IBM Journey to AI blog

DECEMBER 19, 2023

As organizations collect larger data sets with potential insights into business activity, detecting anomalous data, or outliers in these data sets, is essential in discovering inefficiencies, rare events, the root cause of issues, or opportunities for operational improvements. But what is an anomaly and why is detecting it important?

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Dataconomy

MAY 25, 2023

By leveraging techniques like machine learning and deep learning, IoT devices can identify trends, anomalies, and patterns within the data. This enables them to respond quickly to changing conditions or events. Deep learning, in combination with IoT, unlocks various possibilities.

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Becoming Human

MARCH 16, 2023

Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded. Machine Learning Machine learning (ML) focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed. A few AI technologies are empowering drug design.

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Dataconomy

OCTOBER 5, 2023

AI practitioners choose an appropriate machine learning model or algorithm that aligns with the problem at hand. Common choices include neural networks (used in deep learning), decision trees, support vector machines, and more. The next critical step is model selection.

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IBM Journey to AI blog

MAY 9, 2023

Over the past decade, deep learning arose from a seismic collision of data availability and sheer compute power, enabling a host of impressive AI capabilities. We stand on the frontier of an AI revolution. But we’ve faced a paradoxical challenge: automation is labor intensive. But this is starting to change.

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How to Learn Machine Learning

MAY 14, 2023

Acquiring Essential Machine Learning Knowledge Once you have a strong foundation in mathematics and programming, it’s time to dive into the world of machine learning. Additionally, you should familiarize yourself with essential machine learning concepts such as feature engineering, model evaluation, and hyperparameter tuning.

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ML @ CMU

APRIL 23, 2025

The authors introduce two new datasets and propose a three-tiered explanation frameworkranging from concise event descriptions to rich, emotionally grounded narrativesgenerated using large language models. Lee Other Topics In Machine Learning (I.e., Gordon, Fei Fang Physics of Language Models: Part 2.1,

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Google Research AI blog

APRIL 30, 2023

Posted by Catherine Armato, Program Manager, Google The Eleventh International Conference on Learning Representations (ICLR 2023) is being held this week as a hybrid event in Kigali, Rwanda. We are proud to be a Diamond Sponsor of ICLR 2023, a premier conference on deep learning, where Google researchers contribute at all levels.

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ODSC - Open Data Science

JULY 12, 2024

The fields of AI and data science are changing rapidly and ODSC West 2024 is evolving to ensure we keep you at the forefront of the industry with our all-new tracks, AI Agents , What’s Next in AI, and AI in Robotics , and our updated tracks NLP, NLU, and NLG , and Multimodal and Deep Learning , and LLMs and RAG.

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IBM Journey to AI blog

JUNE 14, 2023

Traditional AI tools, especially deep learning-based ones, require huge amounts of effort to use. With a foundation model, often using a kind of neural network called a “transformer” and leveraging a technique called self-supervised learning, you can create pre-trained models for a vast amount of unlabeled data.

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DataRobot Blog

DECEMBER 15, 2022

supervised learning and time series regression). For example, how holidays and events affect forecasting. In the background, models are being trained in parallel for efficiency and speed—from Tree-based models to Deep Learning models (which will be chosen based on your historical data and target variable) and more.

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Heartbeat

JULY 6, 2023

Object detection is typically achieved through the use of deep learning models, particularly Convolutional Neural Networks (CNNs). In this article, you will learn about object detection through the SWIN Transformer. What is the Swin Transformer? If you’d like to contribute, head on over to our call for contributors.

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Heartbeat

FEBRUARY 7, 2023

image by rawpixel.com Understanding the concept of language models in natural language processing (NLP) is very important to anyone working in the Deep learning and machine learning space. Learn more from Uber’s Olcay Cirit. One of the areas that has seen significant growth is language modeling.

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ODSC - Open Data Science

OCTOBER 4, 2023

General and Efficient Self-supervised Learning with data2vec Michael Auli | Principal Research Scientist at FAIR | Director at Meta AI This session will explore data2vec, a framework for general self-supervised learning that uses the same learning method for either speech, NLP, or computer vision. Sign me up!

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Heartbeat

JUNE 19, 2023

Photo by GR Stocks on Unsplash GANs are more than just a breakthrough in the field of deep learning; they represent a quantum leap forward in the capabilities of artificial intelligence. This is like using unsupervised learning, where you don’t have any labeled examples and you try to learn the underlying structure of the data.

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ODSC - Open Data Science

JANUARY 22, 2024

A group of researchers from the Institute of Cyberspace Security, Zhejiang University of Technology, have introduced the SGGRL model, an innovative multi-modal molecular representation learning framework. Interested in attending an ODSC event? Learn more about our upcoming events here. raised €91M in series B.

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Heartbeat

MARCH 7, 2024

ScikitLLM is interesting because it seamlessly integrates LLMs into your traditional Scikit-learn (Sklearn) library. In this post, we’ll take a deep dive into ScikitLLM and explore how you can use it to build text summarization ML models and monitor them all in Comet.

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DagsHub

DECEMBER 23, 2024

Key Characteristics Static Dataset : Works with a predefined set of unlabeled examples Batch Selection : Can select multiple samples simultaneously for labeling because of which it is widely used by deep learning models. Pool-Based Active Learning Scenario : Classifying images of artwork styles for a digital archive.  

