Marktechpost

APRIL 5, 2024

King’s College London researchers have highlighted the importance of developing a theoretical understanding of why transformer architectures, such as those used in models like ChatGPT, have succeeded in natural language processing tasks. Check out the Paper. Also, don’t forget to follow us on Twitter.

Neural Network 128

Neural Network Natural Language Processing Categorization Explainability 128

Pickl AI

NOVEMBER 27, 2024

Summary: Gated Recurrent Units (GRUs) enhance Deep Learning by effectively managing long-term dependencies in sequential data. Their applications span various fields, including natural language processing, time series forecasting, and speech recognition, making them a vital tool in modern AI.

Deep Learning 52

Deep Learning Neural Network Natural Language Processing NLP 52

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. Each neuron receives input, processes it, and passes its output to the next layer. Data moves in one direction—from input to output.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Pickl AI

SEPTEMBER 3, 2024

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 52

Pickl AI

NOVEMBER 13, 2024

Summary: Backpropagation in neural network optimises models by adjusting weights to reduce errors. Despite challenges like vanishing gradients, innovations like advanced optimisers and batch normalisation have improved their efficiency, enabling neural networks to solve complex problems.

Neural Network 52

Neural Network Convolutional Neural Networks Algorithm Natural Language Processing 52

Machine Learning Mastery

JUNE 14, 2024

Deep learning models achieve state-of-the-art performance in several computer vision and natural language processing tasks. To help you get started, we’ve compiled a […] The post 5 Free Resources for Understanding Neural Networks appeared first on MachineLearningMastery.com.

Neural Network 65

Neural Network Natural Language Processing Computer Vision Deep Learning 65

Marktechpost

JANUARY 24, 2024

With the growth of Deep learning, it is used in many fields, including data mining and natural language processing. However, deep neural networks are inaccurate and can produce unreliable outcomes. It can improve deep neural networks’ reliability in inverse imaging issues.

Neural Network 115

Neural Network Deep Learning Data Mining Natural Language Processing 115

Pickl AI

NOVEMBER 24, 2024

Summary: Autoencoders are powerful neural networks used for deep learning. Their applications include dimensionality reduction, feature learning, noise reduction, and generative modelling. An autoencoder is a neural network designed to learn a compressed representation of input data.

Deep Learning 52

Deep Learning Neural Network Natural Language Processing Computer Vision 52

Marktechpost

SEPTEMBER 17, 2024

In deep learning, neural network optimization has long been a crucial area of focus. Training large models like transformers and convolutional networks requires significant computational resources and time. Researchers have been exploring advanced optimization techniques to make this process more efficient.

Neural Network 116

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 116

Marktechpost

MAY 8, 2024

Over the past decade, advancements in deep learning and artificial intelligence have driven significant strides in self-driving vehicle technology. These technologies have revolutionized computer vision, robotics, and natural language processing and played a pivotal role in the autonomous driving revolution.

Deep Learning 124

Deep Learning Neural Network Data Scarcity Natural Language Processing 124

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

Pickl AI

NOVEMBER 28, 2024

Summary: Gradient descent is a fundamental optimisation technique in Deep Learning crucial for minimising loss functions and enhancing model accuracy. Understanding its types—Batch, Stochastic, and Mini-batch Gradient Descent—enables effective training of complex neural networks. What is Gradient Descent?

Deep Learning 52

Deep Learning Neural Network Convolutional Neural Networks Algorithm 52

Pickl AI

NOVEMBER 13, 2024

Summary: Batch Normalization in Deep Learning improves training stability, reduces sensitivity to hyperparameters, and speeds up convergence by normalising layer inputs. It’s a crucial technique in modern neural networks, enhancing performance and generalisation. The global Deep Learning market, valued at $17.60

Deep Learning 52

Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 52

Pickl AI

SEPTEMBER 23, 2024

Summary: This guide covers the most important Deep Learning interview questions, including foundational concepts, advanced techniques, and scenario-based inquiries. Gain insights into neural networks, optimisation methods, and troubleshooting tips to excel in Deep Learning interviews and showcase your expertise.

Deep Learning 52

Deep Learning Neural Network Natural Language Processing Convolutional Neural Networks 52

Marktechpost

MARCH 30, 2024

In 2024, the landscape of Python libraries for machine learning and deep learning continues to evolve, integrating more advanced features and offering more efficient and easier ways to build, train, and deploy models. PyTorch PyTorch is a widely used open-source machine learning library based on the Torch library.

Deep Learning 124

Deep Learning Machine Learning Python Computer Vision 124

Pickl AI

AUGUST 1, 2024

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. Introduction Deep Learning engineers are specialised professionals who design, develop, and implement Deep Learning models and algorithms.

Deep Learning 52

Deep Learning Neural Network Machine Learning Natural Language Processing 52

Marktechpost

MAY 5, 2024

In deep learning, especially in NLP, image analysis, and biology, there is an increasing focus on developing models that offer both computational efficiency and robust expressiveness. The ever-increasing need for processing larger and more complex datasets has driven researchers to find more efficient and scalable solutions.

Deep Learning 134

Deep Learning BERT Neural Network Natural Language Processing 134

Marktechpost

OCTOBER 18, 2023

One of the biggest challenges in Machine Learning has always been to train and use neural networks efficiently. In recent research, a team of researchers has introduced a deep learning compiler specifically made for neural network training.

