#dnn framework

[itonlinetraining]

PyTorch has developed over the past few years into a well-liked and often used framework for deep neural networks (DNN) training. about Pytorch 2.0 PyTorch’s popularity is credited to its ease of use, first-rate Python integration, and imperative programming approach.To know more about PyTorch 2.0, check out the Python training course.

[drmikewatts]

Soft Computing, Volume 29, Issue 4, February 2025

1) A map-reduce algorithm to find strongly connected components of directed graphs

Author(s): Fujun Ji, Jidong Jin

Pages: 1947 - 1966

2) Complex preference analysis: a score-based evaluation strategy for ranking and comparison of the evolutionary algorithms

Author(s): Debojyoti Sarkar, Anupam Biswas

Pages: 1967 - 1980

3) Weighted rank aggregation based on ranker accuracies for feature selection

Author(s): Majid Abdolrazzagh-Nezhad, Mahdi Kherad

Pages: 1981 - 2001

4) Exploring diversity and time-aware recommendations: an LSTM-DNN model with novel bidirectional dynamic time warping algorithm

Author(s): Te Li, Liqiong Chen, Kaiwen Zhi

Pages: 2003 - 2013

5) Cyber-attack detection based on a deep chaotic invasive weed kernel optimized machine learning classifier in cloud computing

Author(s): M. Indrasena Reddy, A. P. Siva Kumar, K. Subba Reddy

Pages: 2015 - 2030

6) A novel instance density-based hybrid resampling for imbalanced classification problems

Author(s): You-Jin Park, Chung-Kang Ma

Pages: 2031 - 2045

7) A bi-objective multi-warehouse multi-period order picking system under uncertainty: a benders decomposition approach

Author(s): Fatemeh Nikkhoo, Ali Husseinzadeh Kashan, Bakhtiar Ostadi

Pages: 2047 - 2074

8) A two-population artificial tree algorithm based on adaptive updating strategy for dominant populations

Author(s): Yaping Xiao, Linfeng Niu, Qiqi Li

Pages: 2075 - 2106

9) Multi-ant colony algorithm based on the Stackelberg game and incremental learning

Author(s): Qihuan Wu, Xiaoming You, Sheng Liu

Pages: 2107 - 2128

10) Review of quantum algorithms for medicine, finance and logistics

Author(s): Alessia Ciacco, Francesca Guerriero, Giusy Macrina

Pages: 2129 - 2170

11) A novel attention based deep learning model for software defect prediction with bidirectional word embedding system

Author(s): M. Chitra Devi, T. Dhiliphan Rajkumar

Pages: 2171 - 2188

12) Modeling and analysis of data corruption attacks and energy consumption effects on edge servers using concurrent stochastic games

Author(s): Abdelhakim Baouya, Brahim Hamid, Saddek Bensalem

Pages: 2189 - 2214

13) Enhanced TODIM-TOPSIS framework for design quality evaluation for college smart sports venues under hesitant fuzzy sets

Author(s): Feng Yang, Yuefang Wu, Yi Li

Pages: 2215 - 2227

14) New Vigenere method with pseudo-random affine functions for color image encryption

Author(s): Hamid El Bourakkadi, Abdelhakim Chemlal, Abdelhamid Benazzi

Pages: 2229 - 2245

15) Adopting fuzzy multi-criteria decision-making ranking approach ensuring connected topology in industrial wireless sensor networks

Author(s): Anvita Nandan, Itu Snigdh

Pages: 2247 - 2261

16) Leveraging feature fusion ensemble of VGG16 and ResNet-50 for automated potato leaf abnormality detection in precision agriculture

Author(s): Amit Kumar Trivedi, Tripti Mahajan, Shailendra Tiwari

Pages: 2263 - 2277

17) Deteriorating inventory model with advance-cash-credit payment schemes and partial backlogging

Author(s): Chun-Tao Chang, Mei-Chuan Cheng, Liang-Yuh Ouyang

Pages: 2279 - 2295

18) Reliability analysis of discrete-time multi-state star configuration power grid systems with performance sharing

Author(s): Peng Su, Keyong Zhang, Honghua Shi

Pages: 2297 - 2310

19) Secure transmission of medical image using a wavelet interval type-2 TSK fuzzy brain-imitated neural network

Author(s): Duc-Hung Pham, Tuan-Tu Huynh, Van-Phong Vu

Pages: 2311 - 2329

20) Enhanced single shot detector for small object detection in drone-capture scenarios

Author(s): Yanxia Shi, Yanrong Liu, Yaru Liu

Pages: 2331 - 2341

21) A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

Author(s): Mohamed Hosny, Mohamed A. Naeem, Yili Fu

Pages: 2343 - 2362

22) A lightweight CNN model for UAV-based image classification

Author(s): Xinjie Deng, Michael Shi, Chee Peng Lim

Pages: 2363 - 2378

23) Gender opposition recognition method fusing emojis and multi-features in Chinese speech

Author(s): Shunxiang Zhang, Zichen Ma, Kuan-Ching Li

Pages: 2379 - 2390

24) RETRACTED ARTICLE: Near-infrared and visible light face recognition: a comprehensive survey

Author(s): Fangzheng Huang, Xikai Tang, Dayan Ban

Pages: 2391 - 2391

25) Retraction Note: Classification of noiseless corneal image using capsule networks

Author(s): H. James Deva Koresh, Shanty Chacko

Pages: 2393 - 2393

26) Retraction Note: Enhancing performance of cell formation problem using hybrid efficient swarm optimization

Author(s): G. Nagaraj, Manimaran Arunachalam, S. Paramasamy

Pages: 2395 - 2395

27) Retraction Note: IADF security: insider attack detection using fuzzy logic in wireless multimedia sensor networks

Author(s): Ashwinth Janarthanan, Dhananjay Kumar, C. B. Divya Parvathe

Pages: 2397 - 2397

[bobbyyoungsworld]

2025’s Top 10 AI Agent Development Companies: Leading the Future of Intelligent Automation

The Rise of AI Agent Development in 2025

AI agent development is revolutionizing automation by leveraging deep learning, reinforcement learning, and cutting-edge neural networks. In 2025, top AI companies are integrating natural language processing (NLP), computer vision, and predictive analytics to create advanced AI-driven agents that enhance decision-making, streamline operations, and improve human-computer interactions. From healthcare and finance to cybersecurity and business automation, AI-powered solutions are delivering real-time intelligence, efficiency, and precision.

