ODSC - Open Data Science

FEBRUARY 13, 2023

These videos are a part of the ODSC/Microsoft AI learning journe y which includes videos, blogs, webinars, and more. How Deep Neural Networks Work and How We Put Them to Work at Facebook Deep learning is the technology driving today’s artificial intelligence boom.

Machine Learning 98

Machine Learning Machine Learning Deep Learning Deep Learning 98

Mlearning.ai

MARCH 13, 2023

Photo by Andy Kelly on Unsplash Choosing a machine learning (ML) or deep learning (DL) algorithm for application is one of the major issues for artificial intelligence (AI) engineers and also data scientists. Here I wan to clarify this issue. Do you have labeled or unlabeled data?

Machine Learning 52

Machine Learning Machine Learning Algorithm Support Vector Machines 52

Pickl AI

SEPTEMBER 10, 2024

Despite its limitations, the Perceptron laid the groundwork for more complex neural networks and Deep Learning advancements. Introduction The Perceptron is one of the foundational concepts in Artificial Intelligence and Machine Learning.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Dataconomy

AUGUST 25, 2023

Machine learning models: Machine learning models, such as support vector machines, recurrent neural networks, and convolutional neural networks, are used to predict emotional states from the acoustic and prosodic features extracted from the voice.

Artificial Intelligence 171

Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 171

Towards AI

JANUARY 26, 2024

For example, in the training of deep learning models, the weights and biases can be considered as model parameters. For example, in the training of deep learning models, the hyperparameters are the number of layers, the number of neurons in each layer, the activation function, the dropout rate, etc.

K-nearest Neighbors 82

K-nearest Neighbors Deep Learning Deep Learning Algorithm 82

Pickl AI

AUGUST 28, 2024

Examples include Logistic Regression, Support Vector Machines (SVM), Decision Trees, and Artificial Neural Networks. Random Forests Random Forests are an ensemble learning method that combines multiple Decision Trees to improve the accuracy and robustness of the model. They can handle non-linear data using kernel tricks.

Machine Learning 52

Machine Learning Machine Learning Algorithm K-nearest Neighbors 52

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.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines Decision Trees 52

Heartbeat

FEBRUARY 15, 2024

In the ever-evolving realm of artificial intelligence, computer vision is a crucial discipline that enables machines to interpret and glean insights from visual data. This learning process enables the system to make accurate predictions. One such powerful approach that has proven its worth is the Histogram of Oriented Gradients (HOG).

Deep Learning 40

Deep Learning Deep Learning Support Vector Machines ML 40

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. Introduction Artificial Intelligence (AI) transforms industries by enabling machines to mimic human intelligence.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

PyImageSearch

NOVEMBER 13, 2023

Home Table of Contents Faster R-CNNs Object Detection and Deep Learning Measuring Object Detector Performance From Where Do the Ground-Truth Examples Come? One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al.

Deep Learning 119

Deep Learning Deep Learning Algorithm Support Vector Machines 119

Flipboard

JULY 31, 2023

Object detection works by using machine learning or deep learning models that learn from many examples of images with objects and their labels. In the early days of machine learning, this was often done manually, with researchers defining features (e.g., edges, corners, or color histograms).

Machine Learning 123

Machine Learning Machine Learning Deep Learning Deep Learning 123

Mlearning.ai

APRIL 8, 2023

The earlier models that were SOTA for NLP mainly fell under the traditional machine learning algorithms. These included the Support vector machine (SVM) based models. 2003) “ Support-vector networks ” by Cortes and Vapnik (1995) Significant people : David Blei Corinna Cortes Vladimir Vapnik 4.

Natural Language Processing 98

Natural Language Processing Algorithm Machine Learning Machine Learning 98

Mlearning.ai

JUNE 14, 2023

YouTube Introduction to Sequence Learning and Attention Mechanisms Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 — Translation, Seq2Seq, Attention — YouTube Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 7 — Translation, Seq2Seq, Attention — YouTube 2.

Natural Language Processing 52

Natural Language Processing ML ML Support Vector Machines 52

Chatbots Life

APRIL 16, 2024

Support Vector Machines (SVM): SVM finds an optimal hyperplane to separate different classes in high-dimensional spaces. They can learn complex mappings between input and output variables. Convolutional Neural Networks (CNN): CNNs are specialized deep learning models commonly used for image classification tasks.

Natural Language Processing 59

Natural Language Processing Support Vector Machines Machine Learning Machine Learning 59

Data Science Dojo

JULY 15, 2024

Support Vector Machines (SVM): This algorithm finds a hyperplane that best separates data points of different classes in high-dimensional space. Decision Trees: These work by asking a series of yes/no questions based on data features to classify data points.

Machine Learning 308

Machine Learning Machine Learning Algorithm Clustering 308

Heartbeat

MAY 22, 2023

Schematic diagram of the overall framework of Emotion Recognition System [ Source ] The models that are used for AI emotion recognition can be based on linear models like Support Vector Machines (SVMs) or non-linear models like Convolutional Neural Networks (CNNs). We pay our contributors, and we don’t sell ads.

