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US Deep Learning Market Size 2023-2027

The US Deep Learning Market size is estimated to increase by USD 3.31 billion and grow at a CAGR of 29.19% between 2022 and 2027. The growth of the market depends on several factors, including industry-specific solutions, increased focus on neuroscience-based deep learning and increasing entry of startups. Deep learning is a subfield of artificial intelligence (AI) and machine learning that focuses on the development and training of neural networks, particularly deep neural networks, to perform tasks that traditionally require human intelligence. Deep learning has a wide range of applications, including computer vision (e.g., object detection and image segmentation), natural language processing (e.g., machine translation and sentiment analysis), speech recognition, recommendation systems, autonomous vehicles, and more.

What will be the size of the Market During the Forecast Period?

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Market Segmentation

This market report extensively covers market segmentation by application (image recognition, voice recognition, video surveillance and diagnostics, and data mining), type (software, services, and hardware), and end-user (security, automotive, healthcare, retail and commerce, and others). It also includes an in-depth analysis of drivers, trends, and challenges. Furthermore, the report includes historic market data from 2017 to 2021.

By Application Segment

The market share growth by image recognition segment will be significant during the forecast period. Image recognition, a subset of computer vision, involves the use of artificial intelligence (AI) and machine learning algorithms to analyze and interpret visual data from images and videos. Image recognition is used in applications like visual search, product recommendations, and inventory management. End-users can take photographs of products to find similar items, making online shopping more convenient.

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The image recognition segment was the largest and was valued at USD 244.06 million in 2017. In the automotive industry, image recognition is essential in advanced driver assistance systems (ADAS) and autonomous vehicles, as it helps in identifying pedestrians, other vehicles, road signs, and lane markings. Deep learning, particularly convolutional neural networks (CNNs), has proven to be exceptionally effective at solving image recognition and computer vision problems. The growing demand for image recognition solutions across different industries leads to increased investments in deep learning research and development, fostering innovation and the creation of specialized solutions, which will boost the growth of the deep learning market in US during the forecast period.

By Type Segment

Deep learning software refers to the category of computer programs and frameworks that are designed to facilitate the development, training, and deployment of deep neural networks for artificial intelligence (AI) and machine learning tasks. The rising demand for deep learning software has led to a competitive landscape with numerous software providers, open-source frameworks, and cloud-based AI platforms offering deep learning solutions. This competition drives further innovation and accessibility, making it easier for organizations to integrate deep learning solutions into their operations and products, which will have a positive impact on the growth of the deep learning market in US during the forecast period.

Market Dynamics and Customer Landscape

In the realm of artificial intelligence (AI) and machine learning, the United States is witnessing a profound shift propelled by several pivotal factors. The landscape is shaped by the declining hardware cost, enabling broader accessibility and adoption of cutting-edge technologies like transformers and sophisticated deep neural network architectures. As infrastructure and storage costs decrease, the scalability of AI solutions becomes more feasible, fostering the proliferation of connected devices and enhancing the capabilities of automation. This revolution extends to diverse applications, including analyzing human behavior and processing human brain cells-generated information across various formats like photos, text, and audio. The evolution is characterized by efficient classification tasks and enhanced performance through advanced techniques such as recurrent neural networks (RNNs). Amidst this transformation, a focus on security and operational costs remains paramount, especially in sectors like education institutes, where AI is revolutionizing data analysis and driving innovation.

Key Market Driver

Industry-specific solutions are notably driving market growth. Deep learning has been instrumental in developing industry-specific solutions across various end-user sectors. Its

Note:- Only publicly available data can be worked upon

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The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four learning rates, six minibatch sizes, four levels of label noise, and fourteen levels of L1 and L2 regularization each. Multiple repetitions (typically 30, sometimes 10) of each combination of hyperparameters were preformed, and statistics including training and test loss (using a 80% / 20% shuffled train-test split) are recorded at the end of each training epoch. In total, this dataset covers 178 thousand distinct hyperparameter settings ("experiments"), 3.55 million individual training runs (an average of 20 repetitions of each experiments), and a total of 13.3 billion training epochs (three thousand epochs were covered by most runs). Accumulating this dataset consumed 5,448.4 CPU core-years, 17.8 GPU-years, and 111.2 node-years.

The global deep learning market size was estimated at USD 69.9 billion in 2023 and is expected to hit around USD 1,185.53 billion by 2033 with a CAGR of 32.57%.

This work applied remote sensing techniques based on unmanned aerial vehicles (UAVs) and deep learning (DL) to detect WLD in sugarcane fields at the Gal-Oya Plantation, Sri Lanka. The established methodology to detect WLD consists of UAV red, green, and blue (RGB) image acquisition, the pre-processing of the dataset, labelling, DL model tuning, and prediction.

Acknowledgements:

Narmilan Amarasingam conducted the UAV flight mission, and analysis and prepared the manuscript for final submission as a corresponding author.
Felipe Gonzalez, Kevin Powell, and Juan Sandino provided overall supervision and contributed to the writing and editing.
Surantha provided the technical guidance to conduct the UAV flight mission and research design and provided feedback on the draft manuscript.

