Google Research AI blog

FEBRUARY 7, 2023

The explosion in deep learning a decade ago was catapulted in part by the convergence of new algorithms and architectures, a marked increase in data, and access to greater compute. However, pre-trained large language models (LLMs) consume a significant amount of information through self-supervision on big training sets.

Deep Learning 118

Deep Learning Deep Learning Algorithm ML 118

Dataconomy

APRIL 16, 2025

By analyzing and identifying patterns within this data, supervised learning algorithms can predict outcomes for new, unseen inputs. Definition of supervised learning At its core, supervised learning utilizes labeled data to inform a machine learning model.

Supervised Learning 91

Supervised Learning Decision Trees Algorithm Machine Learning 91

Data Science Dojo

JULY 15, 2024

By understanding machine learning algorithms, you can appreciate the power of this technology and how it’s changing the world around you! It’s like having a super-powered tool to sort through information and make better sense of the world. Learn in detail about machine learning algorithms 2.

Machine Learning 307

Machine Learning Machine Learning Algorithm Clustering 307

Pickl AI

APRIL 13, 2025

Summary: Classifier in Machine Learning involves categorizing data into predefined classes using algorithms like Logistic Regression and Decision Trees. Introduction Machine Learning has revolutionized how we process and analyse data, enabling systems to learn patterns and make predictions.

Machine Learning 52

Machine Learning Machine Learning Decision Trees K-nearest Neighbors 52

Dataconomy

APRIL 29, 2025

Overview of classification in machine learning Classification serves as a foundational method in machine learning, where algorithms are trained on labeled datasets to make predictions. Classification methods are vital for organizing information and making data-driven decisions.

Support Vector Machines 91

Support Vector Machines Machine Learning Machine Learning Algorithm 91

Pickl AI

FEBRUARY 7, 2025

Summary: Machine Learning and Deep Learning are AI subsets with distinct applications. Introduction In todays world of AI, both Machine Learning (ML) and Deep Learning (DL) are transforming industries, yet many confuse the two. What is Deep Learning? billion by 2034.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Pickl AI

APRIL 9, 2025

Summary: Artificial Intelligence (AI) and Deep Learning (DL) are often confused. AI vs Deep Learning is a common topic of discussion, as AI encompasses broader intelligent systems, while DL is a subset focused on neural networks. Is Deep Learning just another name for AI? Is all AI Deep Learning?

Deep Learning 52

Deep Learning Deep Learning Artificial Intelligence Artificial Intelligence 52

Heartbeat

OCTOBER 11, 2023

This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. Handling Complex and Large Datasets: Machine learning algorithms can handle vast amounts of data and extract meaningful information.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

ODSC - Open Data Science

MARCH 11, 2025

Why Gradient Boosting Continues to Dominate Tabular DataProblems Machine learning has seen the rise of deep learning models, particularly for unstructured data such as images and text. Lucena attributes its dominance to the way gradient boosted decision trees (GBDTs) handle structured information.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Deep Learning 52

PyImageSearch

JANUARY 16, 2023

Setting Up Our Project Comparing XGboost and Gradient Boost Results Summary Citation Information Scaling Kaggle Competitions Using XGBoost: Part 3 We continue our journey into understanding XGBoost, but there is one penultimate stop we need to make before deep diving into the nitty gritty of Extreme Gradient Boosting.

Decision Trees 100

Decision Trees Deep Learning Deep Learning Algorithm 100

Heartbeat

FEBRUARY 22, 2023

Deep learning for feature extraction, ensemble models, and more Photo by DeepMind on Unsplash The advent of deep learning has been a game-changer in machine learning, paving the way for the creation of complex models capable of feats previously thought impossible.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Smart Data Collective

SEPTEMBER 16, 2020

Fundamental to any aspect of data science, it’s difficult to develop accurate predictions or craft a decision tree if you’re garnering insights from inadequate data sources. Deep Learning, Machine Learning, and Automation. Data Sourcing.

