Unite.AI

NOVEMBER 6, 2023

Traditional machine learning is a broad term that covers a wide variety of algorithms primarily driven by statistics. The two main types of traditional ML algorithms are supervised and unsupervised. These algorithms are designed to develop models from structured datasets. Do We Still Need Traditional Machine Learning Algorithms?

Machine Learning 204

Machine Learning Neural Network Deep Learning Convolutional Neural Networks 204

Marktechpost

MAY 5, 2024

Machine Learning with Python This course covers the fundamentals of machine learning algorithms and when to use each of them. The course covers numerous algorithms of supervised and unsupervised learning and also teaches how to build neural networks using TensorFlow. and evaluating the same.

Machine Learning 120

Machine Learning Python Neural Network Convolutional Neural Networks 120

ODSC - Open Data Science

SEPTEMBER 18, 2024

Convolutional neural networks (CNNs) differ from conventional, fully connected neural networks (FCNNs) because they process information in distinct ways. CNNs use a three-dimensional convolution layer and a selective type of neuron to compute critical artificial intelligence processes.

Convolutional Neural Networks 40

Convolutional Neural Networks Neural Network Data Scientist Computer Vision 40

AWS Machine Learning Blog

DECEMBER 7, 2023

Amazon Forecast is a fully managed service that uses machine learning (ML) algorithms to deliver highly accurate time series forecasts. Calculating courier requirements The first step is to estimate hourly demand for each warehouse, as explained in the Algorithm selection section.

Convolutional Neural Networks 125

Convolutional Neural Networks Algorithm Neural Network Data Science 125

Becoming Human

MAY 15, 2023

Data Science is a popular as well as vast field; till date, there are a lot of opportunities in this field, and most people, whether they are working professionals or students, everyone want a transition in data science because of its scope. How much to learn? What to do next?

Data Science 75

Data Science Neural Network Machine Learning Python 75

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

Neural Network Convolutional Neural Networks Machine Learning Deep Learning 98

ODSC - Open Data Science

FEBRUARY 10, 2025

Choose an Appropriate Algorithm As with all machine learning processes, algorithm selection is also crucial. Convolutional neural networks offer high accuracy in video analysis but require considerable amounts of data. The best type of model depends on what you want your A/V analysis to accomplish.

Machine Learning 52

Machine Learning Convolutional Neural Networks Neural Network Natural Language Processing 52

Heartbeat

MAY 2, 2023

Deep learning allows machines to learn from vast amounts of data and carry out complex tasks that were previously only considered possible by humans (like translation between languages, recognizing objects etc.). In this article, we will examine some different popular deep learning algorithms and how they operate.

Deep Learning 52

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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

Data Science Neural Network Convolutional Neural Networks Machine Learning 52

Towards AI

APRIL 7, 2024

Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst? For example, it takes millions of images and runs them through a training algorithm.

Machine Learning 105

Machine Learning Neural Network Convolutional Neural Networks Deep Learning 105

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. 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

Mlearning.ai

JANUARY 28, 2023

From Sale Marketing Business 7 Powerful Python ML For Data Science And Machine Learning need to be use. The data-driven world will be in full swing. With the growth of big data and artificial intelligence, it is important that you have the right tools to help you achieve your goals. To perform data analysis 6.

Data Science 52

Data Science Machine Learning ML Python 52

Marktechpost

OCTOBER 4, 2024

Time series analysis is a complex & challenging domain in data science, primarily due to the sequential nature and temporal dependencies inherent in the data. Prominent models include Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN).

Neural Network 104

Neural Network Machine Learning Convolutional Neural Networks Categorization 104

AWS Machine Learning Blog

MAY 15, 2023

Getir used Amazon Forecast , a fully managed service that uses machine learning (ML) algorithms to deliver highly accurate time series forecasts, to increase revenue by four percent and reduce waste cost by 50 percent. Solution overview Six people from Getir’s data science team and infrastructure team worked together on this project.

Neural Network 84

Neural Network Convolutional Neural Networks Metadata Data Scientist 84

Towards AI

APRIL 27, 2023

Understanding the Principles, Challenges, and Applications of Gradient Descent Image by Author with @MidJourney Introduction to Gradient Descent Gradient descent is a fundamental optimization algorithm used in machine learning and data science to find the optimal values of the parameters in a model.

Machine Learning 98

Machine Learning Neural Network Convolutional Neural Networks Algorithm 98

ODSC - Open Data Science

NOVEMBER 22, 2024

By integrating data from 12 GPS satellites in medium-Earth orbit and one Los Alamos satellite in geosynchronous orbit, the model leverages artificial intelligence to significantly improve the accuracy of space weather predictions. It also highlights the importance of long-term space observations in the age of AI. ”

Convolutional Neural Networks 75

Convolutional Neural Networks Neural Network Machine Learning Big Data 75

Heartbeat

NOVEMBER 14, 2023

Integrating XGboost with Convolutional Neural Networks Photo by Alexander Grey on Unsplash XGBoost is a powerful library that performs gradient boosting. It has an excellent reputation as a tool for predicting many kinds of problems in data science and machine learning. It was envisioned by Thongsuwan et al.,

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network Machine Learning 52

AWS Machine Learning Blog

DECEMBER 14, 2023

As a traditional mining company, the availability of internal resources with data science, CV, or ML skills was limited. The SageMaker semantic segmentation built-in algorithm is used to train models for screener grid area segmentation. He has an MSc in Data Science and an MBA. Ion Kleopas is a Sr.

