ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out his talk, “ Bagging to BERT — A Tour of Applied NLP ,” there! If a Natural Language Processing (NLP) system does not have that context, we’d expect it not to get the joke. I’ll be making use of the powerful SpaCy library which makes swapping architectures in NLP pipelines a breeze. It’s all about context!

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ODSC - Open Data Science

FEBRUARY 10, 2025

adults use only work when they can turn audio data into words, and then apply natural language processing (NLP) to understand it. Mono sound channels are the best option for speech intelligibility , so theyre ideal for NLP applications, but stereo inputs will improve copyright detection use cases.

Machine Learning 52

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ODSC - Open Data Science

FEBRUARY 13, 2023

The field of data science changes constantly, and some frameworks, tools, and algorithms just can’t get the job done anymore. We will take a gentle, detailed tour through a multilayer fully-connected neural network, backpropagation, and a convolutional neural network.

Neural Network 98

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ODSC - Open Data Science

JULY 18, 2023

Here at ODSC, we couldn’t be more excited to announce Microsoft Azure’s tutorial series on Deep Learning and NLP, now available for free on Ai+. Originally posted on OpenDataScience.com Read more data science articles on OpenDataScience.com , including tutorials and guides from beginner to advanced levels!

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Mlearning.ai

MARCH 26, 2023

Embark on Your Data Science Journey through In-Depth Projects and Hands-on Learning Photo by Wes Hicks on Unsplash Data science, as an emerging field, is constantly evolving and bringing forth innovative solutions to complex problems. I’ve handpicked a few Kaggle projects covering a range of data science concepts.

Data Science 52

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Heartbeat

JUNE 6, 2023

In recent years, researchers have also explored using GCNs for natural language processing (NLP) tasks, such as text classification , sentiment analysis , and entity recognition. GCNs use a combination of graph-based representations and convolutional neural networks to analyze large amounts of textual data.

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IBM Journey to AI blog

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. Machine learning algorithms like Naïve Bayes and support vector machines (SVM), and deep learning models like convolutional neural networks (CNN) are frequently used for text classification.

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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).

Computer Vision 52

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ODSC - Open Data Science

SEPTEMBER 17, 2024

Mastery of these AI frameworks for software engineering, and other emerging tools, not only enhances your skillset but also opens up a world of opportunities in data science and AI. LightGBM’s ability to handle large-scale data with lightning speed makes it a valuable tool for engineers working with high-dimensional data.

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Marktechpost

DECEMBER 12, 2024

Despite their simplicity, they lack the ability to model temporal or sequential data due to the absence of memory elements or feedback loops. Convolutional Neural Networks (CNNs) : Convolutional Neural Networks (CNNs) are specifically designed for processing grid-like data such as images or time-series data.

Neural Network 57

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Viso.ai

DECEMBER 21, 2023

From the development of sophisticated object detection algorithms to the rise of convolutional neural networks (CNNs) for image classification to innovations in facial recognition technology, applications of computer vision are transforming entire industries. Here, she studied statistics, neuroscience, and psychology.

Convolutional Neural Networks 52

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Heartbeat

MARCH 8, 2023

Heartbeat these past few weeks has had lots of great articles covering the latest research, NLP use-cases, and Comet tutorials. Happy Reading, Emilie, Abby & the Heartbeat Team Natural Language Processing With SpaCy (A Python Library) — by Khushboo Kumari This post goes over how the most cutting-edge NLP software, SpaCy , operates.

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Pickl AI

FEBRUARY 1, 2023

Thus it reduces the amount of data and computational need. Transfer Learning has various applications like computer vision, NLP, recommendation systems, and robotics. Examples of Transfer Learning in Deep Learning include: Using a pre-trained image classification network for a new image classification task with a similar dataset.

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The MLOps Blog

NOVEMBER 14, 2023

Example of a deep learning visualization: small convolutional neural network CNN, notice how the thickness of the colorful lines indicates the weight of the neural pathways | Source How is deep learning visualization different from traditional ML visualization? How do you adopt deep learning model visualization?

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Heartbeat

JANUARY 12, 2023

Other practical examples of deep learning include virtual assistants, chatbots, robotics, image restoration, NLP (Natural Language Processing), and so on. Convolution, pooling, and fully connected layers are just a few components that make up a convolutional neural network.

Deep Learning 52

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Pickl AI

AUGUST 14, 2024

Neural networks come in various forms, each designed for specific tasks: Feedforward Neural Networks (FNNs) : The simplest type, where connections between nodes do not form cycles. Data moves in one direction—from input to output. Models such as Long Short-Term Memory (LSTM) networks and Transformers (e.g.,

Neural Network 52

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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.