This article explores the top AI agent development companies in 2025, highlighting their proprietary frameworks, API integrations, training methodologies, and large-scale business applications. These companies are not only shaping the future of AI but also driving the next wave of technological innovation.

What Does an AI Agent Development Company Do?

AI agent development companies specialize in designing and building intelligent systems capable of executing complex tasks with minimal human intervention. Using machine learning (ML), reinforcement learning (RL), and deep neural networks (DNNs), these companies create AI models that integrate NLP, image recognition, and predictive analytics to automate processes and improve real-time interactions.

These firms focus on:

Developing adaptable AI models that process vast data sets, learn from experience, and optimize performance over time.

Integrating AI systems seamlessly into enterprise workflows via APIs and cloud-based deployment.

Enhancing automation, decision-making, and efficiency across industries such as fintech, healthcare, logistics, and cybersecurity.

Creating AI-powered virtual assistants, self-improving agents, and intelligent automation systems to drive business success.

Now, let’s explore the top AI agent development companies leading the industry in 2025.

Top 10 AI Agent Development Companies in 2025

1. Shamla Tech

Shamla Tech is a leading AI agent development company transforming businesses with state-of-the-art machine learning (ML) and deep reinforcement learning (DRL) solutions. They specialize in building AI-driven systems that enhance decision-making, automate complex processes, and boost efficiency across industries.

Key Strengths:

Advanced AI models trained on large datasets for high accuracy and adaptability.

Custom-built algorithms optimized for automation and predictive analytics.

Seamless API integration and cloud-based deployment.

Expertise in fintech, healthcare, and logistics AI applications.

Shamla Tech’s AI solutions leverage modern neural networks to enable businesses to scale efficiently while gaining a competitive edge through real-time intelligence and automation.

2. OpenAI

OpenAI continues to lead the AI revolution with cutting-edge Generative Pretrained Transformer (GPT) models and deep learning innovations. Their AI agents excel in content generation, natural language understanding (NLP), and automation.

Key Strengths:

Industry-leading GPT and DALL·E models for text and image generation.

Reinforcement learning (RL) advancements for self-improving AI agents.

AI-powered business automation and decision-making tools.

Ethical AI research focused on safety and transparency.

OpenAI’s innovations power virtual assistants, automated systems, and intelligent analytics platforms across multiple industries.

3. Google DeepMind

Google DeepMind pioneers AI research, leveraging deep reinforcement learning (DRL) and advanced neural networks to solve complex problems in healthcare, science, and business automation.

Key Strengths:

Breakthrough AI models like AlphaFold and AlphaZero for scientific advancements.

Advanced neural networks for real-world problem-solving.

Integration with Google Cloud AI services for enterprise applications.

AI safety initiatives ensuring ethical and responsible AI deployment.

DeepMind’s AI-driven solutions continue to enhance decision-making, efficiency, and scalability for businesses worldwide.

4. Anthropic

Anthropic focuses on developing safe, interpretable, and reliable AI systems. Their Claude AI family offers enhanced language understanding and ethical AI applications.

Key Strengths:

AI safety and human-aligned reinforcement learning (RLHF).

Transparent and explainable AI models for ethical decision-making.

Scalable AI solutions for self-driving cars, robotics, and automation.

Inverse reinforcement learning (IRL) for AI system governance.

Anthropic is setting new industry standards for AI transparency and accountability.

5. SoluLab

SoluLab delivers innovative AI and blockchain-based automation solutions, integrating machine learning, NLP, and predictive analytics to optimize business processes.

Key Strengths:

AI-driven IoT and blockchain integrations.

Scalable AI systems for healthcare, fintech, and logistics.

Cloud AI solutions on AWS, Azure, and Google Cloud.

AI-powered virtual assistants and automation tools.

SoluLab’s AI solutions provide businesses with highly adaptive, intelligent automation that enhances efficiency and security.

6. NVIDIA

NVIDIA is a powerhouse in AI hardware and software, providing GPU-accelerated AI training and high-performance computing (HPC) systems.

Key Strengths:

Advanced AI GPUs and Tensor Cores for machine learning.

AI-driven autonomous vehicles and medical imaging applications.

CUDA parallel computing for faster AI model training.

AI simulation platforms like Omniverse for robotics.

NVIDIA’s cutting-edge hardware accelerates AI model training and deployment for real-time applications.

7. SoundHound AI

SoundHound AI specializes in voice recognition and conversational AI, enabling seamless human-computer interaction across multiple industries.

Key Strengths:

Industry-leading speech recognition and NLP capabilities.

AI-powered voice assistants for cars, healthcare, and finance.

Houndify platform for custom voice AI integration.

Real-time and offline speech processing for enhanced usability.

SoundHound’s AI solutions redefine voice-enabled automation for businesses worldwide.

Final Thoughts

As AI agent technology evolves, these top companies are leading the charge in innovation, automation, and intelligent decision-making. Whether optimizing business operations, enhancing customer interactions, or driving scientific discoveries, these AI pioneers are shaping the future of intelligent automation in 2025.

