
PyTorch Lightning is a wrapper on top of PyTorch, built primarily to redirect focus on research instead of on engineering or redundant tasks. It abstracts the underlying complexities of the model and common code structures so the developer can focus on multiple models in a short span.
The two strengths of PyTorch Lightning, as the name partially suggests, are speed and scale. It supports TPU integration and removes barriers to using multiple GPUs. For scale, PyTorch Lightning allows experiments to run in parallel on multiple virtual machines through grid.ai.
PyTorch Lightning has significantly less need for code because of high-level wrappers. However, that does not restrict the flexibility since the primary objective of PyTorch is to reduce the need for redundant boilerplate code. Developers can still modify and deep dive into areas that need customization.
XGBoost is a tree-based model training algorithm that uses gradient boosting to optimize performance. It is an ensemble learning technique which means several tree-based algorithms are used to achieve the optimal model sequence.
With gradient boosting, XGBoost grows the trees one after the other so that the following trees can learn from the weakness of the previous ones. It gradually moderates the weights of weak and strong learners by borrowing information from the preceding tree model.
To enhance speed XGBoost supports parallel model boosting across distributed environments such as Hadoop or MPI. XGBoost is well suited for large training datasets and combinations of numeric and categorical features.
LightGBM, like XGBoost, is also a gradient boosting algorithm that uses tree-based models. But when it comes to speed, LightGBM has an upper hand over XGBoost. LightGBM is best suited for large datasets that otherwise would consume a lot of training time with other models.
While most tree-based algorithms split the tree level or depth-wise, LightGBM comes in with the unique technique of leaf or breadth-wise splits which has proven to increase performance. Even though this tends to overfit the model, the developer can avoid the situation by tweaking the max_depth parameter.
LightGBM requires low memory space in spite of working with heavy datasets since it replaces continuous values with discrete bins. It also supports parallel learning which is again a major time saver.
CatBoost is a gradient boosting algorithm that provides best-in-class results with minimal training compared to most machine learning models. It is an open-source tool and has become a popular favorite because of its ease of use.
CatBoost cuts down preprocessing efforts since it can directly and optimally handle categorical data. It does so by generating numerical encodings and by experimenting with various combinations in the background.
Even though CatBoost offers the scope of tuning extensively with a range of multiple hyperparameters, it does not require much tuning and can produce results without overfitting the training data. It is well-suited for both low and high-volume data.
Fast.ai’s catchy tagline says it all – “making neural nets uncool again”. Fast.ai aims to make deep learning accessible across multiple languages, operating systems, and small datasets. It was developed on the idea that transfer learning is a key strength in deep learning and can cut down a huge amount of redundant engineering work.
It offers an easy-to-use high-level interface for deep learning models and also allows users to download a set of pre-trained models. Fast.ai has multiple wrappers that hide the complexities of the underlying model architecture. This allows developers to focus on data intelligence and process breakthroughs.
Fast.ai is also extremely popular for sharing their free online course, “Practical Deep Learning for Coders”, which does not demand any pre-requisite yet dives deep into deep learning concepts and illustrates how to make it easy through fast.ai.
16. PyTorch Ignite
PyTorch Ignite is a wrapper built on top of PyTorch and is quite similar to PyTorch Lightning. Both offer an abstraction of model complexities and an easy-to-use interface to expand research abilities and diminish redundant code.
Architecture-wise, there is a subtle difference between the two. While PyTorch Lightning has a standard reproducible interface, Ignite does not have any standard version.
While it cannot support highly advanced features, Ignite works well with an ecosystem of integrations to support the machine learning solution whereas Lightning supports state-of-the-art solutions, advanced features, and distributed training.
It allows for trading computation time for memory via ‘forgetful backprop’ which can be very useful for recurrent nets on very long sequences.
- Built with scalability in mind (fairly easy-to-use support for multi-GPU and multi-machine training).
- Lots of cool features, like easily writing custom layers in high-level languages
- Unlike almost all other major frameworks, it is not directly governed by a major corporation which is a healthy situation for an opensource, community-developed framework.
- TVM support, which will further improve deployment support, and allow running on a whole host of new device types
Here are a few of the top deep learning tools you should be familiar with by 2021. Nevertheless, this list is by no means complete. Although there are more deep learning tools available, these are the ones that are most frequently utilised. These deep learning technologies assist in resolving issues in the real world, and their knowledge can help you develop the necessary AI skills.