Artificial Corner

JANUARY 23, 2025

2 Python for Data Analysis Course This one is more like a playlist than a course; however, you will find more useful lectures in this playlist than in some paid courses. The first 8 videos in the playlist make a 10-hour data analysis course. 4 Machine Learning & Artificial Intelligence with Tensorflow 2.0

Data Science 95

Data Science Python Convolutional Neural Networks Natural Language Processing 95

IBM Journey to AI blog

AUGUST 28, 2023

Text analysis takes it a step farther by focusing on pattern identification across large datasets, producing more quantitative results. Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs.

Data Mining 170

Data Mining Convolutional Neural Networks Categorization Machine Learning 170

Pickl AI

AUGUST 3, 2023

Introduction to Deep Learning Algorithms: Deep learning algorithms are a subset of machine learning techniques that are designed to automatically learn and represent data in multiple layers of abstraction. This process is known as training, and it relies on large amounts of labeled data. How Deep Learning Algorithms Work?

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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.

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Viso.ai

DECEMBER 20, 2023

Supervised learning is a type of machine learning algorithm that learns from a set of training data that has been labeled training data. Typical computer vision tasks of supervised learning algorithms include object detection, visual recognition, and classification. for image data compression). to an image.

Computer Vision 98

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AWS Machine Learning Blog

DECEMBER 14, 2023

The SageMaker semantic segmentation built-in algorithm is used to train models for screener grid area segmentation. After experimenting with different configurations and parameters, ICL used a Fully Convolutional Network (FCN) algorithm with a pyramid scene parsing network (PSPNet) to train the model.

Computer Vision 107

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Viso.ai

OCTOBER 7, 2024

For truly solving real-world scenarios, organizations require more than just a computer vision tool or algorithm. Object Detection : Computer vision algorithms, such as convolutional neural networks (CNNs), analyze the images to identify and classify waste types (i.e., However, this algorithm has few limitations.

Convolutional Neural Networks 52

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Viso.ai

OCTOBER 10, 2024

Pattern recognition is the ability of machines to identify patterns in data, and then use those patterns to make decisions or predictions using computer algorithms. Pattern Recognition in Data Analysis What is Pattern Recognition? The data inputs can be words or texts, images, or audio files.

Neural Network 98

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Chatbots Life

APRIL 16, 2024

Examples of Generative Models Generative models encompass various algorithms that capture patterns in data to generate realistic new examples. Examples of Discriminative Models Discriminative models encompass a range of algorithms that excel in diverse tasks such as classification and sequence analysis.

Neural Network 59

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Viso.ai

OCTOBER 10, 2024

We will elaborate on computer vision techniques like Convolutional Neural Networks (CNNs). They enable pattern recognition and feature extraction from satellite visual data. Data Labeling The enormous diversity of objects and landscapes visible from orbit requires enormous volumes of labeled data.

Computer Vision 52

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Becoming Human

MAY 15, 2023

These communities will help you to be updated in the field, because there are some experienced data scientists posting the stuff, or you can talk with them so they will also guide you in your journey. Data Analysis After learning math now, you are able to talk with your data.

Data Science 75

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Pickl AI

JULY 28, 2023

In order to teach Machine Learning algorithms and teach the system different visual patterns, large datasets of labeled photos are used in this process. Numerous industries have undergone a revolution because of their quick improvements, which have also greatly improved automation and visual data analysis capabilities.

Machine Learning 52

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NVIDIA

FEBRUARY 7, 2024

The AUV’s onboard energy-efficient computing also powers convolutional neural networks that enhance underwater vision by reducing backscatter and correcting colors. This enables CUREE to transmit clear images to scientists, facilitating fish detection and reef analysis.

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Viso.ai

DECEMBER 1, 2023

In short, supervised learning is where the algorithm is given a set of training data. Supervised models learn from ground truth data that was labeled manually by data scientists. The model uses this data to learn (AI training) how to make predictions on new data (AI inferencing).

AI Modeling 98

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Mlearning.ai

JANUARY 28, 2023

Pandas: Pandas is a powerful data analysis library that makes it easy to work with datasets of any size or shape. It offers various data type conversions and aggregations as well as powerful plotting capabilities. Spark: Spark is a popular platform used for big data processing in the Hadoop ecosystem.

Data Science 52

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Pickl AI

SEPTEMBER 3, 2024

Here are some core responsibilities and applications of ANNs: Pattern Recognition ANNs excel in recognising patterns within data , making them ideal for tasks such as image recognition, speech recognition, and natural language processing. Predictive Modelling ANNs can be used to make predictions based on historical data.

Neural Network 52

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Viso.ai

DECEMBER 6, 2024

After that, they utilize specialized algorithms to identify trends, predict outcomes, and absorb fresh data. It is achieved by computer vision algorithms. Semantic segmentation real-time data analysis requires scene comprehension and visual signal processing. The eyes of the automobile are computer vision models.

Computer Vision 52

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Pickl AI

OCTOBER 2, 2024

Genomic Analysis is crucial for various applications, including personalised medicine, cancer research, genetic disorder diagnosis, and evolutionary biology. AI Techniques Used in Genomic Analysis AI encompasses a range of techniques that can be applied to genomic Data Analysis.

