Heartbeat

NOVEMBER 21, 2023

One of the most significant breakthroughs in this field is the convolutional neural network (CNN). In stark contrast, deep learning algorithms take a radically different approach, particularly convolutional neural networks (CNNs).

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Pickl AI

JANUARY 26, 2025

Summary: Deep Learning models revolutionise data processing, solving complex image recognition, NLP, and analytics tasks. These models mimic the human brain’s neural networks, making them highly effective for image recognition, natural language processing, and predictive analytics. billion in 2025 to USD 34.5

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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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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

NLP 52

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Pickl AI

NOVEMBER 22, 2024

This blog will explore the architecture, functioning, applications, and advantages of LSTMs, providing a comprehensive understanding of this powerful deep learning model. Understanding Recurrent Neural Networks (RNNs) To appreciate LSTMs, it’s essential to understand RNNs. In What Applications Are LSTMS Commonly Used?

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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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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.

Deep Learning 76

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Pickl AI

JANUARY 29, 2025

In this blog, we will explore over ten beginner-friendly Deep Learning projects that will help you apply what you’ve learned and develop new skills. Cat vs. Dog Classification This project involves building a Convolutional Neural Network (CNN) to classify images as either cats or dogs.

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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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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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Pickl AI

SEPTEMBER 16, 2024

Summary : Deep Belief Networks (DBNs) are Deep Learning models that use Restricted Boltzmann Machines and feedforward networks to learn hierarchical features and model complex data distributions. They are effective in image recognition, NLP, and speech recognition. They can also model semantic structures and syntactic patterns.

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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.

Convolutional Neural Networks 52

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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.,

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Pickl AI

FEBRUARY 7, 2025

This blog explores the difference between Machine Learning and Deep Learning , highlighting their unique characteristics, benefits, and challenges. On the other hand, Deep Learning relies heavily on neural networks, especially deep neural networks (DNNs), which consist of multiple layers of nodes designed to simulate the human brain.

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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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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.

Neural Network 52

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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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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.

Data Science 52

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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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Pickl AI

DECEMBER 5, 2024

This blog will delve deeper into what epochs are, how they function within Machine Learning frameworks, and their implications for model performance. Natural Language Processing (NLP) NLP tasks often require extensive epochs for models like Transformers to grasp context and grammar nuances effectively.

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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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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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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.

Machine Learning 52

Machine Learning Python Computer Vision Convolutional Neural Networks 52

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:

Computer Vision 52

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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.

Machine Learning 52

Machine Learning Neural Network Natural Language Processing Convolutional Neural Networks 52

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!

Machine Learning 52

Machine Learning Natural Language Processing Convolutional Neural Networks NLP 52

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.

Neural Network 52

Neural Network Natural Language Processing Convolutional Neural Networks NLP 52

Pickl AI

DECEMBER 9, 2024

This blog aims to clarify the concept of inductive bias and its impact on model generalisation, helping practitioners make better decisions for their Machine Learning solutions. Let’s explore how it impacts key areas like image classification, natural language processing (NLP), and recommendation systems.

Machine Learning 52

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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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Deep Learning Computer Vision Neural Network Natural Language Processing 52

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

Natural Language Processing Computer Vision NLP Algorithm 52

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.

Convolutional Neural Networks 52

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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.

Computer Vision 52

Computer Vision Convolutional Neural Networks Robotics AI 52

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.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Pickl AI

DECEMBER 10, 2024

This blog will explore the importance of feature extraction, its techniques, and its impact on model efficiency and accuracy. 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 Automation 52

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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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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ML Review

FEBRUARY 11, 2019

Understanding building blocks of ULMFIT Last week I had the time to tackle a Kaggle NLP competition: Quora Insincere Questions Classification. For a general overview for all the above models and more I encourage you to read this amazing blog post in order to get a deeper understanding in language modeling methodologies and advancements.

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