Predictive Analytics 139

Predictive Analytics Analytics Analytics Decision Trees 139

IBM Journey to AI blog

AUGUST 9, 2024

Most generative AI models start with a foundation model , a type of deep learning model that “learns” to generate statistically probable outputs when prompted. In short, predictive AI helps enterprises make informed decisions regarding the next step to take for their business.

AI 120

AI AI Decision Trees Algorithm 120

DrivenData Labs

MAY 24, 2023

Her primary interests lie in theoretical machine learning. She currently does research involving interpretability methods for biological deep learning models. We chose to compete in this challenge primarily to gain experience in the implementation of machine learning algorithms for data science.

Machine Learning 225

Machine Learning Machine Learning Data Science Decision Trees 225

Towards AI

APRIL 7, 2024

Deep learning multiple– layer artificial neural networks are the basis of deep learning, a subdivision of machine learning (hence the word “deep”). This will also expose you to current and timely information as machine learning is an ever-evolving topic. GIS Random Forest script.

Machine Learning 102

Machine Learning Machine Learning K-nearest Neighbors Supervised Learning 102

Pickl AI

JANUARY 2, 2025

Summary: Entropy in Machine Learning quantifies uncertainty, driving better decision-making in algorithms. It optimises decision trees, probabilistic models, clustering, and reinforcement learning. Key Takeaways Entropy measures randomness, guiding algorithms to make better decisions.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Clustering 52

Dataconomy

MAY 25, 2023

On the other hand, artificial intelligence is the simulation of human intelligence in machines that are programmed to think and learn like humans. By leveraging advanced algorithms and machine learning techniques, IoT devices can analyze and interpret data in real-time, enabling them to make informed decisions and take autonomous actions.

Internet of Things 160

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PyImageSearch

JANUARY 23, 2023

The reasoning behind that is simple; whatever we have learned till now, be it adaptive boosting, decision trees, or gradient boosting, have very distinct statistical foundations which require you to get your hands dirty with the math behind them. The goal is to nullify the abstraction created by packages as much as possible.

Deep Learning 109

Deep Learning Deep Learning Algorithm Decision Trees 109

Flipboard

MARCH 7, 2023

The large volume of contacts creates a challenge for CSBA to extract key information from the transcripts that helps sellers promptly address customer needs and improve customer experience. Here, a non-deep learning model was trained and run on SageMaker, the details of which will be explained in the following section.

ML 122

ML ML AWS AI 122

Towards AI

APRIL 4, 2024

A sector that is currently being influenced by machine learning is the geospatial sector, through well-crafted algorithms that improve data analysis through mapping techniques such as image classification, object detection, spatial clustering, and predictive modeling, revolutionizing how we understand and interact with geographic information.

K-nearest Neighbors 80

K-nearest Neighbors Machine Learning Machine Learning Decision Trees 80

Smart Data Collective

JULY 1, 2023

It involves human annotators using a tool to label images or tag relevant information. The resulting structured data is then used to train a machine learning algorithm. There are a lot of image annotation techniques that can make the process more efficient with deep learning.

Cross Validation 93

Cross Validation AI AI Machine Learning 93

Pickl AI

DECEMBER 9, 2024

In contrast, decision trees assume data can be split into homogeneous groups through feature thresholds. Inductive bias is crucial in ensuring that Machine Learning models can learn efficiently and make reliable predictions even with limited information by guiding how they make assumptions about the data.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Natural Language Processing 52

PyImageSearch

OCTOBER 21, 2024

These sensors continuously send data to a system that analyzes the information and predicts when a part might fail. On Lines 21-27 , we define a Node class, which represents a node in a decision tree. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

Algorithm 113

Algorithm Deep Learning Deep Learning Data Preparation 113

Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. TensorFlow and Keras: TensorFlow is an open-source platform for machine learning.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Dataconomy

OCTOBER 5, 2023

Data preprocessing tasks can include data cleaning to remove errors or inconsistencies, normalization to bring data within a consistent range, and feature engineering to extract meaningful information from raw data. AI practitioners choose an appropriate machine learning model or algorithm that aligns with the problem at hand.