By leveraging cutting-edge machine learning techniques, cloud AI integration, and real-time analytics, these AI companies continue to push the boundaries of what’s possible in AI-driven automation.

Stay ahead of the curve by integrating AI into your business strategy and leveraging the power of these top AI agent development company.

Want to integrate AI into your business? Contact a leading AI agent development company today!

[jcmarchi]

Using JPEG Compression to Improve Neural Network Training

New Post has been published on https://thedigitalinsider.com/using-jpeg-compression-to-improve-neural-network-training/

Using JPEG Compression to Improve Neural Network Training

A new research paper from Canada has proposed a framework that deliberately introduces JPEG compression into the training scheme of a neural network, and manages to obtain better results – and better resistance to adversarial attacks.

This is a fairly radical idea, since the current general wisdom is that JPEG artifacts, which are optimized for human viewing, and not for machine learning, generally have a deleterious effect on neural networks trained on JPEG data.

An example of the difference in clarity between JPEG images compressed at different loss values (higher loss permits a smaller file size, at the expense of delineation and banding across color gradients, among other types of artifact). Source: https://forums.jetphotos.com/forum/aviation-photography-videography-forums/digital-photo-processing-forum/1131923-how-to-fix-jpg-compression-artefacts?p=1131937#post1131937

A 2022 report from the University of Maryland and Facebook AI asserted that JPEG compression ‘incurs a significant performance penalty’ in the training of neural networks, in spite of previous work that claimed neural networks are relatively resilient to image compression artefacts.

A year prior to this, a new strand of thought had emerged in the literature: that JPEG compression could actually be leveraged for improved results in model training.

However, though the authors of that paper were able to obtain improved results in the training of JPEG images of varying quality levels, the model they proposed was so complex and burdensome that it was not practicable. Additionally, the system’s use of default JPEG optimization settings (quantization) proved a barrier to training efficacy.

A later project (2023’s JPEG Compliant Compression for DNN Vision) experimented with a system that obtained slightly better results from JPEG-compressed training images with the use of a frozen deep neural network (DNN) model. However, freezing parts of a model during training tends to reduce the versatility of the model, as well as its broader resilience to novel data.

JPEG-DL

Instead, the new work, titled JPEG Inspired Deep Learning, offers a much simpler architecture, which can even be imposed upon existing models.

The researchers, from the University of Waterloo, state:

‘Results show that JPEG-DL significantly and consistently outperforms the standard DL across various DNN architectures, with a negligible increase in model complexity.

Specifically, JPEG-DL improves classification accuracy by up to 20.9% on some fine-grained classification dataset, while adding only 128 trainable parameters to the DL pipeline. Moreover, the superiority of JPEG-DL over the standard DL is further demonstrated by the enhanced adversarial robustness of the learned models and reduced file sizes of the input images.’

The authors contend that an optimal JPEG compression quality level can help a neural network distinguish the central subject/s of an image. In the example below, we see baseline results (left) blending the bird into the background when features are obtained by the neural network. In contrast, JPEG-DL (right) succeeds in distinguishing and delineating the subject of the photo.

Tests against baseline methods for JPEG-DL. Source: https://arxiv.org/pdf/2410.07081

‘This phenomenon,’ they explain, ‘termed “compression helps” in the [2021] paper, is justified by the fact that compression can remove noise and disturbing background features, thereby highlighting the main object in an image, which helps DNNs make better prediction.’

Method

JPEG-DL introduces a differentiable soft quantizer, which replaces the non-differentiable quantization operation in a standard JPEG optimization routine.

This allows for gradient-based optimization of the images. This is not possible in conventional JPEG encoding, which uses a uniform quantizer with a rounding operation that approximates the nearest coefficient.

The differentiability of JPEG-DL’s schema permits joint optimization of both the training model’s parameters and the JPEG quantization (compression level). Joint optimization means that both the model and the training data are accommodated to each other in the end-to-end process, and no freezing of layers is needed.

Essentially, the system customizes the JPEG compression of a (raw) dataset to fit the logic of the generalization process.

Conceptual schema for JPEG-DL.

One might assume that raw data would be the ideal fodder for training; after all, images are completely decompressed into an appropriate full-length color space when they are run in batches; so what difference does the original format make?

Well, since JPEG compression is optimized for human viewing, it throws areas of detail or color away in a manner concordant with this aim. Given a picture of a lake under a blue sky, increased levels of compression will be applied to the sky, because it contains no ‘essential’ detail.

On the other hand, a neural network lacks the eccentric filters which allow us to zero in on central subjects. Instead, it is likely to consider any banding artefacts in the sky as valid data to be assimilated into its latent space.

Though a human will dismiss the banding in the sky, in a heavily compressed image (left), a neural network has no idea that this content should be thrown away, and will need a higher-quality image (right). Source: https://lensvid.com/post-processing/fix-jpeg-artifacts-in-photoshop/

Therefore, one level of JPEG compression is unlikely to suit the entire contents of a training dataset, unless it represents a very specific domain. Pictures of crowds will require much less compression than a narrow-focus picture of a bird, for instance.

The authors observe that those unfamiliar with the challenges of quantization, but who are familiar with the basics of the transformers architecture, can consider these processes as an ��attention operation’, broadly.

Data and Tests

JPEG-DL was evaluated against transformer-based architectures and convolutional neural networks (CNNs). Architectures used were EfficientFormer-L1; ResNet; VGG; MobileNet; and ShuffleNet.

The ResNet versions used were specific to the CIFAR dataset: ResNet32, ResNet56, and ResNet110. VGG8 and VGG13 were chosen for the VGG-based tests.

For CNN, the training methodology was derived from the 2020 work Contrastive Representation Distillation (CRD). For EfficientFormer-L1 (transformer-based), the training method from the 2023 outing Initializing Models with Larger Ones was used.

