NYU Center for Data Science

JANUARY 31, 2024

“We wanted to create a tool to analyze learning dynamics,” Hu explained. The approach of Hu and his co-authors — CDS PhD student Angelica Chen , Naomi Saphra , and CDS Professor of Computer Science and Data Science Kyunghyun Cho  — was partly inspired by historical theories of human psychological development.

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Heartbeat

DECEMBER 20, 2023

You then repeat that loop for each layer in your network. But because you’re passing individual pixel values through the network, how the network learns becomes very specific. Andrew Jones of Data Science Infinity Imagine that you train a network to recognize pictures of a statue.

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Unite.AI

NOVEMBER 6, 2023

Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning. For instance, NN used for computer vision tasks (object detection and image segmentation) are called convolutional neural networks (CNNs) , such as AlexNet , ResNet , and YOLO.

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AWS Machine Learning Blog

DECEMBER 7, 2023

Calculating courier requirements The first step is to estimate hourly demand for each warehouse, as explained in the Algorithm selection section. CNN-QR is a proprietary ML algorithm developed by Amazon for forecasting scalar (one-dimensional) time series using causal Convolutional Neural Networks (CNNs).

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Heartbeat

DECEMBER 14, 2023

He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University and Vice-President, Chief AI Scientist at Meta.He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets.

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Heartbeat

APRIL 4, 2023

This comprehensive article will explain the fundamentals of neural style transfer (NST), provide an overview of the techniques used for performing NST, and discuss some best use cases for the application. It creates an intermediate representation of both images using a convolutional neural network (CNN).

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Heartbeat

AUGUST 15, 2023

Advanced techniques have been devised to address these challenges, such as deep learning, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Specifically, we will discuss CNNs, RNNs, data augmentation, transfer learning, and ensemble models.

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Heartbeat

NOVEMBER 27, 2023

Convolutional neural networks (CNNs) can learn complicated patterns and features from enormous datasets, emulating the human visual system. Convolutional Neural Networks (CNNs) Deep learning in medical image analysis relies on CNNs. Deep learning automates and improves medical picture analysis.

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Heartbeat

OCTOBER 25, 2023

Bioinformatics: A Haven for Data Scientists and Machine Learning Engineers: Bioinformatics offers an unparalleled opportunity for data scientists and machine learning engineers to apply their expertise in solving complex biological problems.

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Heartbeat

AUGUST 14, 2023

Deep Learning-based Segmentation: Deep learning-based segmentation is a more recent technique that has gained popularity in recent years, particularly with the advent of convolutional neural networks (CNNs). CNNs can learn to segment images by training on large datasets of labeled images.

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Heartbeat

DECEMBER 8, 2023

He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University and Vice-President, Chief AI Scientist at Meta.He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets.

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ODSC - Open Data Science

MARCH 8, 2024

Definition The Vision Transformer (ViT) emerged as an alternative to Convolutional Neural Networks (CNNs). This means that users can enjoy a multimodal experience, explaining concepts with both written and visual inputs. You can also get data science training on-demand wherever you are with our Ai+ Training platform.

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Heartbeat

MAY 30, 2023

A GAN’s generator network is usually composed of multiple layers of convolutional and deconvolutional neural networks, while the discriminator network also employs convolutional neural networks to learn to differentiate between real and generated samples.

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Heartbeat

MAY 22, 2023

Schematic diagram of the overall framework of Emotion Recognition System [ Source ] The models that are used for AI emotion recognition can be based on linear models like Support Vector Machines (SVMs) or non-linear models like Convolutional Neural Networks (CNNs).

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Heartbeat

JULY 24, 2023

Overview of Neural Style Transfer Neural style transfer is a technology that blends two images’ content and style to produce a new image incorporating both features. In order to extract the content and stylistic features from the input photos, a pre-trained convolutional neural network (CNN) is used in the process.

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Heartbeat

JUNE 6, 2023

GCNs use a combination of graph-based representations and convolutional neural networks to analyze large amounts of textual data. A GCN consists of multiple layers, each of which applies a graph convolution operation to the input graph.

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Heartbeat

JULY 6, 2023

Object detection is typically achieved through the use of deep learning models, particularly Convolutional Neural Networks (CNNs). Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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Heartbeat

MARCH 14, 2023

photo from Data Science Central Industrial automation, security and surveillance, and service robots are just a few examples of fields that might benefit from robotics’ ability to identify and track objects. We’re committed to supporting and inspiring developers and engineers from all walks of life.

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Pickl AI

JULY 14, 2023

Big Data and Deep Learning (2010s-2020s): The availability of massive amounts of data and increased computational power led to the rise of Big Data analytics. Deep Learning, a subfield of ML, gained attention with the development of deep neural networks. They promote and enhance the state of the art in AI.

