Marktechpost

NOVEMBER 27, 2024

At its core, machine learning algorithms seek to identify patterns within data, enabling computers to learn and adapt to new information. 2) Logistic regression Logistic regression is a classification algorithm used to model the probability of a binary outcome. Think about how a child learns to recognize a cat. game playing, robotics).

Machine Learning 113

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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.

Convolutional Neural Networks 120

Convolutional Neural Networks Neural Network Deep Learning Computer Vision 120

Unite.AI

OCTOBER 22, 2024

This term refers to how much time, memory, or processing power an algorithm requires as the size of the input grows. AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands.

Neural Network 146

Neural Network Convolutional Neural Networks Deep Learning Natural Language Processing 146

Marktechpost

DECEMBER 10, 2024

1943: McCulloch and Pitts created a mathematical model for neural networks, marking the theoretical inception of ANNs. 1958: Frank Rosenblatt introduced the Perceptron , the first machine capable of learning, laying the groundwork for neural network applications. How Do Artificial Neural Networks Work?

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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 120

Convolutional Neural Networks Neural Network Natural Language Processing Machine Learning 120

Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. Various algorithms are employed in image captioning, including: 1. These algorithms can learn and extract intricate features from input images by using convolutional layers.

Natural Language Processing 52

Natural Language Processing Computer Vision NLP Algorithm 52

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?

Convolutional Neural Networks 98

Convolutional Neural Networks Neural Network Computer Vision Emotion AI 98

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.

Convolutional Neural Networks 130

Convolutional Neural Networks Neural Network BERT Natural Language Processing 130

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 146

Natural Language Processing Neural Network Convolutional Neural Networks Large Language Models 146

Heartbeat

MAY 2, 2023

Deep learning allows machines to learn from vast amounts of data and carry out complex tasks that were previously only considered possible by humans (like translation between languages, recognizing objects etc.). In this article, we will examine some different popular deep learning algorithms and how they operate.

Deep Learning 52

Deep Learning Convolutional Neural Networks Algorithm Neural Network 52

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.

Data Mining 170

Data Mining Convolutional Neural Networks Categorization Machine Learning 170

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

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

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.

Convolutional Neural Networks 98

Convolutional Neural Networks Neural Network Computer Vision Natural Language Processing 98

Pickl AI

AUGUST 3, 2023

Introduction to Deep Learning Algorithms: Deep learning algorithms are a subset of machine learning techniques that are designed to automatically learn and represent data in multiple layers of abstraction. This process is known as training, and it relies on large amounts of labeled data. How Deep Learning Algorithms Work?

Deep Learning 52

Deep Learning Machine Learning Algorithm Neural Network 52

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.

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

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.

Convolutional Neural Networks 97

Convolutional Neural Networks Neural Network Large Language Models Natural Language Processing 97

John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. A VQA system takes free-form, text-based questions about an input image and presents answers in a natural language format.

Natural Language Processing 40

Natural Language Processing Deep Learning NLP Convolutional Neural Networks 40

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 Machine Learning Convolutional Neural Networks 113

Unite.AI

JULY 31, 2023

Self-supervised learning has already shown its results in Natural Language Processing as it has allowed developers to train large models that can work with an enormous amount of data, and has led to several breakthroughs in fields of natural language inference, machine translation, and question answering.

Computer Vision 246

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Marktechpost

DECEMBER 12, 2024

The deep aspect of DNNs comes from multiple hidden layers, which allow the network to learn and model complex patterns and relationships in data. DNNs are the backbone of many advanced artificial intelligence applications, including image recognition, natural language processing, and autonomous systems.

Neural Network 57

Neural Network Convolutional Neural Networks Natural Language Processing Artificial Intelligence 57

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.

Convolutional Neural Networks 59

Convolutional Neural Networks Neural Network Natural Language Processing Machine Learning 59

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

Neural Network Auto-classification Convolutional Neural Networks Auto-complete 87

ODSC - Open Data Science

NOVEMBER 28, 2024

NVIDIA’s AI Tools Suite to Aid in Accelerated Humanoid Robotics Development NVIDIA’s AI tools suite may drive developers toward complex machine learning and natural language processing solutions. In all likelihood, AI technology and humanoid robotics will progress hand in hand in the coming years.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network LLM Robotics 52

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

Marktechpost

JUNE 27, 2023

The technology may have meaningful interactions with consumers because it uses machine learning and natural language processing. Using sophisticated algorithms, it can determine where an object’s borders are and then either replace the background with a translucent one or eliminate it.

AI Tools 98

AI Tools Convolutional Neural Networks Artificial Intelligence Artificial Intelligence 98

Viso.ai

DECEMBER 20, 2023

Supervised learning is a type of machine learning algorithm that learns from a set of training data that has been labeled training data. Typical computer vision tasks of supervised learning algorithms include object detection, visual recognition, and classification. from a set of images. to an image. for image data compression).

Computer Vision 98

Computer Vision Machine Learning Neural Network Algorithm 98

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 72

Machine Learning Neural Network Convolutional Neural Networks Natural Language Processing 72

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

How to Learn Machine Learning

DECEMBER 15, 2024

Covering a comprehensive range of topics, the course provides a deep dive into the fundamental principles and practical applications of machine learning algorithms. This professional certificate provides a holistic approach to machine learning, combining theoretical knowledge with practical skills.

Machine Learning 52

Machine Learning Neural Network Convolutional Neural Networks Software Engineer 52

Viso.ai

FEBRUARY 23, 2024

Prompt-based Segmentation combines the power of natural language processing (NLP) and computer vision to create an image segmentation model. Modern video segmentation algorithms improve their results by utilizing frame pixels and causal information. One such development is prompt-based segmentation.

Computer Vision 98

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 98

Pickl AI

DECEMBER 9, 2024

Types of inductive bias include prior knowledge, algorithmic bias, and data bias. Types of Inductive Bias Inductive bias plays a significant role in shaping how Machine Learning algorithms learn and generalise. This bias allows algorithms to make informed guesses when faced with incomplete or sparse data.

Machine Learning 52

Machine Learning Neural Network Convolutional Neural Networks Natural Language Processing 52

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

Deep Learning Convolutional Neural Networks Neural Network Natural Language Processing 52

Viso.ai

DECEMBER 1, 2023

In short, supervised learning is where the algorithm is given a set of training data. In computer vision, this process is called image annotation. End-to-end data collection and image annotation with Viso Suite On the other hand, unsupervised learning is where the algorithm is given raw data that is not annotated.

AI Modeling 98

AI Modeling Neural Network Deep Learning Computer Vision 98

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

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

Computer Vision Deep Learning NLP Neural Network 52

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?

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 52

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