For fine-grained tasks featured in the tests, four datasets were used: Stanford Dogs; the University of Oxford’s Flowers; CUB-200-2011 (CalTech Birds); and Pets (‘Cats and Dogs’, a collaboration between the University of Oxford and Hyderabad in India).

For fine-grained tasks on CNNs, the authors used PreAct ResNet-18 and DenseNet-BC. For EfficientFormer-L1, the methodology outlined in the aforementioned Initializing Models With Larger Ones was used.

Across the CIFAR-100 and fine-grained tasks, the varying magnitudes of Discrete Cosine Transform (DCT) frequencies in the JPEG compression approach was handled with the Adam optimizer, in order to adapt the learning rate for the JPEG layer across the models that were tested.

In tests on ImageNet-1K, across all experiments, the authors used PyTorch, with SqueezeNet, ResNet-18 and ResNet-34 as the core models.

For the JPEG-layer optimization evaluation, the researchers used Stochastic Gradient Descent (SGD) instead of Adam, for more stable performance. However, for the ImageNet-1K tests, the method from the 2019 paper Learned Step Size Quantization was employed.

Above the top-1 validation accuracy for the baseline vs. JPEG-DL on CIFAR-100, with standard and mean deviations averaged over three runs. Below, the top-1 validation accuracy on diverse fine-grained image classification tasks, across various model architectures, again, averaged from three passes.

Commenting on the initial round of results illustrated above, the authors state:

‘Across all seven tested models for CIFAR-100, JPEG-DL consistently provides improvements, with gains of up to 1.53% in top-1 accuracy. In the fine-grained tasks, JPEG-DL offers a substantial performance increase, with improvements of up to 20.90% across all datasets using two different models.’

Results for the ImageNet-1K tests are shown below:

Top-1 validation accuracy results on ImageNet across diverse frameworks.

Here the paper states:

‘With a trivial increase in complexity (adding 128 parameters), JPEG-DL achieves a gain of 0.31% in top-1 accuracy for SqueezeNetV1.1 compared to the baseline using a single round of [quantization] operation.

‘By increasing the number of quantization rounds to five, we observe an additional improvement of 0.20%, leading to a total gain of 0.51% over the baseline.’

The researchers also tested the system using data compromised by the adversarial attack approaches Fast Gradient Signed Method (FGSM) and Projected Gradient Descent (PGD).

The attacks were conducted on CIFAR-100 across two of the models:

Testing results for JPEG-DL, against two standard adversarial attack frameworks.

The authors state:

‘[The] JPEG-DL models significantly improve the adversarial robustness compared to the standard DNN models, with improvements of up to 15% for FGSM and 6% for PGD.’

Additionally, as illustrated earlier in the article, the authors conducted a comparison of extracted feature maps using GradCAM++ – a framework that can highlight extracted features in a visual manner.

A GradCAM++ illustration for baseline and JPEG-DL image classification, with extracted features highlighted.

The paper notes that JPEG-DL produces an improved result, and that in one instance it was even able to classify an image that the baseline failed to identify. Regarding the earlier-illustrated image featuring birds, the authors state:

‘[It] is evident that the feature maps from the JPEG-DL model show significantly better contrast between the foreground information (the bird) and the background compared to the feature maps generated by the baseline model.

‘Specifically, the foreground object in the JPEG-DL feature maps is enclosed within a well-defined contour, making it visually distinguishable from the background.

‘In contrast, the baseline model’s feature maps show a more blended structure, where the foreground contains higher energy in low frequencies, causing it to blend more smoothly with the background.’

Conclusion

JPEG-DL is intended for use in situations where raw data is available – but it would be most interesting to see if some of the principles featured in this project could be applied to conventional dataset training, wherein the content may be of lower quality (as frequently occurs with hyperscale datasets scraped from the internet).

As it stands, that largely remains an annotation problem, though it has been addressed in traffic-based image recognition, and elsewhere.

First published Thursday, October 10, 2024

[predictiveanalyticsaiproject]

Introduction

In the digital age, data-driven decisions have become the cornerstone of successful businesses. Predictive analytics, powered by deep learning, offers unprecedented insights, enabling companies to anticipate trends and make informed choices. Our project, "Predictive Analytics on Business License Data Using Deep Learning Project," serves as a comprehensive introduction to deep neural networks (DNNs) and their application in real-world scenarios. By analyzing data from 86,000 businesses across various sectors, this project not only demystifies deep learning concepts but also demonstrates how they can be effectively utilized for predictive analytics.

The Importance of Predictive Analytics in Business

Predictive analytics uses historical data to forecast future events, helping businesses anticipate market changes, optimize operations, and enhance decision-making processes. In this project, we focus on business license data to predict the status of licenses, offering valuable insights into compliance trends, potential risks, and operational benchmarks.

Project Overview

Our project is designed to teach participants the fundamentals of deep neural networks (DNNs) through a hands-on approach. Using a dataset of business licenses, participants will learn essential steps such as Exploratory Data Analysis (EDA), data cleaning, and preparation. The project introduces key deep learning concepts like activation functions, feedforward, backpropagation, and dropout regularization, all within the context of building and evaluating DNN models.

Methodology

The project is structured into several key phases:

Data Exploration and Preparation:

Participants begin by exploring the dataset, identifying key features, and understanding the distribution of license statuses.

Data cleaning involves handling missing values, standardizing categorical variables, and transforming the data into a format suitable for modeling.

Building Baseline Models:

Before diving into deep learning, we create baseline models using the H2O framework. This step helps participants understand the importance of model comparison and sets the stage for more complex DNN models.

Deep Neural Networks (DNN) Development:

The core of the project involves building and training DNN models using TensorFlow. Participants learn how to design a neural network architecture, choose activation functions, implement dropout regularization, and fine-tune hyperparameters.

