Thursday, December 8, 2022

Top Deep Learning Providers in 2022

The capability to recognize patterns and predict outcomes based on past data through artificial intelligence (AI) — including deep learning (DL) within machine learning (ML) — has taken computing to the next level.

Machine learning (ML) is why an e-commerce company can highlight the products you are most likely to need based on your buying behavior or how a streaming provider comes up with the most compelling content suggestions based on your watch history. 

Although systems supporting AI applications are considered smart, most don’t learn independently and often rely on human programming. For example, data scientists have to prepare inputs and select variables used in predictive analytics.

Deep learning can automatically do this without human intervention, as it is meant to learn and improve by itself through analyzing algorithms. Deep learning, with the help of artificial neural networks, tries to imitate how humans think and learn.

Deep learning has emerged as an influential piece of digital technology that enterprises leverage to optimize their business models and predict the best possible outcomes.

See below to learn all about deep learning technology and the top deep learning providers in the market:

Choosing the right deep learning providers

Top deep learning providers

PyTorch

PyTorch or Torch is a Lua-based deep learning and scientific computing framework that presents broad support for machine learning algorithms. It is used widely among enterprise leaders, like Google, IBM, and Walmart.

Torch uses CUDA and C/C++ libraries to process and scale the building model production and flexibility. Contrary to Torch, PyTorch runs on Python, which means anyone who can work with Python can build their deep learning models.

Lately, PyTorch is being increasingly adopted and gaining more recognition as a highly competent deep learning framework. PyTorch is a port to the Torch deep learning framework employed to create deep neural networks and perform complex tensor computations. PyTorch’s architectural attributes make the deep modeling process more simplified and transparent than Torch. 

Key differentiators

  • Expedites design process with rapid prototyping
  • Supports multiple GPUs and implementation of parallel programs on multiple GPUs
  • Can exchange data with external libraries
  • Simplified user interface

TensorFlow

Developed by Google, TensorFlow is one of the most popular end-to-end open-source deep learning frameworks that work with desktop and mobile. TensorFlow supports languages like Python, C++, and R to build deep learning models. 

TensorFlow is supported by Google and has a Python-based framework, making it one of the most preferred deep learning frameworks. Plus, it comes with additional training resources and walk-throughs for learning.

TensorFlow can leverage natural language processing to power tools like Google Translate and help with speech, image, and handwriting recognition, summarization, text classification, and forecasting. TensorBoard is TensorFlow’s visualization toolkit that provides comprehensive data visualization and measurements during machine learning workflows. TensorFlow Serving is another TensorFlow tool used to quickly deploy new algorithms and experiments while maintaining the old server architecture and APIs. It also integrates different TensorFlow models and remains extendable to accommodate other models and data types.

Key differentiators

  • Supports computation on multiple GPUs
  • Comprehensive graph visualization on TensorBoard
  • Abundant reference and community support

Microsoft Cognitive Toolkit

Microsoft Cognitive Toolkit, previously known as CNTK, is an open-source deep learning framework that trains deep learning models. It is known for its training modules and various model types across servers. It conducts systematic convolution neural networks and streamlined training for data, including images, speech, and text.

With Microsoft cognitive toolkit, implementing reinforcement learning models or generative adversarial networks can be done. Compared to other toolkits, the Microsoft cognitive toolkit is known for empowering higher performance and scalability while operating on multiple machines.

Due to the fine granularity of building blocks, users don’t have to use low-level language while making complex, new layer types. Both RNN and CNN neural models are accommodated with the Microsoft cognitive toolkit. Hence, it is highly proficient in resolving writing, image, and speech recognition issues. 

The Microsoft Cognitive Toolkit supports both RNN and CNN type neural models and is thus capable of handling image, handwriting, and speech recognition problems. For now, this framework’s capability on mobile is fairly limited due to the lack of support with ARM architecture. 