Computer Vision 52

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Pickl AI

OCTOBER 2, 2024

Read More: Supervised Learning vs Unsupervised Learning Deep Learning Deep Learning is a subset of Machine Learning that uses neural networks with multiple layers to analyse complex data patterns. It has shown great promise in Genomic Analysis due to its ability to handle high-dimensional data.

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Heartbeat

SEPTEMBER 20, 2023

Image captioning integrates computer vision, which interprets visual information, and NLP, which produces human language. 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.

Natural Language Processing 52

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Pickl AI

JULY 22, 2024

Summary: Probabilistic model in Machine Learning handle uncertainty and complex data structures, improving decision-making and predictions. Introduction Machine Learning models are essential tools in Data Science , designed to predict outcomes and uncover patterns from data. Explore: What is Tokenization in NLP?

Machine Learning 52

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Heartbeat

AUGUST 22, 2023

It’s an essential task in natural language processing (NLP) and machine learning, with applications ranging from sentiment analysis to spam detection. Machine learning models like ANNs need to be trained on labeled data to perform text classification. You can get the dataset here.

Neural Network 52

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Pickl AI

DECEMBER 10, 2024

Image Data Image features involve identifying visual patterns like edges, shapes, or textures. Methods like Histogram of Oriented Gradients (HOG) or Deep Learning models, particularly Convolutional Neural Networks (CNNs), effectively extract meaningful representations from images.

Machine Learning 52

Machine Learning Convolutional Neural Networks Categorization Deep Learning 52

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 applications like NLP and video analysis, they’ve performed admirably. Similar to LSTMs but with fewer parameters, Gated recurrent units (GRUs) are another kind of RNN.

Deep Learning 52

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Heartbeat

APRIL 23, 2023

Photo by RetroSupply on Unsplash Introduction Deep learning has been widely used in various fields, such as computer vision, NLP, and robotics. The success of deep learning is largely due to its ability to learn complex representations from data using deep neural networks.

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Viso.ai

SEPTEMBER 2, 2024

In this article, we introduce several positions in the world of computer vision (CV) aside from computer vision engineers, or AI/machine learning/data science specialists. Experience with classical computer vision tools, such as OpenCV , object detection, image segmentation, data annotation, etc.

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Pickl AI

SEPTEMBER 25, 2024

Natural Language Processing (NLP) NLP applications powered by Deep Learning have transformed how machines understand human language. Sentiment Analysis Businesses use NLP to gauge customer sentiment from social media posts or reviews by analysing text data.

Deep Learning 52

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Explosion

AUGUST 3, 2017

We’ve been working on Prodigy since we first launched Explosion last year, alongside our open-source NLP library spaCy and our consulting projects (it’s been a busy year!). Data science projects are said to have uneven returns, like start-ups: a minority of projects are very successful, recouping costs for a larger number of failures.

Python 52

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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.

Software Development 52

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Heartbeat

OCTOBER 12, 2023

These models, powered by massive neural networks, have catalyzed groundbreaking advancements in natural language processing (NLP) and have reshaped the landscape of machine learning. They owe their success to many factors, including substantial computational resources, vast training data, and sophisticated architectures.

Large Language Models 59

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Pickl AI

SEPTEMBER 23, 2024

Backpropagation: This algorithm adjusts the neural network’s weights during training by calculating the error at the output layer and propagating it backwards through the network, optimising the model. In image recognition, Convolutional Neural Networks (CNNs) can accurately identify objects and faces in images.

Deep Learning 52

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Heartbeat

FEBRUARY 8, 2024

Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively. Building the Model Deep learning techniques have proven to be highly effective in performing cross-modal retrieval.

Neural Network 52

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Mlearning.ai

JUNE 6, 2023

We will use LSTM for this project because of the fact that the data is Sequential. Emotion Detector using Keras In this blog, we will be building an Emotion Detector model in Keras using Convolutional Neural Networks. This is one of my favorite projects. We have custom-made the architecture in this project.

Machine Learning 52

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Lexalytics

APRIL 5, 2021

I was out of the neural net biz. Fast-forward a couple of decades: I was (and still am) working at Lexalytics, a text-analytics company that has a comprehensive NLP stack developed over many years. The CNN was a 6-layer neural net with 132 convolution kernels and (don’t laugh!) Hinton (again!) and BERT.

Neural Network 52

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