The model is trained to predict the status of business licenses based on various features, such as application type, license code, and business type.

Model Evaluation:

After training, the DNN model is evaluated on a test dataset to assess its performance. Participants learn to interpret metrics like accuracy, loss, and confusion matrices, gaining insights into the model's predictive power.

Results and Impact

The DNN model developed in this project demonstrates strong predictive capabilities, accurately classifying business license statuses. This model serves as a valuable tool for businesses and regulators, enabling them to anticipate compliance issues, streamline operations, and make data-driven decisions. Beyond the immediate application, participants gain a solid foundation in deep learning, preparing them for more advanced projects in the field of AI and machine learning.

Conclusion

The "Predictive Analytics on Business License Data Using Deep Learning" project offers a practical and educational journey into the world of deep learning. By engaging with real-world data and building predictive models, participants not only enhance their technical skills but also contribute to the broader field of AI-driven business analytics. This project underscores the transformative potential of deep learning in unlocking valuable insights from complex datasets, paving the way for more informed and strategic business decisions. You can download "Predictive Analytics on Business License Data Using Deep Learning Project (https://www.aionlinecourse.com/ai-projects/playground/predictive-analytics-on-business-license-data-using-deep-learningund/complete-cnn-image-classification-models-for-real-time-prediction)" from Aionlinecourse. Also you will get a live practice session on this playground.

[jhavelikes]

SQUID (Surrogate Quantitative Interpretability for Deepnets), a genomic DNN interpretability framework based on domain-specific surrogate modelling. SQUID approximates genomic DNNs in user-specified regions of sequence space using surrogate models—simpler quantitative models that have inherently interpretable mathematical forms. SQUID leverages domain knowledge to model cis-regulatory mechanisms in genomic DNNs, in particular by removing the confounding effects that nonlinearities and heteroscedastic noise in functional genomics data can have on model interpretation. Benchmarking analysis on multiple genomic DNNs shows that SQUID, when compared to established interpretability methods, identifies motifs that are more consistent across genomic loci and yields improved single-nucleotide variant-effect predictions. SQUID also supports surrogate models that quantify epistatic interactions within and between cis-regulatory elements, as well as global explanations of cis-regulatory mechanisms across sequence contexts. SQUID thus advances the ability to mechanistically interpret genomic DNNs.

Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models | Nature Machine Intelligence

[govindhtech]

Convolutional Neural Network & AAAI 2024 vision transformer

How Does AMD Improve AI Algorithm Hardware Efficiency?

Convolutional Neural Network(CNN) Unified Progressive Depth Pruner and AAAI 2024 Vision Transformer. Users worldwide have acknowledged AMD, one of the biggest semiconductor suppliers in the world, for its innovative chip architectural design and AI development tools. As AI advances so quickly, one of Their goals is to create high-performance algorithms that work better with AMD hardware.

Inspiration

Deep neural networks (DNNs) have achieved notable breakthroughs in a wide range of tasks, leading to impressive achievements in industrial applications. Model optimization is one of these applications that is in high demand since it can increase model inference speed while reducing accuracy trade-offs. This effort involves several methods, including effective model design, quantization, and model pruning. A common method for optimizing models in industrial applications is model trimming.

Model pruning is a major acceleration technique that aims to remove unnecessary weights intentionally while preserving accuracy. Because of sparse computation and fewer parameters, depth-wise convolutional layers provide difficulties for the traditional channel-wise pruning approach. Furthermore, channel-wise pruning techniques would make efficient models thinner and sparser, which would result in low hardware utilization and lower possible hardware efficiency.

Moreover, current model platforms favor a larger degree of parallel computation, such as GPUs. Depth Shrinker and Layer-Folding are suggested as ways to optimize MobileNetV2 in order to solve these problems by using reparameterization approaches to reduce model depth.

These techniques do have some drawbacks, though, such as the following:

The process of fine-tuning a subnet by eliminating activation layers directly may jeopardies the integrity of baseline model weights, making it more difficult to achieve high performance.

These techniques have usage restrictions.

They cannot be used to prune models that have certain normalization layers, such as Layer Norm.

Because Layer Norm is present in vision transformer models, these techniques cannot be applied to them for optimization.

Convolutional Neural Network

In order to address these issues, they suggest a depth pruning methodology that can prune Convolutional Neural Network(CNN) and vision transformer models, together with a novel block pruning method and progressive training strategy. Higher accuracy can be achieved by using the progressive training technique to transfer the baseline model structure to the subnet structure with high utilization of baseline model weights.

The current normalization layer problem can be resolved by their suggested block pruning technique, which in theory can handle all activation and normalization layers. As a result, vision transformer models can be pruned using the AMD method, which is incompatible with current depth pruning techniques.

Important Technologies

Rather than just removing the block, the AMD depth pruning approach proposes a novel block pruning strategy with reparameterization technique in an effort to reduce model depth. In block merging, the AMD block trimming technique transforms a complicated and sluggish block into a simple and fast block, as seen in Figure.

Figure : The suggested depth pruner framework by AMD. To speed up and conserve memory, each baseline block that has been pruned will progressively grow into a smaller merged block. Four baselines are tested: one vision transformer network (DeiT-Tiny) and three CNN-based networks (ResNet34, MobileNetV2, and ConvNeXtV1).

Supernet training, Subnet finding, Subnet training, and Subnet merging are the four primary phases that make up the technique. As seen in Figure , users first build a Supernet based on the basic architecture and modify blocks inside it. An ideal subnet is found via a search algorithm following Supernet training. It then use a progressive training approach that has been suggested to optimize the best Subnet with the least amount of accuracy loss. In the end, the reparameterization process would combine the Subnet into a shallower model.

Advantages

Key contributions are summarized below:

A novel block pruning strategy using reparameterization technique.

A progressive training strategy for subnet optimization.