Neural Network 52

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Dlabs.ai

OCTOBER 23, 2019

PyBrain , for example, is a modular ML library that helps developers build algorithms, then test and compare solutions in predefined environments. These days, it’s possible to build and train an algorithm using any general-purpose programming language you want. Whereas a language such as JavaScript? …and that includes Javascript.

Machine Learning 52

Machine Learning Convolutional Neural Networks Python ML 52

Pickl AI

FEBRUARY 7, 2025

Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables machines to improve their task performance by learning from data rather than following explicit instructions. ML algorithms use statistical methods to identify patterns in data, allowing systems to make predictions or decisions without human intervention.

Deep Learning 52

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John Snow Labs

APRIL 30, 2024

By recognizing tables, OCR can convert this data into a format easily manipulatable and analyzable, such as a spreadsheet or a database. This opens up possibilities for data analysis, machine learning, and other data-driven tasks. It’s based on an image processing algorithm that detects horizontal and vertical lines.

Deep Learning 52

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Pickl AI

SEPTEMBER 25, 2024

Medical Image Analysis Deep Learning algorithms analyse medical images such as X-rays, MRIs, and CT scans to detect anomalies like tumours or fractures. Algorithmic Trading AI-driven trading systems use Deep Learning to analyse market trends and execute trades at optimal times.

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Heartbeat

OCTOBER 25, 2023

The field demands a unique combination of computational skills and biological knowledge, making it a perfect match for individuals with a data science and machine learning background. Traditional computational infrastructure may not be sufficient to handle the vast amounts of data generated by high-throughput technologies.

Machine Learning 52

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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. 2: Automated Document Analysis and Processing No.3: 4: Algorithmic Trading and Market Analysis No.5: 2: Automated Document Analysis and Processing No.3:

Computer Vision 52

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Pickl AI

DECEMBER 10, 2024

Raw data, such as images or text, often contain irrelevant or redundant information that hinders the model’s performance. By extracting key features, you allow the Machine Learning algorithm to focus on the most critical aspects of the data, leading to better generalisation.

Machine Learning 52

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Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field.

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Pickl AI

JULY 12, 2024

Jupyter notebooks are widely used in AI for prototyping, data visualisation, and collaborative work. Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. Importance of Data in AI Quality data is the lifeblood of AI models, directly influencing their performance and reliability.

Artificial Intelligence 52

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John Snow Labs

MAY 23, 2023

This is because NLP technology enables the VQA algorithm to not only understand the question posed to it about the input image, but also to generate an answer in a language that the user (asking the question) can easily understand. Two other possible applications of VQA are self-driving cars and data analysis.

Natural Language Processing 40

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Viso.ai

OCTOBER 17, 2024

John Hopfield networks can recognize images, even if they’re distorted or incomplete. They are also useful for search algorithms where you need to find the best solution among many possibilities, like finding the shortest route. This algorithm is like a feedback mechanism for neural networks.

Neural Network 52

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Viso.ai

OCTOBER 10, 2024

When we integrate computer vision algorithms with geospatial intelligence, it helps automate large volumes of spatial data analysis. A vast amount of spatial data is now manageable by traditional GIS systems, but when those are coupled with AI algorithms, their capabilities are greatly increased.

Computer Vision 52

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Viso.ai

NOVEMBER 11, 2024

Those models are based on convolutional neural networks (CNNs) which are a popular type of artificial neural networks (ANNs) that work great for vision tasks like classification and detection. This requires using a variety of path-learning algorithms to optimize against certain criteria.

Computer Vision 52

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PyImageSearch

SEPTEMBER 16, 2024

Home Table of Contents Credit Card Fraud Detection Using Spectral Clustering Understanding Anomaly Detection: Concepts, Types and Algorithms What Is Anomaly Detection? Jump Right To The Downloads Section Understanding Anomaly Detection: Concepts, Types, and Algorithms What Is Anomaly Detection? Looking for the source code to this post?

Computer Vision 55

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Viso.ai

MAY 10, 2024

Data cleaning If we gather data using the second or third approach described above, then it’s likely that there will be some amount of corrupted, mislabeled, incorrectly formatted, duplicate, or incomplete data that was included in the third-party datasets. text vs images) and (2) the desired output (e.g.

Machine Learning 52

Machine Learning Computer Vision Neural Network Convolutional Neural Networks 52

Unite.AI

JULY 21, 2023

For example, when implemented correctly, the image recognition algorithm can identify & label the dog in the image. Fundamentally, an image recognition algorithm generally uses machine learning & deep learning models to identify objects by analyzing every individual pixel in an image. How Image Recognition Works?

Computer Vision 244

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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.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

John Snow Labs

MAY 8, 2024

The potential of LLMs, in the field of pathology goes beyond automating data analysis. in 2017 highlighted this by demonstrating a deep learning algorithm’s ability to classify skin cancer with accuracy comparable to that of human dermatologists, based on an extensive dataset of 129,450 clinical images.

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