ODSC - Open Data Science

APRIL 20, 2023

Deep Learning Approaches to Sentiment Analysis (with spaCy!) In this post, we’ll be demonstrating two deep learning approaches to sentiment analysis, specifically using spaCy. Six Core Competencies Data Scientists Need to Succeed in Their Careers Data scientists need to know more than just algorithms to succeed.

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Flipboard

JANUARY 8, 2024

Researchers have developed a deep learning-based AI algorithm that automatically analyzes chest X-rays to rapidly detect COVID-19 infection with more …

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Flipboard

NOVEMBER 2, 2023

Two researchers from the University of Cambridge have developed a deep-learning algorithm that could make it easier, faster, and cheaper to identify energy-wasting homes — a significant source of greenhouse gas emissions. Trained on open-source data including energy performance certificates and …

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Dataconomy

MARCH 6, 2024

The programs and algorithms that instruct and teach AI models how to learn and think are all designed and written by software engineers. Deep learning models recognize complex patterns in sound, pictures and text, producing accurate insights and predictions and automating tasks that typically require human input.

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Dataconomy

JANUARY 23, 2024

The field of artificial intelligence grew popular with the dawn of computers and sci-fi media. Fantasy is quickly becoming a reality as more companies and computer science-related studies are shifting their focus to artificial intelligence and developing them.

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Data Science Dojo

JULY 4, 2023

With a foundation in math, statistics, and programming, learning Generative AI requires dedication and patience as the technology evolves. Generative AI harnesses deep learning algorithms to generate human-like data in response to user input. Back to basics: What is Generative AI?

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ODSC - Open Data Science

JANUARY 27, 2023

Ten Game-Changing Generative AI Projects, The Quest for the Ultimate Learning Algorithm, and Training Your PyTorch Model Top Ten Game-Changing Generative AI Projects in 2023 Here are our picks for a few generative AI projects that are worth checking out for yourself, most of which you can experiment with for free. Get the deal here!

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Towards AI

AUGUST 24, 2023

Common mistakes and misconceptions about learning AI/ML Markus Spiske on Unsplash A common misconception of beginners is that they can learn AI/ML from a few tutorials that implement the latest algorithms, so I thought I would share some notes and advice on learning AI. Trying to code ML algorithms from scratch.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. This will lead to algorithm development for any machine or deep learning processes.

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Dataconomy

MAY 29, 2023

Explanation of AI and ML Artificial Intelligence (AI) refers to a field within computer science dedicated to the creation of intelligent machines, capable of executing tasks typically requiring human intelligence. These algorithms allow AI systems to recognize patterns, forecast outcomes, and adjust to new situations.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Developing NLP tools isn’t so straightforward, and requires a lot of background knowledge in machine & deep learning, among others. Machine & Deep Learning Machine learning is the fundamental data science skillset, and deep learning is the foundation for NLP.

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Pickl AI

APRIL 25, 2023

There are five different subsets of Artificial Intelligence which include Machine Learning, Deep Learning, Robotics, Neural Networks, and NLP. There are various Machine Learning algorithms including regression and classification algorithms. What is Deep Learning? What is Robotics?

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Dataconomy

APRIL 18, 2023

Additionally, it is crucial to comprehend the fundamental concepts that underlie AI, including neural networks, algorithms, and data structures. AI systems use a combination of algorithms, machine learning techniques, and data analytics to simulate human intelligence. What is artificial intelligence?

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Towards AI

FEBRUARY 3, 2024

(Left) Photo by Pawel Czerwinski on Unsplash U+007C (Right) Unsplash Image adjusted by the showcased algorithm Introduction It’s been a while since I created this package ‘easy-explain’ and published on Pypi. A few weeks ago, I needed an explainability algorithm for a YoloV8 model. Lecture Notes in Computer Science(), vol 11700.

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Dataconomy

APRIL 12, 2023

By incorporating insights from cognitive science, AI is becoming more advanced and capable, with the potential to transform many aspects of our lives. Artificial intelligence, or AI, is a field of computer science and engineering that focuses on creating machines and systems that can perform tasks that typically require human intelligence.

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NYU Center for Data Science

JULY 18, 2023

MaD & MaD+ The Math and Data (MaD) group is a collaboration between CDS and the NYU Courant Institute of Mathematical Sciences. Their work specializes in signal processing and inverse problems, machine learning and deep learning, and high-dimensional statistics and probability.

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How to Learn Machine Learning

MAY 14, 2023

Building a Solid Foundation in Mathematics and Programming To become a successful machine learning engineer, it’s essential to have a strong foundation in mathematics and programming. Mathematics is crucial because machine learning algorithms are built on concepts such as linear algebra, calculus, probability, and statistics.

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Mlearning.ai

FEBRUARY 17, 2023

to reshape or re-express a programmer’s solution for a problem in a way to be executable by a computer system. We all remember courses such as (1) Fundamentals of Programming (2) Advanced Programming (3) Data Structures and Algorithms, which were mandatory to pass in our bachelor’s. [credit] OpenAI DALL.E credit] OpenAI DALL.E

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Mlearning.ai

JANUARY 26, 2023

NLP sentiment analysis uses algorithms to identify key sentiment indicators in text data. The algorithms then assign a sentiment score to each indicator based on how strongly it expresses a particular sentiment. The most common method is supervised machine learning algorithms, which require using labeled data to train the algorithm.