Conducting extensive experiments on both Convolutional Neural Network(CNN) and vision transformer models to showcase the superb pruning performance provided by depth pruning method.

A unified and efficient depth pruning method for both Convolutional Neural Network(CNN) and vision transformer models.

With the AMD approach applied on ConvNeXtV1, they got three pruned ConvNeXtV1 models, which outperform popular models with similar inference performance, as illustrates, where P6 represents pruning 6 blocks of the model. Furthermore, this approach beats existing state-of-the-art methods in terms of accuracy and speedup ratio, as demonstrated. With only 1.9% top-1 accuracy reductions, the suggested depth pruner on AMD Instinct MI100 GPU accelerator achieves up to 1.26X speedup.

ConvNeXtV1 depth pruning findings on ImageNet performance. A batch size of 128 AMD Instinct MI100 GPUs is used to test speedups. Use the slowest network (EfficientFormerV2) in the table as the benchmark (1.0 speedup) for comparison.

The findings of WD-Pruning (Yu et al. 2022) and S2ViTE (Tang et al. 2022) are cited in their publication. The results of XPruner (Yu and Xiang 2023) and HVT (Pan et al. 2021), as well as SCOP (Tang et al. 2020), are not publicly available.

In summary

They have implemented this method on several Convolutional Neural Network(CNN) models and transformer models, to provide a unified depth pruner for both effective Convolutional Neural Network(CNN) and visual transformer models to prune models in the depth dimension. The benefits of this approach are demonstrated by the SOTA pruning performance. They plan to investigate the methodology on additional transformer models and workloads in the future.

Read more on Govindhtech.com

[inestwebnoida]

 .NET Based CMS Platforms For Your Business

In today’s digital landscape, Content Management Systems (CMS) play a crucial role in helping businesses manage their online presence efficiently. For companies utilizing .NET, selecting the appropriate CMS is vital for seamless content creation, publishing, and management. Let’s explore the top 5 .NET-based CMS platforms and their key features:

Kentico:

Robust CMS platform with features tailored for businesses of all sizes.

User-friendly interface and extensive customization options.

Key features include content editing, multilingual support, e-commerce capabilities, and built-in marketing tools.

Sitecore:

Renowned for scalability and personalization capabilities.

Enables businesses to deliver personalized digital experiences across various touchpoints.

Advanced analytics and marketing automation tools drive customer engagement and conversion.

Umbraco:

Open-source CMS known for flexibility and simplicity.

Ideal for businesses seeking lightweight yet powerful content management.

User-friendly interface, extensive customization options, and seamless integration with Microsoft technologies.

Orchard Core:

Modular and extensible CMS built on ASP.NET Core framework.

Allows developers to create custom modules and extensions, adapting to diverse business needs.

Offers flexibility and scalability for building simple blogs to complex enterprise applications.

DNN (formerly DotNetNuke):

Feature-rich CMS trusted by thousands of businesses worldwide.

Drag-and-drop page builder, customizable themes, and robust security features.

Offers modules and extensions for creating powerful websites, intranets, and online communities

In conclusion, selecting the right .NET-based CMS platform is crucial for establishing a strong online presence and engaging effectively with the audience. Each platform offers unique features and benefits to suit various business needs. By evaluating factors like flexibility, scalability, personalization, and community support, businesses can choose the ideal CMS platform to drive digital success.

[technology-123s-blog]

Best Deep Learning Projects for Final Year Students

       By using a group of committed experts in a Best deep learning Projects, the Takeoff Projects fully guarantees the use of the selected characteristics in terms of the best deep learning projects. Students get chance either they come up with their own deep learning project idea or they can select one from our preselected list of deep learning project ideas. Whether you need full assistance for your project or strive to do it yourself and we've got you covered, our job will be done flawlessly and we'll deliver it within the set time frame. Thus, if you are a student wanting to do those class projects that don’t need much attention, you may be looking forward to the team of Takeoff Projects excellent experts.                                                                                              

Latest Deep Learning Projects:

Brain Disease Classification /Brain Age Estimation by Using a CNN.

A Humanize Video Image De-Blurring Algorithm with Digital Engines.

A New cryptographic watermarking method based on 3D object and hash encryption.

Satellite Image Classification Method Using ELBP and SVM Classifier Utilizing Satellite Image

 Classification Method Using ELBP And SVM Classifier

Computer Vision Approach of Food Classification employing the Deep Learning technique.

YOLO-V3 infrared picture recognition of pedestrians as a method of detection.

Trending Deep Learning Projects:

To provide a full comprehensive cause of Alzheimer’s Disorder, a hybrid model is invented.

A Solid H2O Image Watermark Embedding Withdrawal of Convolutional Neural Networks

An Integrated Aircraft Image Segmentation Sequence Taking into Account Refinement Multi-scale.

Fascinating Imagery of the Under Water due to a Deep Residual Framework.

Satellite Ship Detection through the use of Deep Learning Techniques from Optical Imagery.

Deep Convolutional Neural Network for Video Classification-DNN

Convolutional Neural Network-Based Image forging Classification

Trending Deep Learning Projects:

Dense Fuse: A Fusion Method of Infrared and Visual Images that operate together.

Facial emotion recognition in real time with the aid of CNNs.

An implementation of Cascaded Convolutional Neural Network from Data Intensity of the Single Image.

Capsule Network Painting of Breast Cancer Classification

Fingerprints as an Indicator in Crime Prevention Systems

Did We Interest You and Did we Mention That We Have Deep Learning Assistance as Well?

