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Deep Learning -What Kind of Problems Does Deep Learning Solve?

Deep learning technologies are rapidly Growing advancing in a variety of machine learning areas, including natural language processing, reinforcement learning, ML frameworks, and etc. Industrial equipment is becoming more useful and smarter in predictive support and condition monitoring. Deep learning finds a More type of applications, such as voice control in consumer electronics, driverless cars, and more. The key ability to automate predictive analytics eventually leads to increased use of deep learning. Deep learning is a sub-branch of AI and ML that follow the workings of the human brain for processing the datasets and making efficient decision making. Practical examples of deep learning are Virtual assistants, vision for driverless cars, money laundering, face recognition and many more.

Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.

Key Prominent Players in The Deep Learning Market:

Advanced Micro Devices, ARM Ltd, Clarifai, Entilic, Google, HyperVerge, IBM, Intel, Microsoft, NVIDIA

Deep Learning market is projected to reach USD $ 110 billion by 2028, registering a CAGR of 38.25%.

How deep learning works

Computer programs that use deep learning go through much the same process as the toddler learning to identify the dog. Each algorithm in the hierarchy applies a nonlinear transformation to its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep.

In traditional machine learning, the learning process is supervised, and the programmer has to be extremely specific when telling the computer what types of things it should be looking for to decide if an image contains a dog or does not contain a dog. This is a laborious process called feature extraction, and the computer’s success rate depends entirely upon the programmer’s ability to accurately define a feature set for dog. The advantage of deep learning is the program builds the feature set by itself without supervision. Unsupervised learning is not only faster, but it is usually more accurate.

Deep Learning Market Segment by Types: – Software, Hardware, Service

Deep Learning Market Segment by End user: –

Aerospace &   Defense, Automotive, Manufacturing, Healthcare, Others

Deep Learning Market Segment by Applications 2028

Signal Recognition, Image Recognition, Others

Limitations and challenges include In Deep learning:

  • Deep learning requires large amounts of data. Furthermore, the more powerful and accurate models will need more parameters, which, in turn, require more data.
  • Once trained, deep learning models become inflexible and cannot handle multitasking. They can deliver efficient and accurate solutions but only to one specific problem. Even solving a similar problem would require retraining the system.
  • Any application that requires reasoning — such as programming or applying the scientific method — long-term planning and algorithmlike data manipulation is completely beyond what current deep learning techniques can do, even with large data.

Industry experts have identified key factors influencing the pace of development of the deep learning industry, including various opportunities and gaps. A thorough analysis of the Deep Learning market for the growth trends of each category makes the whole study interesting. The facts and figures provided in this report are based on material types and end-user consumption and demand. Market value and volume are obtained by taking a bottom-up approach and taking into account general price trends

Deep Learning Report provides insights into the following queries:

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For many tasks, for recognizing and generating images, speech and language, and in combination with reinforcement learning to match human-level performance in games ranging from the ancient, such as Go, to the modern, such as Dota 2 and Quake III.

This systems are a foundation of modern online services. Such systems are used by Amazon to understand what you say — both your speech and the language you use — to the Alexa virtual assistant or by Google to translate text when you visit a foreign-language website.

Every Google search uses multiple machine-learning systems, to understand the language in your query through to personalizing your results, so fishing enthusiasts searching for “bass” aren’t inundated with results about guitars.

But beyond these very visible manifestations of machine and deep learning, such systems are starting to find a use in just about every industry. These uses include: computer vision for driverless cars, drones and delivery robots; speech and language recognition and synthesis for chatbots and service robots; facial recognition for surveillance in countries like China; helping radiologists to pick out tumors in x-rays, aiding researchers in spotting genetic sequences related to diseases and identifying molecules that could lead to more effective drugs in healthcare; allowing for predictive maintenance on infrastructure by analyzing IoT sensor data; underpinning the computer vision that makes the cashierless Amazon Go supermarket possible, offering reasonably accurate transcription and translation of speech for business meetings — the list goes on and on.

What Are The Drawbacks Of Deep Learning?

One of the big drawbacks is the amount of data they require to train, with Facebook recently announcing it had used one billion images to achieve record-breaking performance by an image-recognition system. When the datasets are this large, training systems also require access to vast amounts of distributed computing power. This is another issue of deep learning, the cost of training. Due to the size of datasets and number of training cycles that have to be run, training often requires access to high-powered and expensive computer hardware, typically high-end GPUs or GPU arrays. Whether you’re building your own system or renting hardware from a cloud platform, neither option is likely to be cheap.

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