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.

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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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AWS Machine Learning Blog

MARCH 21, 2025

JupyterLab applications flexible and extensive interface can be used to configure and arrange machine learning (ML) workflows. We use JupyterLab to run the code for processing formulae and charts. Generate metadata Using natural language processing, you can generate metadata for the paper to aid in searchability.

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Heartbeat

AUGUST 21, 2023

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs learn geometric properties on different scales by applying convolutional filters to input data.

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AssemblyAI

JANUARY 23, 2025

Modern speech recognition systems often leverage machine learning and artificial intelligence, allowing them to handle various accents, languages, and speaking styles with impressive accuracy. It works by analyzing audio signals, identifying patterns, and matching them to words and phrases using advanced algorithms.

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Marktechpost

NOVEMBER 27, 2024

In today’s world, you’ve probably heard the term “Machine Learning” more than once. Machine Learning, a subset of Artificial Intelligence, has emerged as a transformative force, empowering machines to learn from data and make intelligent decisions without explicit programming. housing prices, stock prices).

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Marktechpost

JANUARY 30, 2025

Contrastingly, agentic systems incorporate machine learning (ML) and artificial intelligence (AI) methodologies that allow them to adapt, learn from experience, and navigate uncertain environments. Natural Language Processing (NLP): Text data and voice inputs are transformed into tokens using tools like spaCy.

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Marktechpost

JUNE 3, 2024

This article lists top Intel AI courses, including those on deep learning, NLP, time-series analysis, anomaly detection, robotics, and edge AI deployment, providing a comprehensive learning path for leveraging Intel’s AI technologies. Deep Learning for Robotics This course teaches applying machine learning to robotics.

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Unite.AI

AUGUST 2, 2023

Machine learning models have heavily relied on labeled data for training, and traditionally speaking, training models on labeled data yields accurate results. To tackle the annotation issue, developers came up with the concept of SSL or Self Supervised Learning. They require a high amount of computational power.

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Marktechpost

SEPTEMBER 17, 2024

One of the central challenges in this field is the extended time needed to train complex neural networks. In one experiment on a language task, the baseline Adam optimizer required 23,500 steps to reach the target perplexity, while NINO achieved the same performance in just 11,500 steps. reduction in training time.

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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.

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Pickl AI

JANUARY 29, 2025

In this blog, we will explore over ten beginner-friendly Deep Learning projects that will help you apply what you’ve learned and develop new skills. Whether you’re interested in image recognition, natural language processing, or even creating a dating app algorithm, theres a project here for everyone.

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Viso.ai

JANUARY 2, 2024

In the following, we will explore Convolutional Neural Networks (CNNs), a key element in computer vision and image processing. Whether you’re a beginner or an experienced practitioner, this guide will provide insights into the mechanics of artificial neural networks and their applications.

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Pickl AI

APRIL 6, 2025

Neural Networks are foundational structures, while Deep Learning involves complex, layered networks like CNNs and RNNs, enabling advanced AI capabilities such as image recognition and natural language processing. Deep Learning Complexity : Involves multiple layers for advanced AI tasks.

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Pickl AI

NOVEMBER 22, 2024

This leads to the vanishing gradient problem, making it difficult for RNNs to retain information from earlier time steps when processing long sequences. LSTMs are crucial for natural language processing tasks. Key Takeaways LSTMs address the vanishing gradient problem in RNNs. In What Applications Are LSTMS Commonly Used?

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Marktechpost

APRIL 20, 2024

Deep neural networks like convolutional neural networks (CNNs) have revolutionized various computer vision tasks, from image classification to object detection and segmentation. As models grew larger and more complex, their accuracy soared. If you like our work, you will love our newsletter.

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Marktechpost

JULY 31, 2024

TensorFlow is a powerful open-source framework for building and deploying machine learning models. Learning TensorFlow enables you to create sophisticated neural networks for tasks like image recognition, natural language processing, and predictive analytics.

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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.

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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.

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Pickl AI

JANUARY 26, 2025

Introduction Deep Learning models transform how we approach complex problems, offering powerful tools to analyse and interpret vast amounts of data. These models mimic the human brain’s neural networks, making them highly effective for image recognition, natural language processing, and predictive analytics.

Deep Learning 52

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Towards AI

MARCH 3, 2025

Can machines understand human language? These questions are addressed by the field of Natural Language processing, which allows machines to mimic human comprehension and usage of natural language. What if we could interact with them the same way we do with other humans?

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Marktechpost

MAY 24, 2024

Transformers have greatly transformed natural language processing, delivering remarkable progress across various applications. Research on sparsity for model pruning is a significant focus in machine learning.

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Marktechpost

JANUARY 15, 2025

Traditional machine learning models, while effective in many scenarios, often struggle to process high-dimensional and unstructured data without extensive preprocessing and feature engineering. This gap has led to the evolution of deep learning models, designed to learn directly from raw data.

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Marktechpost

MARCH 20, 2025

Let’s create a small dataset of abstracts from various fields: Copy Code Copied Use a different Browser abstracts = [ { "id": 1, "title": "Deep Learning for Natural Language Processing", "abstract": "This paper explores recent advances in deep learning models for natural language processing tasks.

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Artificial Corner

JANUARY 23, 2025

Some of the datasets used in this course to teach the libraries mentioned before are: Pokemon Dataset FIFA 20 Dataset Amazon Product Review Dataset Sales Data So if you want to have fun analyzing Pokemon and FIFA datasets or build a machine-learning model using product review, consider watching this playlist. That’s it!

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How to Learn Machine Learning

AUGUST 15, 2024

In this blog post we will speak about how to create a Machine Learning Portfolio, a very useful piece of content for landing a job on Artificial Intelligence and creating a data base of knowledge for future use. How to Create a Machine Learning Portfolio Creating an effective portfolio requires careful planning and execution.

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ODSC - Open Data Science

MARCH 10, 2025

Over the past decade, data science has undergone a remarkable evolution, driven by rapid advancements in machine learning, artificial intelligence, and big data technologies. By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow.

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Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. They consist of interconnected nodes that learn complex patterns in data. Reinforcement Learning: An agent learns to make decisions by receiving rewards or penalties based on its actions within an environment.

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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. So let’s get started.

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How to Learn Machine Learning

DECEMBER 15, 2024

In the rapidly evolving world of technology, machine learning has become an essential skill for aspiring data scientists, software engineers, and tech professionals. Coursera Machine Learning Courses are an exceptional array of courses that can transform your career and technical expertise.

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Pickl AI

NOVEMBER 20, 2024

Summary: Linear Algebra is foundational to Machine Learning, providing essential operations such as vector and matrix manipulations. Introduction Linear Algebra is a fundamental mathematical discipline that underpins many algorithms and techniques in Machine Learning.

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AWS Machine Learning Blog

JANUARY 21, 2025

In this post, we show you how Amazon Web Services (AWS) helps in solving forecasting challenges by customizing machine learning (ML) models for forecasting. To learn more about the modalities that Amazon SageMaker Canvas supports, visit the Amazon SageMaker Canvas product page.

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Heartbeat

SEPTEMBER 20, 2023

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

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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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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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Marktechpost

NOVEMBER 10, 2023

Traditionally, Convolutional Neural Networks (CNNs) have been the go-to models for processing image data, leveraging their ability to extract meaningful features and classify visual information.

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PyImageSearch

MARCH 30, 2023

If you want a gentle introduction to machine learning for computer vision, you’re in the right spot. Here at PyImageSearch we’ve been helping people just like you master deep learning for computer vision. Also, you might want to check out our computer vision for deep learning program before you go.

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