AI 52

AI AI Deep Learning Deep Learning 52

How to Learn Machine Learning

AUGUST 14, 2024

Relationship Extraction – RNNs (Recurrent Neural Networks) and SVMs (Support Vector Machines) work perfectly to extract relations between things. For complex tasks, you may want to opt for deep learning models such as GPT-4o , or RoBERTa. This is part of the scikit library that we discussed earlier.

Machine Learning 52

Machine Learning Machine Learning Python Algorithm 52

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.

AI 139

AI AI Artificial Intelligence Artificial Intelligence 139

Pickl AI

NOVEMBER 20, 2024

Example In Deep Learning, neural networks use matrices to represent weights between layers. Model Training Most Machine Learning models rely heavily on Linear Algebra during training phases. Support Vector Machines (SVM) : Employs dot products between feature vectors to find optimal hyperplanes for classification tasks.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Clustering 52

Data Science Blog

OCTOBER 20, 2024

Moreover, random forest models as well as support vector machines (SVMs) are also frequently applied. When it comes to deep learning models, that are often used for more complex problems and sequential data, Long Short-Term Memory (LSTM) networks or Transformers are applied.

Machine Learning 130

Machine Learning Machine Learning Deep Learning Deep Learning 130

ODSC - Open Data Science

JANUARY 5, 2024

If you’re looking to start building up your skills in these important Python libraries, especially for those that are used in machine & deep learning, NLP, and analytics, then be sure to check out everything that ODSC East has to offer. And did any of your favorites make it in?

Python 52

Python K-nearest Neighbors Data Science Data Visualization 52

Heartbeat

SEPTEMBER 28, 2023

Model Training We train multiple machine learning models, including Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine. Support Vector Machine (svm): Versatile model for linear and non-linear data. These models serve as the basis for our ensemble approach.

Support Vector Machines 52

Support Vector Machines Machine Learning Machine Learning ML 52

Pickl AI

AUGUST 1, 2023

Machine Learning algorithms, including Naive Bayes, Support Vector Machines (SVM), and deep learning models, are commonly used for text classification. Gather a dataset of customer support tickets with different categories, such as billing, technical issues, or product inquiries.

Data Analysis 52

Data Analysis Data Analysis Python Support Vector Machines 52

Towards AI

JULY 20, 2023

Other NLP techniques commonly used to automate parts of the SLR process are text vector (used in research identification and primary study selection), singular value decomposition (primary study selection), and latent semantic analysis models (primary study selection).

Natural Language Processing 80

Natural Language Processing ML ML Support Vector Machines 80

Pickl AI

FEBRUARY 12, 2024

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Heartbeat

APRIL 17, 2023

NRE is a complex task that involves multiple steps and requires sophisticated machine learning algorithms like Hidden Markov Models (HMMs) , Conditional Random Fields (CRFs), and Support Vector Machines (SVMs) be present. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Natural Language Processing 52

Natural Language Processing Support Vector Machines Machine Learning Machine Learning 52

Heartbeat

MARCH 21, 2023

Hinge Losses  — Another set of losses for classification problems, but commonly used in support vector machines. The sequential model API allows you to create a deep learning model where the sequential class is created, and then you add layers to it. Here we’re building a sequential model.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 23, 2024

AI, particularly Machine Learning and Deep Learning uses these insights to develop intelligent models that can predict outcomes, automate processes, and adapt to new information. Deep Learning: Advanced neural networks drive Deep Learning , allowing AI to process vast amounts of data and recognise complex patterns.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

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. With the model selected, the initialization of parameters takes place.

AI 136

AI AI Artificial Intelligence Artificial Intelligence 136

IBM Journey to AI blog

JULY 6, 2023

Python is the most common programming language used in machine learning. Machine learning and deep learning are both subsets of AI. Deep learning teaches computers to process data the way the human brain does. Deep learning algorithms are neural networks modeled after the human brain.

Machine Learning 80

Machine Learning Machine Learning Data Science Big Data 80

IBM Journey to AI blog

DECEMBER 20, 2023

Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others. They’re also part of a family of generative learning algorithms that model the input distribution of a given class or/category.

Machine Learning 97

Machine Learning Machine Learning Supervised Learning Clustering 97

Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

Heartbeat

OCTOBER 23, 2023

By analyzing historical data and utilizing predictive machine learning algorithms like BERT, ARIMA, Markov Chain Analysis, Principal Component Analysis, and Support Vector Machine, they can assess the likelihood of adverse events, such as hospital readmissions, and stratify patients based on risk profiles.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Big Data Analytics 52

Heartbeat

JUNE 14, 2023

Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks. Support vector machine classifiers as applied to AVIRIS data.” Measuring Calibration in Deep Learning. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Deep Learning 59

Deep Learning Deep Learning Support Vector Machines Machine Learning 59

Heartbeat

MARCH 6, 2024

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. We're committed to supporting and inspiring developers and engineers from all walks of life.

Exploratory Data Analysis 52

Exploratory Data Analysis Support Vector Machines Data Analysis Data Analysis 52

phData

OCTOBER 21, 2024

In this blog, we discuss LLMs and how they fall under the umbrella of AI and Machine learning. Large Language Models are deep learning models that recognize, comprehend, and generate text, performing various other natural language processing (NLP) tasks. What Are Large Language Models?

Machine Learning 52

Machine Learning Machine Learning Deep Learning Deep Learning 52

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.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Algorithm 52