Deep Learning 124

Deep Learning Neural Network Natural Language Processing Computer Vision 124

Pickl AI

SEPTEMBER 16, 2024

Summary : Deep Belief Networks (DBNs) are Deep Learning models that use Restricted Boltzmann Machines and feedforward networks to learn hierarchical features and model complex data distributions. Among these networks, the Deep Belief Network (DBN) stands out due to its hierarchical structure.

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network Natural Language Processing 52

Viso.ai

MAY 17, 2024

Neural Networks have changed the way we perform model training. This gave birth to a new domain called Deep Learning. Neural networks, sometimes referred to as Neural Nets, need large datasets for efficient training. Liquid Neural Networks solve the problems posed by traditional networks.

Neural Network 52

Neural Network Deep Learning Computer Vision Artificial Intelligence 52

Towards AI

NOVEMBER 19, 2023

Photo by Pietro Jeng on Unsplash Deep learning is a type of machine learning that utilizes layered neural networks to help computers learn from large amounts of data in an automated way, much like humans do. We will explain intuitively what each one means and how it contributes to the deep learning process.

Deep Learning 109

Deep Learning Neural Network Natural Language Processing Machine Learning 109

Marktechpost

DECEMBER 19, 2023

Advancements in deep learning have influenced a wide variety of scientific and industrial applications in artificial intelligence. Recurrent Neural Networks (RNNs) and Transformers are the most common methods; each has advantages and disadvantages.

Neural Network 135

Neural Network Natural Language Processing Deep Learning Artificial Intelligence 135

Marktechpost

SEPTEMBER 5, 2024

Spiking Neural Networks (SNNs) hold significant promise in developing energy-efficient and biologically plausible artificial neural networks. Overcoming this challenge is essential to enhance the applicability of SNNs in real-world AI tasks, where processing complex sequences with high accuracy and efficiency is vital.

Neural Network 59

Neural Network Natural Language Processing Deep Learning AI 59

Mlearning.ai

JANUARY 26, 2023

Introduction Natural language processing (NLP) sentiment analysis is a powerful tool for understanding people’s opinions and feelings toward specific topics. NLP sentiment analysis uses natural language processing (NLP) to identify, extract, and analyze sentiment from text data.

Natural Language Processing 52

Natural Language Processing NLP Deep Learning Computer Vision 52

Dlabs.ai

DECEMBER 9, 2021

Natural language processing (NLP) is a core part of artificial intelligence. Natural Language Processing Succinctly Author : Joseph D. Booth The first book in our list focuses on machine learning-based NLP. There’s plenty of literature that covers the topic. A simple solution is to ask the experts.

Natural Language Processing 59

Natural Language Processing Neural Network NLP Deep Learning 59

Unite.AI

NOVEMBER 6, 2023

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

Machine Learning 204

Machine Learning Neural Network Deep Learning Convolutional Neural Networks 204

Mlearning.ai

MAY 30, 2023

Recurrent Neural Networks (RNNs) have become a potent tool for analysing sequential data in the large subject of artificial intelligence and machine learning. As we know that Convolutional Neural Network (CNN) is used for structured arrays of data such as image data. RNN is used for sequential data.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing Artificial Intelligence 52

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.

Neural Network 110

Neural Network Auto-classification Natural Language Processing Convolutional Neural Networks 110

Pickl AI

NOVEMBER 28, 2024

Summary: Generative Adversarial Network (GANs) in Deep Learning generate realistic synthetic data through a competitive framework between two networks: the Generator and the Discriminator. In answering the question, “What is a Generative Adversarial Network (GAN) in Deep Learning?”

Deep Learning 52

Deep Learning Neural Network Machine Learning Artificial Intelligence 52

Becoming Human

MARCH 16, 2023

Photo by NASA on Unsplash Hello and welcome to this post, in which I will study a relatively new field in deep learning involving graphs — a very important and widely used data structure. This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications.

Neural Network 87

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

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. For visual question answering in Deep Learning using NLP, public datasets play a crucial role.

Natural Language Processing 40

Natural Language Processing Deep Learning NLP Convolutional Neural Networks 40

Analytics Vidhya

FEBRUARY 3, 2023

For example, researchers predicted that deep neural networks would eventually be used for autonomous image recognition and natural language processing as early as the 1980s. As a result, numerous researchers have focused on creating intelligent machines throughout history.

Neural Network 301

Neural Network Natural Language Processing AI AI 301

Marktechpost

DECEMBER 23, 2023

Deep Neural Networks (DNNs) represent a powerful subset of artificial neural networks (ANNs) designed to model complex patterns and correlations within data. These sophisticated networks consist of multiple layers of interconnected nodes, enabling them to learn intricate hierarchical representations.

Neural Network 126

Neural Network Machine Learning Natural Language Processing Computer Vision 126

Heartbeat

FEBRUARY 8, 2023

Source: Author The field of natural language processing (NLP), which studies how computer science and human communication interact, is rapidly growing. By enabling robots to comprehend, interpret, and produce natural language, NLP opens up a world of research and application possibilities.

Natural Language Processing 52

Natural Language Processing NLP Machine Learning Deep Learning 52

Heartbeat

MAY 2, 2023

Artificial intelligence has undergone a revolution thanks to deep learning. 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.).

Deep Learning 52

Deep Learning Convolutional Neural Networks Algorithm Neural Network 52

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 are artificial neural networks built to handle data having a grid-like architecture, such as photos or movies.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Natural Language Processing Deep Learning 52

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. In AI, particularly in deep learning , this often means dealing with a rapidly increasing number of computations as models grow in size and handle larger datasets.

Neural Network 147

Neural Network Convolutional Neural Networks Deep Learning Natural Language Processing 147