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?

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Marktechpost

JUNE 3, 2024

Introduction to Machine Learning This course covers machine learning basics, including problem-solving, model building, and key algorithms. By the end, students will understand network construction, kernels, and expanding networks using transfer learning.

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Viso.ai

OCTOBER 9, 2024

Human-machine interaction is an important area of research where machine learning algorithms with visual perception aim to gain an understanding of human interaction. State-of-the-art emotion AI Algorithms Outlook, current research, and applications What Is AI Emotion Recognition? About us: Viso.ai What is Emotion AI?

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Pickl AI

NOVEMBER 20, 2024

Introduction Linear Algebra is a fundamental mathematical discipline that underpins many algorithms and techniques in Machine Learning. By understanding Linear Algebra operations, practitioners can better grasp how Machine Learning models work, optimize their performance, and implement various algorithms effectively.

Machine Learning 52

Machine Learning Neural Network Natural Language Processing Convolutional Neural Networks 52

Marktechpost

APRIL 12, 2024

Deep learning architectures have revolutionized the field of artificial intelligence, offering innovative solutions for complex problems across various domains, including computer vision, natural language processing, speech recognition, and generative models.

Deep Learning 137

Deep Learning Convolutional Neural Networks Neural Network Natural Language Processing 137

Unite.AI

SEPTEMBER 21, 2023

Organizations and practitioners build AI models that are specialized algorithms to perform real-world tasks such as image classification, object detection, and natural language processing. Some prominent AI techniques include neural networks, convolutional neural networks, transformers, and diffusion models.

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Unite.AI

AUGUST 2, 2023

To tackle the issue of single modality, Meta AI released the data2vec, the first of a kind, self supervised high-performance algorithm to learn patterns information from three different modalities: image, text, and speech. Why Does the AI Industry Need the Data2Vec Algorithm?

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Marktechpost

MAY 24, 2024

Transformers have greatly transformed natural language processing, delivering remarkable progress across various applications. Removing or approximating these linear blocks minimally affects model performance, prompting the development of depth-pruning algorithms and novel distillation techniques.

Convolutional Neural Networks 122

Convolutional Neural Networks Neural Network Natural Language Processing Machine Learning 122

Unite.AI

MAY 31, 2023

A neural network (NN) is a machine learning algorithm that imitates the human brain's structure and operational capabilities to recognize patterns from training data. Lack of Literature Liquid Neural Networks have limited literature on implementation, application, and benefits.

Neural Network 354

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Pickl AI

AUGUST 14, 2024

Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling. Optimization Algorithms Optimization algorithms play a crucial role in training neural networks.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Unite.AI

MAY 24, 2024

In modern machine learning and artificial intelligence frameworks, transformers are one of the most widely used components across various domains including GPT series, and BERT in Natural Language Processing, and Vision Transformers in computer vision tasks. million training images, and over 50,000 validation images.

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Chatbots Life

APRIL 16, 2024

Examples of Generative Models Generative models encompass various algorithms that capture patterns in data to generate realistic new examples. This is useful in natural language processing tasks. Convolutional Neural Networks (CNN): CNNs are specialized deep learning models commonly used for image classification tasks.

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Pickl AI

SEPTEMBER 16, 2024

They extract features from data by capturing higher-level abstractions, making them suitable for complex tasks like image recognition and natural language processing. The two primary components are Restricted Boltzmann Machines (RBMs) and Feedforward Networks. However, their approach differs.

Deep Learning 52

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Viso.ai

DECEMBER 10, 2023

An introduction The basic concepts and how it works Traditional and modern deep learning image recognition The best popular image recognition algorithms How to use Python for image recognition Examples and deep learning applications Popular image recognition software About: We provide the leading end-to-end computer vision platform Viso Suite.

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Marktechpost

JUNE 3, 2024

Numerous groundbreaking models—including ChatGPT, Bard, LLaMa, AlphaFold2, and Dall-E 2—have surfaced in different domains since the Transformer’s inception in Natural Language Processing (NLP). Iterative improvement algorithms and stochastic algorithms are the two main categories under which heuristic algorithms fall.

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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

NLP 98

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Unite.AI

MARCH 26, 2024

The development of Large Language Models (LLMs) built from decoder-only transformer models has played a crucial role in transforming the Natural Language Processing (NLP) domain, as well as advancing diverse deep learning applications including reinforcement learning , time-series analysis, image processing, and much more.

Natural Language Processing 147

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Marktechpost

JULY 16, 2023

This idea is based on “example packing,” a technique used in natural language processing to efficiently train models with inputs of varying lengths by combining several instances into a single sequence. The resulting NaViT models are flexible and easy to adapt to new jobs without breaking the bank.