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PyImageSearch

SEPTEMBER 16, 2024

Anomaly detection ( Figure 2 ) is a critical technique in data analysis used to identify data points, events, or observations that deviate significantly from the norm. Machine Learning Methods Machine learning methods ( Figure 7 ) can be divided into supervised, unsupervised, and semi-supervised learning techniques.

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Heartbeat

FEBRUARY 8, 2024

Building the Model Deep learning techniques have proven to be highly effective in performing cross-modal retrieval. In the case of image-to-text search, deep learning models such as VGG16 or ResNet can be used to extract image features.

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Heartbeat

FEBRUARY 15, 2023

It is a supervised learning methodology that predicts if a piece of text belongs to one category or the other. As a machine learning engineer, you start with a labeled data set that has vast amounts of text that have already been categorized. If you’d like to contribute, head on over to our call for contributors.

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Pickl AI

JANUARY 2, 2025

Mathematical Definition and Formula of Entropy The mathematical formula for entropy H(X) is: Here: P(xi) is the probability of the iii-th event. log2P(xi) measures the information content of each event in bits. Entropy is highest when all events are equally likely, indicating maximum uncertainty.

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Mlearning.ai

JUNE 1, 2023

Using PyTorch Deep Learning Framework and CNN Architecture Photo by Andrew S on Unsplash Motivation Build a proof-of-concept for Audio Classification using a deep-learning neural network with PyTorch framework. This is inherently a supervised learning problem. Data Source here.

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Snorkel AI

JULY 5, 2023

Sheer volume—I think where this came about is when we had the rise of deep learning, there was a much larger volume of data used, and of course, we had big data that was driving a lot of that because we found ourselves with these mountains of data. And in supervised learning, it has to be labeled data. AR : Yeah.

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Heartbeat

OCTOBER 25, 2023

Machine Learning Tools in Bioinformatics Machine learning is vital in bioinformatics, providing data scientists and machine learning engineers with powerful tools to extract knowledge from biological data. Deep learning, a subset of machine learning, has revolutionized image analysis in bioinformatics.

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Heartbeat

JULY 20, 2023

Limited availability of labeled datasets: In some domains, there is a scarcity of datasets with fine-grained annotations, making it difficult to train segmentation networks using supervised learning algorithms. We’re committed to supporting and inspiring developers and engineers from all walks of life.

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Snorkel AI

JULY 5, 2023

Sheer volume—I think where this came about is when we had the rise of deep learning, there was a much larger volume of data used, and of course, we had big data that was driving a lot of that because we found ourselves with these mountains of data. And in supervised learning, it has to be labeled data. AR : Yeah.

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Snorkel AI

JULY 5, 2023

Sheer volume—I think where this came about is when we had the rise of deep learning, there was a much larger volume of data used, and of course, we had big data that was driving a lot of that because we found ourselves with these mountains of data. And in supervised learning, it has to be labeled data. AR : Yeah.

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Pickl AI

AUGUST 9, 2024

Data Streaming Learning about real-time data collection methods using tools like Apache Kafka and Amazon Kinesis. Students should understand the concepts of event-driven architecture and stream processing. Big Data and Machine Learning The intersection of Big Data and Machine Learning is a critical area of focus in a Big Data syllabus.

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Towards AI

MARCH 28, 2023

Mapping these farms allows policy-makers to allocate resources and monitor the impacts of extreme events on food production and food security. According to the problem statement from Zindi, small farms produce about 35% of the world’s food,and are mostly found in low- and middle-income countries.

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Pickl AI

SEPTEMBER 12, 2024

Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks. Deep Learning : A subset of Machine Learning that uses Artificial Neural Networks with multiple hidden layers to learn from complex, high-dimensional data.

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Heartbeat

SEPTEMBER 20, 2023

Deep learning-based models, especially CNNs, have revolutionized feature extraction in image captioning. CNNs are particularly well-suited for this task due to their ability to learn hierarchical representations of visual data. In image captioning, a pre-trained CNN is often utilized to extract image features.

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BAIR

APRIL 29, 2022

At the same time as the emergence of powerful RL systems in the real world, the public and researchers are expressing an increased appetite for fair, aligned, and safe machine learning systems. Control feedback gives an agent the ability to react to unforeseen events (e.g. a sudden snap of cold weather) autonomously.

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AssemblyAI

NOVEMBER 21, 2022

At the bedrock of the Deep Learning that powers incredible technologies like text-to-image models lies matrix multiplication. Regardless of the specific architecture employed, (nearly) every Neural Network relies on efficient matrix multiplication to learn and infer.

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Heartbeat

NOVEMBER 16, 2023

The process involves supervised learning (SL) and reinforcement learning (RL) phases. Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

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Hacker News

FEBRUARY 14, 2023

And in fact the big breakthrough in “deep learning” that occurred around 2011 was associated with the discovery that in some sense it can be easier to do (at least approximate) minimization when there are lots of weights involved than when there are fairly few.

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The MLOps Blog

JANUARY 5, 2023

To get started, it is my pleasure to introduce you to our guest, machine learning and data science engineer Kuba Cieslik. It’s nice to participate in this event. I’m a machine learning engineer with some years of experience in building ML products and ML solutions. How self-supervised learning works.

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