The cutting edge in machine learning is called deep learning, and it is the one to automate artificial intelligence. Accordingly, put yourself in the shoes of the machine learning algorithms as they are the most sober trained and outfitted soldiers that have only done basic training but will be in total control of the soldiers who are the deep learning algorithms, so these are the commandoes that are specially tasked out for the operation of the strategy, to fit the conditions, and most importantly to get the job done in the most complicated and sophisticated way possible, While the main objective of both these algorithms is the same, they are different from each other just as two humans while they gone through the same schooling also have the different strengths and weaknesses.

Why it is pretty to carry out (deep learning is that) a project?

A key difference between machine learning and deep learning is that the latter doesn’t require feature extraction as it learns patterns and features automatically, while the former requires feature extraction. Thus, the data points in deep learning may be millions in theory in which the layers of artificial neural networks are also complex and high-performance computers with GPUs (graphics processing units) are usually needed to process and also great training data to train. If machine learning projects require deep thought, deep learning projects are even more challenging and technologically grounded. In this case, the task’s success will greatly depend on how fast the students will be able to consult and seek guidance from the experts amid the allotted time frame.

If you are an undergraduate and you need hands-on experiences with AI and Best deep learning projects, then Takeoff Projects is an organization that can offer you all the assistance you may require.

Visit More information: https://takeoffprojects.com/best-deep-learning-projects

[archiveosorg]

DNN

DNN (DotNetNuke) is a free web application framework based on the https://archiveapp.org/dnn/

[amn-group]

Ukraine says USA and German air guard frameworks ‘exceptionally successful’.

[siyacarla]

The Impact of Python on Data Science and Machine Learning

Data science and machine learning have become increasingly important in a variety of industries, from finance to healthcare to marketing. With the rise of large data sets and the need for sophisticated algorithms to analyze it, companies hire professionals with expertise in these areas. 

Programming languages are crucial in data science and machine learning as they create models, manipulate data, and automate processes.

 Python has emerged as one of the premier data science and machine learning programming languages. It is known for its readability, simplicity, and versatility – making it an appealing choice for both novice and experienced developers.

Python's extensive libraries, such as NumPy, Pandas Matplotlib, etc., enable efficient manipulation & visualization of large datasets, making it a go-to choice among Data Scientists worldwide.

Python: An Overview

Python is a dynamic, versatile, ever-growing programming language that has taken the tech industry by storm. It was created in 1991 with an emphasis on simplicity & ease-of-use making it one of the most beginner-friendly languages.

One of Python's main strengths is its readability which makes it accessible even for non-technical stakeholders while still providing developers with powerful abstractions required for building complex systems. Additionally, Python's emphasis on code readability makes it easy to maintain and modify existing codebases.

 It also boasts a rich library and framework ecosystem, enabling a Python app development agency to build robust applications quickly. These include NumPy & Pandas (for Data Analysis), Django & Flask (for Web Development), and TensorFlow & PyTorch(for Artificial Intelligence/Machine Learning), which simplify the creation of complex systems.

 In addition to being used extensively in web application development services & data analysis, python has emerged as one of the primary languages utilized within AI/ML due to its capability to handle large amounts of data efficiently.

Python for Data Science

Python has revolutionized the field of data science with its powerful libraries and frameworks. NumPy, Pandas, and Matplotlib are some of the key components that make Python an excellent tool for data scientists.

 Pandas is a game-changing library that simplifies data manipulation and analysis tasks. With Pandas, you can easily load datasets from various sources, perform complex queries using DataFrame objects, handle missing values efficiently, and much more.

NumPy is another essential library for numerical computations in Python. It provides fast array operations for large-scale scientific computing applications such as linear algebra or Fourier transforms.

Data visualization is crucial to understand trends within your dataset quickly. Matplotlib offers a wide range of charts/graphs/histograms/diagrams to display your information interactively, providing valuable insights into your dataset.

With these tools under their belt, Data Scientists can explore complex datasets without worrying about implementation details & instead focus on extracting meaningful insights from raw data.

Python for Machine Learning

Machine learning is the practice of teaching machines to learn from data, enabling them to make predictions or decisions without being explicitly programmed. Its applications range from natural language processing and image recognition to fraud detection and autonomous vehicles.

Python has emerged as a leading language for machine learning due to its powerful libraries like scikit-learn & TensorFlow. 

Scikit-Learn provides an extensive array of supervised and unsupervised algorithms that enable users to build models with minimal coding effort. kNN (K-nearest neighbors) is a supervised learning algorithm used to solve classification and regression tasks.

TensorFlow offers an approachable way to create complex Neural Networks(DNN/CNN/RNN) capable of handling large-scale datasets. 

Keras is another popular library built on top of Tensorflow, which simplifies building deep learning models by abstracting away some implementation details.

With these tools, Python developers can leverage machine learning techniques across industries/domains regardless of domain expertise, making it easier than ever for anyone interested in exploring this exciting field.

Advantages of Python in Data Science and Machine Learning

Python has emerged as the language of choice for data science and machine learning because of its many advantages over other languages. Some of these benefits include:

Simplicity & Readability

Python is known for its convenience and readability, making it easy for newcomers to learn. Its straightforward syntax ensures that even complex models can be implemented with ease.

Vast community support and active development: 

The Python community is incredibly supportive, providing users access to vast libraries/forums/blogs, and tutorials. Active development ensures that new tools/features are continually added while existing ones are improved upon.

Easy integration with other tools/languages: 

Python's ability to interface seamlessly with other languages/tools makes it highly versatile enabling developers to use their favorite libraries or leverage specialized hardware like GPUs/Tensor Processing Units (TPUs) without worrying about compatibility issues.

Availability of pre-trained models/Open-source code repositories: 

With numerous open-source libraries such as TensorFlow/Keras/scikit-learn amongst others, Developers can leverage pre-trained models or ready-made solutions rather than building from scratch saving time & effort in implementation.