Key differentiators

  • Provides support with interfaces like Python, C++, and Command Line
  • High efficiency and scalability for multiple machines
  • Works with complex image, handwriting, and speech recognition 
  • Supports RNN and CNN neural networks

Deeplearning4j

Deeplearning4j is a deep learning library for the Java Virtual Machine (JVM). This deep learning ecosystem is developed in Java and effectively supports JVM languages, such as Scala, Clojure, and Kotlin. Deeplearning4j framework supports parallel training and micro-service architecture adaptation run by linking distributed CPUs and GPUs Eclipse. Deeplearning4j is widely adopted as a distributed, commercial, enterprise-focused, deep learning platform. Deeplearning4j enables deep network support with the help of RBM, DBN, convolution neural networks, recursive neural Tensor networks, recurrent neural networks, and long short-term memory.

This framework runs on Java, and it is much more efficient than Python for certain applications. DL4J is as fast as the Caffe frame for image recognition when using multiple GPUs. It also shows extreme potential in text mining, natural language processing, fraud detection, and speech tagging.

As the core programming language of this deep learning framework, Java unlocks many features and functionalities for its users and serves as an effective way to deploy deep learning models to production.

Key differentiators

  • Executes deep learning processes by leveraging the entire Java ecosystem
  • Capable of processing massive amounts of data in less time
  • Involves multi-threaded as well as single-threaded deep learning
  • Framework can be implement in addition to Hadoop and Spark

MXNet

MXNet is a deep learning framework that supports programming languages like Python, R, Scala, C++, and Julia. It was designed specifically to meet high efficiency, productivity, and adaptability requirements. MXNet is Amazon’s deep learning framework of choice, also used in their reference library. 

One of MXNet’s most notable features is its functions in distributed learning. It offers efficient, nearly linear scaling and uses hardware to its fullest extent. MXNet ecosystem also enables the user to code in a range of programming languages. Developers can train their deep learning models based on whichever language they are proficient in, without needing additional skills or expertise.

MXNet can scale and work with several GPUs as the back end is written in C++ and CUDA. It also supports RNN, CNN, and long short-term memory networks. MXNet deep learning framework use cases include imaging, speech recognition, forecasting, and natural language processing.

Key differentiators

  • Hybrid programming accommodates both imperative and symbolic programming
  • Efficient distributed training
  • Supports several different programming languages for added flexibility 
  • Excellent scalability with near linearity on GPU clusters

ONNX

Developed by Microsoft and Facebook as an open-source deep learning ecosystem, Open Neural Network Exchange (ONNX) represents a common file format, so AI developers can use models with different frameworks, tools, runtimes, and compilers. It enables developers to switch between platforms. 

ONNX comes with an emphasis on in-built operators, standard data types, and an expandable computation graph model. These models are natively supported on Caffe2, MXNet, Microsoft Cognitive Toolkit, and PyTorch. ONNX also offers converters for other machine learning frameworks, such as CoreML, TensorFlow, Sci-kit Learn, and Keras.

ONNX is a dependable tool that prevents framework lock-in by making hardware optimization easy and allowing model sharing. Users can convert their pre-trained model into a file and merge it with their applications. ONNX has gained recognition due to its adaptable nature and interoperability.

Key differentiators

  • Element of interoperability and flexibility
  • Delivers compatible run times and libraries
  • Freedom to use preferred DL framework inference engine of choice
  • Optimizes hardware performance

Deep learning features

Supervised, semi-supervised, and unsupervised learning

Supervised learning is the simplest learning method, as it assumes the labels of each given image and makes the learning process for the network easier. Semi-supervised learning is used to train an initial model by employing a few labels and then repeatedly applying them to a greater number of unlabeled data. Unsupervised learning uses algorithms to identify patterns in a dataset, including data points that are not classified or labeled.

Neurons

Deep learning acts as a comprehensive neural network. Hence, it possesses a large number of interconnected neurons organized in layers. The input layer receives information. Several hidden layers process the information, and the output layer provides valuable results. 

Resource dependency

Deep learning algorithms depend more on high-end machines as compared to ML applications. They require advanced GPUs to process heavy workloads. A huge amount of data, structured or unstructured, can be processed with deep learning, and the performance also improves as more data is fed. 