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The goal of this dataset is to better undertand how open source machine learning projects evolve. Data collection date: early May 2018. Source: GitHub user interface and API. Contains original research.

Presentation

Columns

• name - name of the project.
• alignment - either corporate, academia or indie. Corporate projects are being developed by professional engineers, typically have a dedicated development team and trying to solve specific problems. Academical projects usually mention publications, they help to research. Independent projects are often a hobby.
• company - name of the company if the alignment is corporate.
• forecast - expected middle-term evolution of the project. 1 means positive, 0 means negative (stagnation) and -1 means factual death.
• year - when the project was created. Defaults to the GitHub repository creation date but can be earlier - this is a subject of manual adjustments.
• code of conduct - whether the project has a code of conduct.
• contributing - whether the project has a contributions guide.
• stars - number of stargazers on GitHub.
• issues - number of issues on GitHub, either open or closed.
• contributors - number of contributors as reported by GitHub.
• core - estimation of the core team aka "bus factor".
• team - number of people which commit to a project regularly.
• commits - number of commits in the project.
• team / all - ratio of the number of commits by the dedicated development team to the overall number of contributions. Indicates roughly which part of the project is own by the internal developers.
• link - URL of the project.
• language - API language. multi means several languages.
• implementation - the language which was mainly used for implementing the project.
• license - license of the project.

Contributing

Feel free to correct any mistakes or append other open machine learning projects.

These are saggital brain images from the ABIDE2 dataset. The data are resampled into 50-by-50 pixel images. Diagnosis labels (autism yes/no) as well as subject identifiers in the ABIDE2 dataset are also provided. See also https://figshare.com/articles/dataset/Lightly_processed_ABIDE_II_statistics/16959148

Emotion Prediction with Quantum5 Neural Network AI Machine Learning - By Emirhan BULUT

V1

I have created an artificial intelligence software that can make an emotion prediction based on the text you have written using the Semi Supervised Learning method and the RC algorithm. I used very simple codes and it was a software that focused on solving the problem. I aim to create the 2nd version of the software using RNN (Recurrent Neural Network). I hope I was able to create an example for you to use in your thesis and projects.

V2

I decided to apply a technique that I had developed in the emotion dataset that I had used Semi-Supervised learning in Machine Learning methods before. This technique is produced according to Quantum5 laws. I developed a smart artificial intelligence software that can predict emotion with Quantum5 neuronal networks. I share this software with all humanity as open source on Kaggle. It is my first open source project in NLP system with Quantum technology. Developing the NLP system with Quantum technology is very exciting!

Happy learning!

Emirhan BULUT

Head of AI and AI Inventor

Emirhan BULUT. (2022). Emotion Prediction with Quantum5 Neural Network AI [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DS/2129637

The coding language used:

Python 3.9.8

Libraries Used:

Keras

Tensorflow

NumPy

Pandas

Scikit-learn (SKLEARN)

https://raw.githubusercontent.com/emirhanai/Emotion-Prediction-with-Semi-Supervised-Learning-of-Machine-Learning-Software-with-RC-Algorithm---By/main/Quantum%205.png" alt="Emotion Prediction with Quantum5 Neural Network on AI - Emirhan BULUT">

https://raw.githubusercontent.com/emirhanai/Emotion-Prediction-with-Semi-Supervised-Learning-of-Machine-Learning-Software-with-RC-Algorithm---By/main/Emotion%20Prediction%20with%20Semi%20Supervised%20Learning%20of%20Machine%20Learning%20Software%20with%20RC%20Algorithm%20-%20By%20Emirhan%20BULUT.png" alt="Emotion Prediction with Semi Supervised Learning of Machine Learning Software with RC Algorithm - Emirhan BULUT">

Developer Information:

Name-Surname: Emirhan BULUT

Contact (Email) : emirhan@isap.solutions

LinkedIn : https://www.linkedin.com/in/artificialintelligencebulut/

Kaggle: https://www.kaggle.com/emirhanai

Official Website: https://www.emirhanbulut.com.tr

Deep Learning Hard (DL-HARD) is an annotated dataset designed to more effectively evaluate neural ranking models on complex topics. It builds on TREC Deep Learning (DL) questions extensively annotated with query intent categories, answer types, wikified entities, topic categories, and result type metadata from a leading web search engine.

DL-HARD contains 50 queries from the official 2019/2020 evaluation benchmark, half of which are newly and independently assessed. Overall, DL-HARD is a new resource that promotes research on neural ranking methods by focusing on challenging and complex queries.

