Pickl AI

AUGUST 1, 2023

Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. Types of Machine Learning Model: Machine Learning models can be broadly categorized as: 1. What is Machine Learning?

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Viso.ai

DECEMBER 20, 2023

However, unsupervised learning has its own advantages, such as being more resistant to overfitting (the big challenge of Convolutional Neural Networks ) and better able to learn from complex big data, such as customer data or behavioral data without an inherent structure.

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Viso.ai

MARCH 7, 2023

These neural networks have made significant contributions to computer vision, natural language processing , and anomaly detection, among other fields. The reconstruction error is calculated using various loss functions, such as mean squared error, binary cross-entropy, or categorical cross-entropy.

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ODSC - Open Data Science

APRIL 28, 2023

If a Natural Language Processing (NLP) system does not have that context, we’d expect it not to get the joke. Raw text is fed into the Language object, which produces a Doc object. cats” component of Docs, for which we’ll be training a text categorization model to classify sentiment as “positive” or “negative.”

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The MLOps Blog

FEBRUARY 22, 2024

Methods for continual learning can be categorized as regularization-based, architectural, and memory-based, each with specific advantages and drawbacks. For example, convolutional neural networks achieve significantly better accuracy in continual learning when they use batch normalization and skip connections.

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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?

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Viso.ai

MARCH 26, 2024

This enhances the interpretability of AI systems for applications in computer vision and natural language processing (NLP). Using content-based attention it focuses on relevant words to improve accuracy when translating to the target language. The typical architecture of a neural machine translation model (NMT).

Deep Learning 52

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Heartbeat

AUGUST 29, 2023

Deep learning is a branch of machine learning that makes use of neural networks with numerous layers to discover intricate data patterns. Deep learning models use artificial neural networks to learn from data. Speech and Audio Processing : Speaker identification, speech recognition, creation of music, etc.

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Heartbeat

FEBRUARY 22, 2023

These models have been used to achieve state-of-the-art performance in many different fields, including image classification, natural language processing, and speech recognition. The model is then compiled with the Adam optimizer, the sparse categorical cross-entropy loss function, and accuracy as the metric.

Deep Learning 52

Deep Learning Machine Learning Convolutional Neural Networks Computer Vision 52

Heartbeat

FEBRUARY 8, 2024

Cross-modal retrieval is a branch of computer vision and natural language processing that links visual and verbal descriptions. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively.

Neural Network 52

Neural Network Deep Learning Convolutional Neural Networks Computer Vision 52

Snorkel AI

MARCH 1, 2023

This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al.

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John Snow Labs

JUNE 2, 2023

Sentiment analysis, also known as opinion mining, is the process of computationally identifying and categorizing the subjective information contained in natural language text.

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Explosion

NOVEMBER 9, 2016

Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. A four-step strategy for deep learning with text Embedded word representations, also known as “word vectors”, are now one of the most widely used natural language processing technologies.

Deep Learning 52

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Heartbeat

JANUARY 12, 2023

Other practical examples of deep learning include virtual assistants, chatbots, robotics, image restoration, NLP (Natural Language Processing), and so on. Convolution, pooling, and fully connected layers are just a few components that make up a convolutional neural network.

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network Machine Learning 52

Unite.AI

AUGUST 8, 2023

Instead of complex and sequential architectures like Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs), the Transformer model introduced the concept of attention, which essentially meant focusing on different parts of the input text depending on the context.

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