Computer Vision 104

Computer Vision Convolutional Neural Networks Machine Learning ML 104

Dlabs.ai

JUNE 11, 2019

How to use deep learning (even if you lack the data)? To train a computer algorithm when you don’t have any data. Some would say, it’s impossible – but at a time where data is so sensitive, it’s a common hurdle for a business to face. Read on to learn how to use deep learning in the absence of real data.

Deep Learning 52

Deep Learning Algorithm Convolutional Neural Networks Neural Network 52

Heartbeat

MARCH 14, 2023

By incorporating computer vision methods and algorithms into robots, they are able to view and understand their environment. 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.

Computer Vision 97

Computer Vision Robotics Deep Learning Algorithm 97

Heartbeat

APRIL 4, 2023

Using Deep Learning Algorithms to Perform Image Style Transfer source: Chelsea Troy Artificial Intelligence (AI) has revolutionized the way computer vision and deep learning algorithms create stunning visuals. One of the fascinating applications of AI is Neural Style Transfer (NST).

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Algorithm 52

AWS Machine Learning Blog

FEBRUARY 13, 2024

The BigBasket team was running open source, in-house ML algorithms for computer vision object recognition to power AI-enabled checkout at their Fresho (physical) stores. We used a convolutional neural network (CNN) architecture with ResNet152 for image classification.

Computer Vision 125

Computer Vision Convolutional Neural Networks ML AI 125

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

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

NYU Center for Data Science

JANUARY 31, 2024

Our goal was to segment training into what the algorithm believes are distinct phases of that learning process.” These models learn in a weird way, unlike humans who are bound by constraints like data and computation time,” Hu said. The technique Hu and his team propose is “mainly a tool for science,” said Hu.

Neural Network 52

Neural Network Convolutional Neural Networks Machine Learning Explainability 52

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.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Computer Scientist 52

Marktechpost

DECEMBER 12, 2024

The early 2000s witnessed a resurgence, fueled by advancements in hardware like GPUs, innovative algorithms such as ReLU activation and dropout, and the availability of massive datasets. Despite their simplicity, they lack the ability to model temporal or sequential data due to the absence of memory elements or feedback loops.

Neural Network 57

Neural Network Convolutional Neural Networks Natural Language Processing Artificial Intelligence 57

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. It provides a range of supervised and unsupervised learning algorithms, along with tools for model fitting, data preprocessing, and evaluation.

Software Engineer 52

Software Engineer Neural Network Deep Learning Convolutional Neural Networks 52

Heartbeat

MAY 30, 2023

It provides sensory data (observations) and rewards to the agent, and the agent acts in the environment based on its policy. The agent is a machine learning algorithm that adapts to take actions in the environment that optimize its total reward. The agent operates in a simulated or physical world called the environment.

Neural Network 72

Neural Network Convolutional Neural Networks Deep Learning Machine Learning 72

How to Learn Machine Learning

DECEMBER 15, 2024

Covering a comprehensive range of topics, the course provides a deep dive into the fundamental principles and practical applications of machine learning algorithms. This professional certificate provides a holistic approach to machine learning, combining theoretical knowledge with practical skills.

Machine Learning 52

Machine Learning Neural Network Convolutional Neural Networks Software Engineer 52

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.

Python 52

Python Convolutional Neural Networks Neural Network Deep Learning 52

Pickl AI

JANUARY 15, 2023

and how it works in the Data Science field. A subset of Machine Learning makes use of artificial neural networks and computer algorithms to imitate human learning. In order to improve the outcome of every task, Deep Learning uses machine learning algorithms to perform tasks repeatedly.

Deep Learning 52

Deep Learning Neural Network Convolutional Neural Networks Machine Learning 52

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? All of these visualizations do not only satisfy curiosity.

Deep Learning 111

Deep Learning Neural Network Convolutional Neural Networks Data Scientist 111

Heartbeat

NOVEMBER 21, 2023

From object detection and recognition to natural language processing, deep reinforcement learning, and generative models, we will explore how deep learning algorithms have conquered one computer vision challenge after another. However, they needed help capturing the intricate details and nuances in visual data.

Computer Vision 52

Computer Vision Deep Learning NLP Neural Network 52

Heartbeat

AUGUST 14, 2023

Edges are the boundaries between different regions in the image, and they can be detected using various edge detection algorithms, such as the Canny edge detector. Region-based segmentation can be performed using clustering algorithms, such as k-means clustering or mean-shift clustering, which group similar pixels into clusters.

Computer Vision 52

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 52

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. How Do Neural Networks Differ from Traditional Machine Learning Algorithms?

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Heartbeat

JANUARY 12, 2023

Deep learning systems can perform better with access to more data, which is the machine equivalent of more experience, in contrast to typical machine learning algorithms, many of which have a finite ability to learn regardless of the amount of data they obtain. A branch of machine learning is deep learning.

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network Machine Learning 52

Heartbeat

MARCH 8, 2023

Things You Can do Using Kangas Library in Data Science — by Pranjal Saxena Kangas, developed by the team at Comet , is an open source tool that allows data developers to load, sort, group, and visualize millions of images at once without the risk of crashing their notebooks.

Convolutional Neural Networks 40

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