Pickl AI

NOVEMBER 20, 2024

This blog discusses key Linear Algebra concepts, their practical applications in data preprocessing and model training, and real-world examples that illustrate how these mathematical principles drive advancements in various Machine Learning tasks. Example In Natural Language Processing (NLP), word embeddings are often represented as vectors.

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IBM Journey to AI blog

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data. What is text mining?

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ODSC - Open Data Science

FEBRUARY 13, 2023

These videos are a part of the ODSC/Microsoft AI learning journe y which includes videos, blogs, webinars, and more. How Deep Neural Networks Work and How We Put Them to Work at Facebook Deep learning is the technology driving today’s artificial intelligence boom.

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Pickl AI

AUGUST 14, 2024

This blog post aims to provide a comprehensive overview of neural networks and their integral role in Machine Learning, covering their history, fundamental concepts, training methodologies, applications, recent advancements, and ethical considerations. Models such as Long Short-Term Memory (LSTM) networks and Transformers (e.g.,

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Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP). For an excellent overview, check out Lilian Weng's blog post. In NLP, Gunel et al. 2020 ; Lewis et al., What's next?  For

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Viso.ai

APRIL 26, 2024

Object detection systems typically use frameworks like Convolutional Neural Networks (CNNs) and Region-based CNNs (R-CNNs). Concept of Convolutional Neural Networks (CNN) However, in prompt object detection systems, users dynamically direct the model with many tasks it may not have encountered before.

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Mlearning.ai

FEBRUARY 28, 2023

Read the full blog here —  [link] Technology Stack HTML, CSS for frontend Flask(Python) for backend Introduction to Flask Flask is a web application framework written in Python. The model uses a convolutional neural network (CNN) to extract features from the images and classify them as healthy or diseased.

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Pickl AI

OCTOBER 2, 2024

This blog will explore the role of AI in Genomic Analysis, its techniques, applications, benefits, challenges, and future prospects. 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.

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John Snow Labs

JUNE 2, 2023

Spark NLP’s deep learning models have achieved state-of-the-art results on sentiment analysis tasks, thanks to their ability to automatically learn features and representations from raw text data. Spark NLP has multiple approaches for detecting the sentiment (which is actually a text classification problem) in a text.

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Topbots

OCTOBER 4, 2023

Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g., spam vs. not spam), while generative NLP models can create new text based on a given prompt (e.g., a social media post or product description).

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Bugra Akyildiz

NOVEMBER 17, 2024

They have shown impressive performance in various computer vision tasks, often outperforming traditional convolutional neural networks (CNNs). The blog post also discusses the challenges faced in implementing interleaving designs and provides recommendations for handling these challenges.

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Pickl AI

JULY 22, 2024

This blog explores the fundamentals of probabilistic models, highlighting their significance and how they differ from deterministic approaches. Natural Language Processing (NLP) In NLP , probabilistic models enhance text understanding and generation. Explore: What is Tokenization in NLP? Everything You Need to Know!

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Mlearning.ai

MAY 30, 2023

We will go into the world of RNNs in this blog and examine their construction, uses, and drawbacks. As we know that Convolutional Neural Network (CNN) is used for structured arrays of data such as image data. RNN is used for sequential data.

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Explosion

AUGUST 22, 2017

This point has been well made by Hal Daumé in a blog post , and reiterated more recently on Twitter by Jason Eisner. import spacy nlp = spacy.load("en_core_web_sm") doc = nlp(u"search for pictures of playful rodents") spacy.displacy.serve(doc) This parse is wrong – it’s analysed “search” as a noun, where it should be a verb.

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Mlearning.ai

MARCH 26, 2023

This blog post aims to provide you with a step-by-step guide to kickstart your data science journey, with a focus on learning through comprehensive projects and hands-on experiences. Learn the basics of neural networks for image datasets and how convolutional neural networks (CNNs) work their magic in image classification tasks.

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Pickl AI

SEPTEMBER 25, 2024

Summary: This blog delves into 20 Deep Learning applications that are revolutionising various industries in 2024. This blog explores 20 significant applications of Deep Learning across various sectors, highlighting their transformative impact. Discover how this powerful AI technology is shaping the future of business and society.

