Published by Coherent Market Insights
Posted on September 24, 2021
3 min readLast updated: February 2, 2026
The deep learning market is projected to grow significantly in North America, with healthcare adoption driving a CAGR of 25.8% from 2020 to 2027.
United States/WA: The global deep learning market was valued at US$ 5.6 Bn in 2019 and is expected to reach US$ 31.3 Bn by 2027 at a CAGR of 25.8% between 2020 and 2027.
Report Pages:[150 Pages]
The Competitive Area of the Deep Learning Market is Defined by Key Players Like:-NVIDIA Corporation, Intel Corporation, Xilinx, Micron Technology, Inc., Qualcomm Technologies, Inc., IBM Corporation, Google Inc., Microsoft, Facebook, Inc., Samsung Electronics Co., Ltd., Sensory Inc., Pathmind, Inc., Baidu Inc, Nuance Communications, Cisco Systems, Inc., Apple, Inc., and Wipro Limited.
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, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.
Deep learning is usually described as an experiment-driven field under continuous criticizes of lacking theoretical foundations. This problem has been partially fixed by a large volume of literature which has so far not been well organized. The literature is categorized into six groups: complexity and capacity-based approaches for analyzing the generalizability of deep learning; stochastic differential equations and their dynamic systems for modeling stochastic gradient descent and its variants, which characterize the optimization and generalization of deep learning, partially inspired by Bayesian inference;
The geometrical structures of the loss landscape that drives the trajectories of the dynamic systems; the roles of over-parameterization of deep neural networks from both positive and negative perspectives; theoretical foundations of several special structures in network architectures; and the increasingly intensive concerns in ethics and security and their relationships with generalizability. In deep learning, a computer algorithm learns to perform classification tasks directly on complex data in the form of images, text, or sound. These algorithms can accomplish state-of-the-art (SOTA) accuracy, and even sometimes surpassing human-level performance. They are trained with a large set of labeled data and neural network architectures, involving many layers.
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The post [PDF] Deep Learning Market to Witness Significant Growth in North America with Increasing Adoption of DL in Healthcare appeared first on Gatorledger.
The article discusses the projected growth of the deep learning market in North America, particularly in healthcare.
Key players include NVIDIA, Intel, Google, Microsoft, and IBM.
The deep learning market is expected to reach $31.3 billion by 2027.
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