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Pickl AI

JULY 18, 2023

They possess a deep understanding of AI technologies, algorithms, and frameworks and have the ability to translate business requirements into robust AI systems. AI Engineers focus primarily on implementing and deploying AI models and algorithms, working closely with data scientists and machine learning experts.

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Heartbeat

OCTOBER 25, 2023

Empowering Data Scientists and Machine Learning Engineers in Advancing Biological Research Image from European Bioinformatics Institute Introduction: In biological research, the fusion of biology, computer science, and statistics has given birth to an exciting field called bioinformatics.

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Pickl AI

AUGUST 21, 2023

Key Takeaways: Data Science is a multidisciplinary field bridging statistics, mathematics, and computer science to extract insights from data. The roadmap to becoming a Data Scientist involves mastering programming, statistics, machine learning, data visualization, and domain knowledge. Practical experience is crucial.

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Dataconomy

MAY 2, 2023

They design, develop, and deploy the machine learning algorithms that power everything from self-driving cars to personalized recommendations. They are the driving force behind the artificial intelligence revolution, creating new opportunities and possibilities that were once the stuff of science fiction. They build the future.

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DrivenData Labs

MAY 18, 2023

How did you get started in data science? I was first introduced to the field of AI during my BSc studies in Computer Science at the Athens University of Economics and Business.

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Heartbeat

MARCH 5, 2024

This article explores the exciting world of image enhancement techniques, including various methods that can be employed to preprocess and improve images, ranging from traditional image processing techniques to modern deep learning-based approaches. Developing algorithms that can handle such variations is a significant challenge.

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PyImageSearch

MAY 8, 2023

You could write an algorithm that predicts the sales of comic books, and your model works well and produces high-accuracy predictions, but you need to know why. Or maybe it’s the opposite, your algorithm predicts completely wrong sales figures, and you need to figure out why. Or requires a degree in computer science?

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IBM Journey to AI blog

JULY 6, 2023

Just as humans can learn through experience rather than merely following instructions, machines can learn by applying tools to data analysis. Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention.

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Dataconomy

MARCH 13, 2023

With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless. Key concepts of AI The following are some of the key concepts of AI: Data:  AI requires vast amounts of data to learn and improve its performance over time.

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PyImageSearch

JANUARY 9, 2023

To learn how to develop Face Recognition applications using Siamese Networks, just keep reading. Jump Right To The Downloads Section Face Recognition with Siamese Networks, Keras, and TensorFlow Deep learning models tend to develop a bias toward the data distribution on which they have been trained. That’s not the case.

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NYU Center for Data Science

DECEMBER 12, 2023

In an era where algorithms determine everything from creditworthiness to carceral sentencing, the imperative for responsible innovation has never been more urgent. Andrew Bell and Lucius Bynum: Challenging Algorithmic Boundaries Andrew Bell’s exploration of algorithmic fairness sets a foundation for the responsible AI dialogue.

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Mlearning.ai

JUNE 4, 2023

Computer graphics the creation and rendering of synthetic images or animations, such as 3D models, virtual reality, or augmented reality. Machine learning the use of algorithms and data to learn patterns and make predictions or decisions, such as deep learning, neural networks, or reinforcement learning.

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ODSC - Open Data Science

OCTOBER 17, 2023

For metrics that may not correlate with any other variables, we can attempt to characterize the behavior over time using forecasting algorithms. One state-of-the-art forecasting algorithm is Prophet, developed by Meta. Her interests lie in software testing, cloud computing, big data analytics, systems engineering, and architecture.

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Dataconomy

FEBRUARY 22, 2023

The systems can only “understand” specific situations they have been exposed to in the past and are unable to generalize in a way that humans find easy, said Stuart Russell, a computer science professor at the University of California, Berkeley. Of course, like any technology, machine learning is not infallible.

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DrivenData Labs

MAY 24, 2023

He is interested in researching human cognition and computational methods for modeling the brain. Nika Chuzhoy is a first-year undergraduate student at Caltech majoring in Computer Science. Her primary interests lie in theoretical machine learning. Dr. Martine De C**k is a Professor with expertise in machine learning.

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Heartbeat

AUGUST 23, 2023

Consider a scenario where legal practitioners are armed with clever algorithms capable of analyzing, comprehending, and extracting key insights from massive collections of legal papers. Algorithms can automatically detect and extract key items. But what if there was a technique to quickly and accurately solve this language puzzle?

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Pickl AI

JULY 25, 2023

Machine learning engineers specialize in designing, building, and deploying machine learning models at scale. With expertise in Python, machine learning algorithms, and cloud platforms, machine learning engineers optimize models for efficiency, scalability, and maintenance.

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Flipboard

AUGUST 25, 2023

Zhavoronkov has a narrower definition of AI drug discovery, saying it refers specifically to the application of deep learning and generative learning in the drug discovery space. The “deep learning revolution” — a time when development and use of the technology exploded — took off around 2014, Zhavoronkov said. “It

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