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Pragnakalp

JANUARY 1, 2024

This blog delves into Mistral AI’s open-source model and API, offering a hands-on exploration through code snippets. Compared with traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs), transformers differ in their ability to capture long-range dependencies and contextual information.

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Viso.ai

MARCH 26, 2024

Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. Technologies such as Optical Character Recognition (OCR) and Natural Language Processing (NLP) are foundational to this. 1: Fraud Detection and Prevention No.2:

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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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Mlearning.ai

JUNE 6, 2023

In today’s blog, we will see some very interesting Python Machine Learning projects with source code. Doctor-Patient Appointment System in Python using Flask Hey guys, in this blog we will see a Doctor-Patient Appointment System for Hospitals built in Python using Flask. I myself made this as my final year major project.

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Viso.ai

JUNE 18, 2024

In this blog, we will discover how AI can be used in different tasks and areas of Archeology. Using a Mask R–CNN ( convolutional neural network ) model, they were able to achieve a detection accuracy of 75% and 79.5% Explore our other blogs that explain how AI can be used in different industries or sectors.

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Viso.ai

MARCH 26, 2024

This enhances the interpretability of AI systems for applications in computer vision and natural language processing (NLP). Source ) This has led to groundbreaking models like GPT for generative tasks and BERT for understanding context in Natural Language Processing ( NLP ). Vaswani et al.

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Viso.ai

APRIL 17, 2024

Deep Learning extensively utilizes Convolutional Neural Networks (CNNs) in which convolution operations play a central role in automatic feature extraction. The primary goal of using convolution in image processing is to extract important features from the image and discard the rest.

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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.

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DataRobot Blog

MARCH 9, 2022

This is one of the reasons why detecting sentiment from natural language (NLP or natural language processing ) is a surprisingly complex task. The idea is that instead of performing convolutions on image pixels, the model can instead perform those convolutions in the embedded feature space of the words in a sentence.

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Viso.ai

JULY 9, 2024

In this blog, we’ll explore the concept of transfer learning, how it technically works, and provide a step-by-step guide to implementing it in Python. We can choose a language model like BERT that can parse human text to build an NLP model such as a text summary. Uses in NLP: NLP tasks benefit hugely from transfer learning.

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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? You can find an interactive version online.

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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. By integrating a text classification model in Comet, you can unlock the full potential of machine learning and NLP in your text analysis workflows. You can get the dataset here.

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Viso.ai

JULY 9, 2024

Discriminative models include a wide range of models, like Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Support Vector Machines (SVMs), or even simpler models like random forests. For example, in Natural Language Processing (NLP), the model works by predicting the next word in a sequence.

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Viso.ai

MAY 6, 2024

In particular, researchers are working with the following deep learning models to enhance spatio-temporal action recognition systems: Convolutional Neural Networks (CNNs) In a basic sense, spatial recognition systems use CNNs to extract features from pixel data. This is due to their ability to handle long-range dependencies.

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Snorkel AI

MARCH 1, 2023

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al. 2016) This paper introduced DCGANs, a type of generative model that uses convolutional neural networks to generate images with high fidelity. Attention Is All You Need Vaswani et al.

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Viso.ai

DECEMBER 20, 2023

However, unsupervised learning has its own advantages, such as being more resistant to overfitting (the big challenge of Convolutional Neural Networks ) and better able to learn from complex big data, such as customer data or behavioral data without an inherent structure.

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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 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.

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Pickl AI

SEPTEMBER 23, 2024

This blog will equip you with the most important Deep Learning interview questions, ranging from basic to advanced and scenario-based inquiries. In image recognition, Convolutional Neural Networks (CNNs) can accurately identify objects and faces in images. Applications Deep Learning powers many real-world applications.

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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. We pay our contributors, and we don’t sell ads.

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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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Pickl AI

SEPTEMBER 18, 2023

PixelRNN models generate pixels sequentially, while PixelCNN models use a convolutional neural network to model the conditional distribution of each pixel. Conclusion In conclusion, the above blog presents you with an in-depth understanding of what is Generative AI and how it works. Can an AI model generate data?

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