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Heartbeat

MARCH 5, 2024

The primary goal of image augmentation is to improve the robustness and generalization of machine learning models, particularly convolutional neural networks (CNNs), when training on a limited amount of data. We're committed to supporting and inspiring developers and engineers from all walks of life.

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Viso.ai

MAY 10, 2024

It can also help reduce the dimensionality of the input which can help to avoid overfitting in situations where we don’t have as much training data as we would like. It often helps to do some exploratory data science. Try running some basic statistical analyses on your data. How do we perform feature engineering?

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Heartbeat

JANUARY 15, 2024

Convolutional Neural Networks (CNNs) have enthralled audiences with their prodigious capacity to extract nuanced patterns and features from massive volumes of data. CNNs orchestrate a symphony of convolutions, pooling, and activations to create representations that reveal the essence of the visual world.

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Heartbeat

APRIL 11, 2023

The core of our model will be a pre-trained convolutional neural network (CNN) named VGG16. Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

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Heartbeat

JUNE 23, 2023

However, with the advent of deep learning, researchers have explored various neural network architectures to model and forecast time series data. In this post, we will look at deep learning approaches for time series analysis and how they might be used in real-world applications.

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Heartbeat

MARCH 28, 2023

After finishing the project, our app will look like this: Image Classification App (Video by Author) Introduction Before diving into the project, let me explain the libraries I’m going to use in this analysis. Model Building Convolutional neural network (CNN) is a deep learning technique often used to extract patterns in visual data.

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Heartbeat

JUNE 27, 2023

Neural networks leverage the structure and properties of graph and work in a similar fashion. Graph Neural Networks are a class of artificial neural networks that can be represented as graphs. Similarly, RNNs can be applied to graph structures where each node is represented by a word.

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Heartbeat

MAY 2, 2023

To train a neural network you give the network a set of data and it compares the output with the desired output, the difference between the two will be known as an error which will be used in adjusting the weights till the outputs align.

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Heartbeat

SEPTEMBER 20, 2023

Object Detection Image from a personal computer Convolutional neural networks (CNNs) are utilized in object detection algorithms to identify and locate objects based on their visual attributes accurately. These algorithms can learn and extract intricate features from input images by using convolutional layers.

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Heartbeat

SEPTEMBER 25, 2023

Convolutional Neural Networks (CNNs), a machine learning algorithm, has successfully identified and classified abnormalities, lesions, tumors, and other critical features in medical images such as X-rays, MRIs, CT scans, and mammograms.

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Heartbeat

NOVEMBER 21, 2023

Deep learning, inspired by the structure and function of the human brain, empowers machines to learn intricate representations and features directly from the data automatically. One of the most significant breakthroughs in this field is the convolutional neural network (CNN).

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Mlearning.ai

DECEMBER 13, 2023

Explain the vanishing and exploding gradient problems. Vanishing Gradient: When training a neural network, sometimes the updates to the weights get too small. Difference: Traditional neural networks usually have a single goal, like classification or regression. Explain the concept of batch normalization.

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Heartbeat

AUGUST 29, 2023

Types of Deep Learning Approaches A variety of methods and designs are used to train neural networks under the umbrella of deep learning. Some of the symbolic approaches of deep learning are listed below: CNNs (Convolutional Neural Networks) : CNNs are frequently employed in image and video recognition jobs.

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The MLOps Blog

NOVEMBER 14, 2023

Visualizing deep learning models can help us with several different objectives: Interpretability and explainability: The performance of deep learning models is, at times, staggering, even for seasoned data scientists and ML engineers. DFF identifies regions in the network’s feature space that belong to the same semantic concept.

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Pickl AI

JANUARY 15, 2023

and how it works in the Data Science field. Explaining through examples of Deep Learning, you might find yourself searching for career prospects in the domain as well. Multi-layer Perceptron Within this type of network, there are more than 3 layers to classify data which is not linear. What is Deep Learning in AI?

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Mlearning.ai

FEBRUARY 2, 2023

In this article, I show how a Convolutional Neural Network can be used to predict a person's age based on the person's ECG Attia et al 2019 [1], showed that a person's age could be predicted from an ECG using convolutional neural networks (CNN). Data Min Knowl Disc 34 , 1936–1962 (2020).

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Heartbeat

FEBRUARY 6, 2023

In the second step, these potential fields are classified and corrected by the neural network model. R-CNN (Regions with Convolutional Neural Networks) and similar two-stage object detection algorithms are the most widely used in this regard. However, this algorithm is slower than other algorithms.

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Heartbeat

FEBRUARY 22, 2023

Deep learning models can extract different, and often more useful, features compared to traditional machine learning models for several reasons: Depth Deep learning models, especially Convolutional Neural Networks (CNNs), have multiple layers that can learn hierarchical representations of the input data.

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