 These benefits make it clear why Python is becoming increasingly popular among data scientists worldwide

Case Studies and Real-World Applications

Python has proven to be a game-changer in data science and machine learning, as evidenced by numerous case studies showcasing its impact in diverse industries. From healthcare to finance and marketing, it has played a significant role in driving innovation and enabling data-driven decision-making.

 In the healthcare industry, Python is used to analyze medical records and identify patients at risk of developing certain diseases. This enables early intervention and personalized treatment plans based on individual patient needs.

In finance, Python is used to develop models that can predict stock prices or identify fraudulent activities. These models are trained using vast amounts of historical data enabling accurate predictions resulting in better trading decisions while minimizing risks.

Furthermore, it has revolutionized marketing by giving companies access to advanced analytics and machine learning algorithms. 

Real-world success stories also highlight Python's impact. For instance, Netflix relies on Python's recommendation system to provide personalized content suggestions, while Airbnb optimizes pricing algorithms using Python to ensure the best rates for hosts and guests.

These examples highlight how Python is reshaping industries worldwide providing valuable insights into complex datasets leading innovation across domains while offering flexible solutions at every stage.

Conclusion

Python has emerged as a driving force in the fields of data science and machine learning, leaving an indelible impact on the way we approach and leverage data. Its significance cannot be overstated, as it continues to shape industries, drive innovation, and fuel breakthroughs.

In this age of data-driven transformation, the significance of data science and machine learning is undeniable. With an ever-growing demand for insights, these fields promise endless possibilities. Thanks to supportive communities like Finoit, led by visionary CEO Yogesh Choudhary, aspiring data enthusiasts have abundant resources and powerful tools to shape the future. 

So, embrace the power of Python and unlock the doors to a world of unlimited possibilities in data science and machine learning. 

[craigbrownphd]

If you did not already know

MorphNet We introduce MorphNet, a single model that combines morphological analysis and disambiguation. Traditionally, analysis of morphologically complex languages has been performed in two stages: (i) A morphological analyzer based on finite-state transducers produces all possible morphological analyses of a word, (ii) A statistical disambiguation model picks the correct analysis based on the context for each word. MorphNet uses a sequence-to-sequence recurrent neural network to combine analysis and disambiguation. We show that when trained with text labeled with correct morphological analyses, MorphNet obtains state-of-the art or comparable results for nine different datasets in seven different languages. … Moran’s I In statistics, Moran’s I is a measure of spatial autocorrelation developed by Patrick Alfred Pierce Moran. Spatial autocorrelation is characterized by a correlation in a signal among nearby locations in space. Spatial autocorrelation is more complex than one-dimensional autocorrelation because spatial correlation is multi-dimensional (i.e. 2 or 3 dimensions of space) and multi-directional. … Regularization Learning Network (RLN) Despite their impressive performance, Deep Neural Networks (DNNs) typically underperform Gradient Boosting Trees (GBTs) on many tabular-dataset learning tasks. We propose that applying a different regularization coefficient to each weight might boost the performance of DNNs by allowing them to make more use of the more relevant inputs. However, this will lead to an intractable number of hyperparameters. Here, we introduce Regularization Learning Networks (RLNs), which overcome this challenge by introducing an efficient hyperparameter tuning scheme that minimizes a new Counterfactual Loss. Our results show that RLNs significantly improve DNNs on tabular datasets, and achieve comparable results to GBTs, with the best performance achieved with an ensemble that combines GBTs and RLNs. RLNs produce extremely sparse networks, eliminating up to 99.8% of the network edges and 82% of the input features, thus providing more interpretable models and reveal the importance that the network assigns to different inputs. RLNs could efficiently learn a single network in datasets that comprise both tabular and unstructured data, such as in the setting of medical imaging accompanied by electronic health records. … Meta-Learning Autoencoder (MeLA) Compared to humans, machine learning models generally require significantly more training examples and fail to extrapolate from experience to solve previously unseen challenges. To help close this performance gap, we augment single-task neural networks with a meta-recognition model which learns a succinct model code via its autoencoder structure, using just a few informative examples. The model code is then employed by a meta-generative model to construct parameters for the task-specific model. We demonstrate that for previously unseen tasks, without additional training, this Meta-Learning Autoencoder (MeLA) framework can build models that closely match the true underlying models, with loss significantly lower than given by fine-tuned baseline networks, and performance that compares favorably with state-of-the-art meta-learning algorithms. MeLA also adds the ability to identify influential training examples and predict which additional data will be most valuable to acquire to improve model prediction. … https://analytixon.com/2023/04/23/if-you-did-not-already-know-2025/?utm_source=dlvr.it&utm_medium=tumblr

[our13belowconsulting-blog]

13 Below provides you the best DNN web development.

[mindfiresolutions-blog]

ONLINE PHOTO UPLOADING MODULE

Executive Summary

This project aims at developing an online photo uploading module. The client had always thought of building a module which would provide user to upload their school days photo with description related to the photo and the photographer. In addition, he wanted another module for multiple photo upload and an option to transfer them to the site administrator to be published on specific pages. Due to the usage of older version of DNN and ASP.NET in their existing system, their dream could not be achieved.

In order to make his dream a reality the client approached Mindfire Solutions to come up with solution for its requirement that will help its end users. Constant research on the existing application by Mindfire’s technical team resulted with an outcome which was something that the client accepted without any hesitation.

About our Client

Client Description: Software Product & Services

Client Location: Virginia, USA

Industry: Education

Technologies

.NET Framework 2.0, DotNetNuke 4.9, SQL Server 2005 (Express Edition)

Download Full Case Study

[hackgit]

[Media] ​​NNSmith

​​NNSmith Automatic #DNN generation for #fuzzing and more. A random DNN generator and a fuzzing infrastructure, primarily designed for automatically validating deep-learning frameworks and compilers. https://github.com/ise-uiuc/nnsmith #bugbounty #pentesting