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Convolutional Neural Networks Neural Network Computer Vision Natural Language Processing 98

Pickl AI

SEPTEMBER 3, 2024

Here are some core responsibilities and applications of ANNs: Pattern Recognition ANNs excel in recognising patterns within data , making them ideal for tasks such as image recognition, speech recognition, and natural language processing. Frequently Asked Questions What are the main types of Artificial Neural Network?

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Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 52

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. This is precisely where the You Only Look Once (YOLO) algorithm shines.

Computer Vision 52

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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.

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Convolutional Neural Networks Data Mining Categorization Machine Learning 188

Heartbeat

MARCH 8, 2023

Happy Reading, Emilie, Abby & the Heartbeat Team Natural Language Processing With SpaCy (A Python Library) — by Khushboo Kumari This post goes over how the most cutting-edge NLP software, SpaCy , operates. Finally, we will look at some of the recent semi-supervised medical image segmentation algorithms.

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Topbots

OCTOBER 4, 2023

Generated with Bing and edited with Photoshop Predictive AI has been driving companies’ ROI for decades through advanced recommendation algorithms, risk assessment models, and fraud detection tools. The predictive AI algorithms can be used to predict a wide range of variables, including continuous variables (e.g.,

Generative AI 113

Generative AI Natural Language Processing Convolutional Neural Networks Machine Learning 113

Pickl AI

NOVEMBER 13, 2024

Despite challenges like vanishing gradients, innovations like advanced optimisers and batch normalisation have improved their efficiency, enabling neural networks to solve complex problems. Backpropagation in Neural Networks is vital in training these systems by efficiently updating weights to minimise errors.

Neural Network 52

Neural Network Convolutional Neural Networks Algorithm Natural Language Processing 52

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 80

Machine Learning Neural Network Convolutional Neural Networks Natural Language Processing 80

How to Learn Machine Learning

MAY 14, 2023

Mathematics is crucial because machine learning algorithms are built on concepts such as linear algebra, calculus, probability, and statistics. Familiarity with these subjects will enable you to understand and implement machine learning algorithms more effectively.

Machine Learning 52

Machine Learning Convolutional Neural Networks Natural Language Processing Deep Learning 52

Viso.ai

OCTOBER 10, 2024

Pattern recognition is the ability of machines to identify patterns in data, and then use those patterns to make decisions or predictions using computer algorithms. At the heart of a pattern recognition system are computer algorithms that are designed to analyze and interpret data.

Neural Network 98

Neural Network Computer Vision Deep Learning Machine Learning 98

Pickl AI

OCTOBER 2, 2024

Some of the most prominent AI techniques used in this field include: Machine Learning Machine Learning algorithms are designed to learn from data and make predictions or decisions based on that data. Recurrent Neural Networks (RNNs): Suitable for sequential Data Analysis like DNA sequences where the order of nucleotides matters.

Neural Network 52

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Pickl AI

JULY 12, 2024

Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data.

Artificial Intelligence 52

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Pickl AI

AUGUST 1, 2024

Introduction Deep Learning engineers are specialised professionals who design, develop, and implement Deep Learning models and algorithms. They work on complex problems that require advanced neural networks to analyse vast amounts of data.

Deep Learning 52

Deep Learning Neural Network Machine Learning Natural Language Processing 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). RNNs have been extensively employed for natural language processing tasks such as speech recognition and language translation.

Python 52

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Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. Transformer models have become the de-facto status quo in Natural Language Processing (NLP).

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

Mlearning.ai

JULY 11, 2023

Also, learn about common algorithms used in machine learning. Step 3: Explore Neural Networks and Deep Learning Basics Deep learning is based on neural networks. Start by understanding the basics of neural networks, which are networks of connected nodes that imitate the human brain.

Deep Learning 40

Deep Learning Neural Network Convolutional Neural Networks Computer Vision 40

Becoming Human

MARCH 16, 2023

This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications. Through the next series of this post here , I will try to make an implementation of Graph Convolutional Neural Network. So, let’s get started! What are Graphs?

Neural Network 87

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Viso.ai

MARCH 20, 2024

The main goal of foundation models is to serve as a starting point or foundational building block for many AI tasks, like natural language processing , question answering, text generation, summarization, and even code generation. It builds algorithms to identify objects, analyze scenes, and track motion.

AI Developer 52

AI Developer AI Development Computer Vision BERT 52

Pickl AI

SEPTEMBER 25, 2024

Medical Image Analysis Deep Learning algorithms analyse medical images such as X-rays, MRIs, and CT scans to detect anomalies like tumours or fractures. Algorithmic Trading AI-driven trading systems use Deep Learning to analyse market trends and execute trades at optimal times.

Deep Learning 52

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Pickl AI

AUGUST 5, 2024

This beginner’s guide aims to provide a comprehensive overview of DRL, covering the fundamental concepts, key algorithms, and practical applications. Recurrent Neural Networks (RNNs) : Suitable for handling sequential data and modelling temporal dependencies.

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