case study on deep learning

Now comes the cool part, end-to-end application of deep learning to real-world datasets. We will cover the 3 most commonly encountered problems as case studies: binary classification, multiclass classification and regression. Case Study: Binary Classification. 1.1) Data Visualization & Preprocessing. 1.2) Logistic Regression Model.

Deep learning can effectively process complex and varied data types to provide precise agricultural forecasts. Accurate yield predictions are crucial for optimizing the agricultural supply chain and reducing unnecessary waste. Related: Generative AI Case Studies . Case Study 4: AutoDrive Inc. - Autonomous Vehicle Navigation. Task or Conflict:

NLP deep learning applications include speech recognition, text classification, sentiment analysis, text simplification and summarisation, writing style recognition, machine translation, parts-of-speech tagging, and text-to-speech tasks. This technology helps us for. virtual voice/smart assistants. Digital workers.

Abstract. In this paper, we propose mesoscience-guided deep learning (MGDL), a deep learning modeling approach guided by mesoscience, to study complex systems. When establishing sample dataset based on the same system evolution data, different from the operation of conventional deep learning method, MGDL introduces the treatment of the dominant ...

When a deep-learning model (or any machine-learning model) is deployed, we need to be careful of how it may treat real-world entities (in the case of Netflix, members and videos for example), and whether there are any unintentional biases that cause the model to treat some entities in an unfair way.

Easy over Hard: A Case Study on Deep Learning Wei Fu, Tim Menzies Com.Sci., NC State, USA [email protected],[email protected] ABSTRACT While deep learning is an exciting new technique, the bene•ts of this method need to be assessed with respect to its computational cost. „is is particularly important for deep learning since these

Let's explore some real-world case studies now and build some deep transfer learning models! Case Study 1: Image Classification with a Data Availability Constraint. In this simple case study, will be working on an image categorization problem with the constraint of having a very small number of training samples per category. The dataset for ...

Case studies All TensorFlow TensorFlow Lite TensorFlow.js TFX Airbnb improves the guest experience by using TensorFlow to classify images and detect objects at scale ... China Mobile has created a deep learning system using TensorFlow that can automatically predict cutover time window, verify operation logs, and detect network anomalies. This ...

Deep learning algorithms try to learn massive amounts of unlabelled data and make a better analysis. With deep learning, all layers learn the input data and transform it into a more abstract and composite format. The word "deep" means higher numbers of hidden layers in which the data from one layer to another is transformed to generate the ...

Easy over Hard: A Case Study on Deep Learning. While deep learning is an exciting new technique, the benefits of this method need to be assessed with respect to its computational cost. This is particularly important for deep learning since these learners need hours (to weeks) to train the model. Such long training time limits the ability of (a ...

500+ times faster than deep learning: a case study exploring faster methods for text mining stackoverflow. MSR '18: Proceedings of the 15th International Conference on Mining Software Repositories . Deep learning methods are useful for high-dimensional data and are becoming widely used in many areas of software engineering. Deep learners ...

Easy over Hard: A Case Study on Deep Learning. W ei Fu, Tim Menzies. Com.Sci., NC State, USA. [email protected],[email protected]. ABSTRACT. While deep learning is an exciting new technique, the ...

recent results applying deep learning to software engineering, they could achieve a new state-of-the-art result. While an interesting study, Yedida & Menzies never tested their methods on anything else other than defect prediction. Accord-ingly, in this paper, we test if their extension to deep learning helps another SE domain.

In recent years, deep learning has become one of the most important topics in computer sciences. Deep learning is a growing trend in the edge of technology and its applications are now seen in many aspects of our life such as object detection, speech recognition, natural language processing, etc. Currently, almost all major sciences and technologies are benefiting from the advantages of deep ...

The current development in deep learning is witnessing an exponential transition into automation applications. This automation transition can provide a promising framework for higher performance and lower complexity. This ongoing transition undergoes several rapid changes, resulting in the processing of the data by several studies, while it may lead to time-consuming and costly models. Thus ...

Source: Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Published 2016 by MIT Press. Deep learning has also been used to improve the performance of traditional machine learning algorithms, such as decision trees and support vector machines.By using deep learning instead of traditional techniques, one can often achieve higher levels of accuracy, robustness, and reliability.

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL ...

Deep Learning (DL) has become more than just a buzzword in the Artificial Intelligence (AI) community - it is reshaping global business through the prolific use of autonomous, self-teaching systems, which can build models by directly studying images, text, audio, or video data. ... Deep Learning case studies are easily found in many ...

Deep learning has profoundly impacted many areas of machine learning. However, it took a while for its impact to be felt in the field of recommender systems. In this article, we outline some of the challenges encountered and lessons learned in using deep learning for recommender systems at Netflix. We first provide an overview of the various recommendation tasks on the Netflix service.

The case-study methodology is employed. The study focuses on the innovative and sustainable design elements utilized in three case studies of green buildings—one each in China, Indonesia, and Dubai.

Deep learning accelerators are considered as hardware architec-. ture, which are designed and optimized for increasing speed, efficiency and accuracy. of computers that are running deep learning ...

The central claims of recent deep learning-based models of brain circuits are that they make novel predictions about neural phenomena or shed light on the fundamental functions being optimized. We show, through the case-study of grid cells in the entorhinal-hippocampal circuit, that one may get neither. We begin by reviewing the principles of ...

This paper initially introduces deep learning. The next step in machine learning is deep learning. This paper make deep insightsinto the review of the literature related to deep learning. The papers used various deep learning approaches such as an Autoencoder (AE), convolutional neural network (CNN), deep belief network (DBN), recurrent neural network (RNN). Offshore wind farms are the subject ...

Home Research & Knowledge Strategy Nvidia: Winning the deep-learning leadership battle. The case charts the evolution of NVIDIA, the market leading producer of graphics processing units (GPU), from its beginnings to becoming a leader at the forefront of artificial intelligence (AI) development. Founded in 1993, the company designed processors ...

3D mineral prospectivity modeling using deep adaptation network transfer learning: A case study of the Xiadian gold deposit, Eastern China. Author links open overlay panel Jin ... Zhang et al., 2024) and deep learning methods mainly include convolutional neural networks, long short-term memory networks, generative adversarial networks, and ...

BACKGROUND AND PURPOSE: ASPECTS is a long-standing and well documented selection criteria for acute ischemic stroke treatment, however, the interpretation of ASPECTS is a challenging and time-consuming task for physicians with significant interobserver variabilities. We conducted a multi-reader, multi-case study in which readers assessed ASPECTS without and with the support of a deep learning ...

This study uses Yuelu Mountain National University Science and Technology City as a case study. By constructing an analysis framework that links campus environment with emotional perception and employing street view images and deep learning, it aims to extract and analyze campus environment features and quantitatively evaluate emotional perception.

DRL integrates deep learning with reinforcement learning, allowing intelligent agents to learn optimal behavioural strategies in complex environments. This approach is particularly well-suited for dynamic and uncertain conditions encountered in MG operations. ... 6 CASE STUDY. This section uses a case study to demonstrate the superiority of the ...

As the use of drones continues to increase, their capabilities pose a threat to airspace safety when they are misused. Deploying AI models for intercepting these unwanted drones becomes crucial. However, these AI models, such as deep learning models, often operate as "black boxes", making it hard to trust their decision-making system. This also affects end-users' confidence in these AI ...