Over 10% of the world's population now suffers from chronic kidney disease (CKD), and millions die yearly. To extend the lives of those suffering and lower the cost of therapy, CKD should be detected early. Building such a multimedia-driven model is necessary to detect the illness effectively and accurately before it worsens the situation. It is challenging for doctors to identify the various conditions connected to CKD early to prevent the condition. For CKD early detection and prediction, this study introduces a novel hybrid deep learning network model (HDLNet). A deep learning-based technique called the Deep Separable Convolution Neural Network (DSCNN) has been suggested in this research for the early detection of CKD. More processing attributes of characteristics chosen to indicate a kidney issue are extracted by the Capsule Network (CapsNet). Using the Aquila Optimisation Algorithm (AO) method, the pertinent characteristics are selected to speed up the categorization process. The necessary features improve classification effectiveness while needing less computational effort. The DSCNN technique is optimized to diagnose kidney illness as CKD or non-CKD using the Sooty Tern Optimization Algorithm (STOA). The CKD dataset, found in the UCI machine learning repository, is then used to test the dataset. Accuracy, sensitivity, MCC, PPV, FPR, FNR, and specificity are the performance metrics for the suggested CKD classification approach. Additional experimental findings demonstrate that the suggested method produces a better categorization of CKD than the present state-of-the-art method.

The benchmarking datasets used for deepBlink. The npz files contain train/valid/test splits inside and can be used directly. The files belong to the following challenges / classes:- ISBI Particle tracking challenge: microtubule, vesicle, receptor- Custom synthetic (based on http://smal.ws): particle- Custom fixed cell: smfish- Custom live cell: suntagThe csv files are to determine which image in the test splits correspond to which original image, SNR, and density.

We have an in-house team of Data Scientists & Data Engineers along with sophisticated data labeling, data pre-processing, and data wrangling tools to speed up the process of data management and ML model development. We have an AI-enabled platform "ADVIT", the most advanced Deep Learning (DL) platform to create, manage high-quality training data and DL models all in one place. ADVIT simplifies the working of your DL Application development.

This is a dataset prepared and intended as a data source for development of stress analysis methods based on machine learning. The dataset is based on finite element (FEM/FEA) stress analyses of generated mechanical structures using PyCalculix Python API. The dataset contains more than 270,794 pairs of stress analyses images (von Mises stress) of randomly generated 2D structures with predefined thickness and material properties. All the structures are fixed at their bottom edges and loaded with gravity force only. See PREVIEW directory with some examples.

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks.

Trained models for four deep learning based segmentation pipelines are included here. These include Unet models for the Plantseg and the Unet+Watershed and Cellpose pipelines and a MaskRCNN model for the MRCNN+Watershed pipeline.

This dataset contains adversarial attacks on Deep Learning (DL) when it is employed for the classification of wireless modulated communication signals. The attack is executed with an obfuscating waveform that is embedded in the transmitted signal in such a way that prevents the extraction of clean data for training from a wireless eavesdropper. At the same time it allows a legitimate receiver (LRx) to demodulate the data. The scheme works for both single carrier and multi-carrier orthogonal frequency division multiplexing (OFDM) waveforms and can be implemented as part of frame-based wireless protocols.The related paper that we ask to be cited if you use this dataset is by D. Varkatzas and A. Argyriou that appears in IEEE MILCOM 2023: Limitations of Deep Learning for Modulation Classification of Obfuscated Wireless Signals.

Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in the presence of highly echogenic structures, the resulting photoacoustic wave creates a reflection artifact that may appear as a true signal. We propose to use deep learning techniques to identify these type of noise artifacts for removal in experimental photoacoustic data. To achieve this goal, a convolutional neural network (CNN) was first trained to locate and classify sources and artifacts in pre-beamformed data simulated with k-Wave. Simulations initially contained one source and one artifact with various medium sound speeds and 2D target locations. Based on 3,468 test images, we achieved a 100% success rate in classifying both sources and artifacts. After adding noise to assess potential performance in more realistic imaging environments, we achieved at least 98% success rates for channel signal-to-noise ratios (SNRs) of -9dB or greater, with a severe decrease in performance below -21dB channel SNR. We then explored training with multiple sources and two types of acoustic receivers and achieved similar success with detecting point sources. Networks trained with simulated data were then transferred to experimental waterbath and phantom data with 100% and 96.67% source classification accuracy, respectively (particularly when networks were tested at depths that were included during training). The corresponding mean ± one standard deviation of the point source location error was 0.40 ± 0.22 mm and 0.38 ± 0.25 mm for waterbath and phantom experimental data, respectively, which provides some indication of the resolution limits of our new CNN-based imaging system. We finally show that the CNN- based information can be displayed in a novel artifact-free image format, enabling us to effectively remove reflection artifacts from photoacoustic images, which is not possible with traditional geometry-based beamforming.

This database contains images used for the semantic segmentation of landslide scars from a fully convolutional neural network U-Net.

1. Training dataset: it contains 230 GeoTIFF 8 bits images and associated PNG masks (scars indicated in white and background in black color).

2. Validation dataset: it contains 35 GeoTIFF 8 bits images and associated PNG masks used for U-Net validation step.

3. Test dataset: it contains 10 GeoTIFF 8 bits images and associated PNG masks for testing.

Also, the "SHAPEFILES_LANDSLIDES.rar" file contains the vector layers of the masked images in .shp format.