Hyperparameter optimization 

Hyperparameters, like batch size, learning rate, momentum, and several epochs or layers, need to be tuned well for better model accuracy, since they connect layer prediction and final predicted output. Over-fitting and under-fitting can be well adjusted in deep learning using hyperparameters.

Benefits of deep learning

  • Deep learning algorithms can automatically generate new features in the training dataset without human intervention. It can perform intricate tasks without extensive feature engineering, allowing faster product rollouts with superior accuracy.
  • Deep learning also works well with unstructured data. This feature is useful, since the majority of business data is unstructured. With traditional ML algorithms, the wealth of information in unstructured data often goes untapped.
  • Learning complex features and conducting intensive computational tasks become more efficient with multiple layers of deep neural networks. Due to its ability to learn from errors, deep learning can carry out perceptive tasks. It verifies the accuracy of its predictions and making necessary adjustments.
  • A deep learning model takes several days to learn the model’s parameters. Coupled with parallel algorithms, training can be distributed across multiple systems. It can complete the training much faster depending on the volume of training datasets and GPU capacities.
  • While training can be cost-intensive, it helps businesses reduce expenditure by preventing inaccurate predictions or product defects. Manufacturing, retail, and health care industries can leverage deep learning algorithms to reduce error margins dramatically. 
  • Combining deep learning and data science, more effective processing models can be achieved as they ensure more reliable and concise analysis outcomes. Deep learning has many applications in analytics and forecasting, such as marketing and sales, HR, and finance.
  • Deep learning is scalable as it can process large amounts of data and perform extensive computation processes cost-effectively. This quality directly impacts productivity, adaptability, and portability. 

Deep learning use cases

Self-driving cars

Self-driving cars use deep learning to analyze data to function in different terrains, like mountains, bridges, urban and rural roads. This data can come from sensors, public cameras, and satellite images that will help test and implement self-driving cars. Deep learning systems can ensure that self-driving cars handle in all scenarios through training.

Health care

Deep learning and GPU processors can provide better image analysis and diagnosis for patients. Artificial intelligence can also help develop new, more effective medications and cures for life-threatening diseases and expedite treatment as well.

Stock analysis

Deep learning makes it more convenient to find or predict the trends for a particular stock and whether it will be bullish or bearish. Analysts can consider multiple factors, including several transactions, buyers, sellers, and the closing balance of the previous day, while training the deep learning algorithm. Qualitative equity analysts can train deep learning layers using P/E ratio, return on equity or assets or capital employed, and dividend.

News analysis

Deep learning algorithms can help detect specific news and its origin and determine whether it is fake. For instance, if deep learning is allowed to mainstream social and local media during elections, it can also help predict election results.

Fraud detection

As hacking and cybercrime have become more sophisticated, deep learning can serve as an adaptive model to counter cyberattacks. Deep learning can learn to detect different types of fraudulent transactions on the web and track their origin, frequency, and hotspots by taking factors, like IP addresses and router and device information.

Image recognition

Image recognition through deep learning can ultimately help the AI system classify different variables and points of consideration based on their appearance. A practical example of image recognition can be seen in face recognition for surveillance. 

What to look for in a deep learning provider

Deep Learning unlocks several practical use cases of machine learning and artificial intelligence technologies. Deep learning has the power to break down tasks in the most efficient manner and assist machine applications with superior intelligence and adaptability. 

To know which one of the deep learning frameworks above best meets your requirements depends on several factors, some of which are as follows. 

  • Architecture and functional attributes
  • Speed and processing capacity 
  • Debugging considerations
  • Level of APIs
  • Integration with existing systems

It also depends a lot on your level of expertise, so one should consider implementing a beginner-friendly DL mechanism in the initial stages. Python-based frameworks turn out to be more straightforward for beginners as well.

But if you happen to be experienced, there is a whole set of considerations, such as integration with applications and platforms, resource requirements, usability, availability and coherence of the training models. A rigorous evaluation system, constant trial and error, and an open mind will help you get through to your ideal deep learning framework.

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