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AI AI Artificial Intelligence Artificial Intelligence 136

Pickl AI

AUGUST 1, 2023

The model learns to map input features to the correct output by minimizing the error between its predictions and the actual target values. Examples of supervised learning models include linear regression, decision trees, support vector machines, and neural networks. regression, classification, clustering).

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines Decision Trees 52

Pickl AI

JULY 1, 2024

Demand forecasting , the art of anticipating customer needs, allows companies to optimize inventory levels, streamline production processes, and make informed strategic decisions. Data Transformation Combine existing data points to create features that might be more informative for forecasting.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Data Science Dojo

AUGUST 16, 2023

Transformer models are a type of deep learning model that are used for natural language processing (NLP) tasks. They are able to learn long-range dependencies between words in a sentence, which makes them very powerful for tasks such as machine translation, text summarization, and question answering.

Natural Language Processing 195

Natural Language Processing Decision Trees Deep Learning Deep Learning 195

Data Science Dojo

AUGUST 16, 2023

Transformer models are a type of deep learning model that are used for natural language processing (NLP) tasks. They are able to learn long-range dependencies between words in a sentence, which makes them very powerful for tasks such as machine translation, text summarization, and question answering.

Natural Language Processing 195

Natural Language Processing Decision Trees Deep Learning Deep Learning 195

Towards AI

APRIL 11, 2023

Feature Extraction Feature Extraction is basically extracting relevant information from the pre-processed image that can be used for classification. ANNs consist of layers of interconnected nodes, which process and transmit information. Such are some of the use cases.

Machine Learning 98

Machine Learning Machine Learning Support Vector Machines Algorithm 98

PyImageSearch

DECEMBER 12, 2022

Setting Up the Prerequisites Building the Model Assessing the Model Summary Citation Information Scaling Kaggle Competitions Using XGBoost: Part 2 In our previous tutorial , we went through the basic foundation behind XGBoost and learned how easy it was to incorporate a basic XGBoost model into our project. Table 1: The Dataset.

Decision Trees 52

Decision Trees Deep Learning Deep Learning Exploratory Data Analysis 52

IBM Journey to AI blog

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. Rule-based chatbots : Also known as decision-tree or script-driven bots, they follow preprogrammed protocols and generate responses based on predefined rules. What makes a good AI conversationalist?

AI 116

AI AI ML ML 116

IBM Journey to AI blog

SEPTEMBER 5, 2023

Essentially, these chatbots operate like a decision tree. Rules-based chatbots Building upon the menu-based chatbot’s simple decision tree functionality, the rules-based chatbot employs conditional if/then logic to develop conversation automation flows.

Decision Trees 73

Decision Trees Artificial Intelligence Artificial Intelligence AI 73

AWS Machine Learning Blog

JUNE 22, 2023

This includes information such as machine unique identifier, cabinet type, location, operating system, software version, game theme, and more, as shown in the following table. All the names in the table are anonymized to protect customer information.) Increasing the number of bins preserves more temporal information.

AWS 118

AWS ML ML Machine Learning 118

Pickl AI

NOVEMBER 2, 2023

It serves as a handy quick-reference tool to assist data professionals in their work, aiding in data interpretation, modeling , and decision-making processes. In the fast-paced world of Data Science, having quick and easy access to essential information is invaluable when using a repository of Cheat sheets for Data Scientists.

Data Scientist 52

Data Scientist Data Science Data Visualization Machine Learning 52

Dataconomy

APRIL 4, 2024

From deterministic software to AI Earlier examples of “thinking machines” included cybernetics (feedback loops like autopilots) and expert systems (decision trees for doctors). Deep learning, TensorFlow and other technologies emerged, mostly to power search engines, recommendations and advertising.

Algorithm 103

Algorithm AI AI Decision Trees 103

Towards AI

APRIL 14, 2023

Adding such extra information should improve the classification compared to the previous method (Principle Label Space Transformation). Correctly predicting the tags of the questions is a very challenging problem as it involves the prediction of a large number of labels among several hundred thousand possible labels.

K-nearest Neighbors 98

K-nearest Neighbors Algorithm Clustering Support Vector Machines 98