Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

Neural Network 158

Neural Network Convolutional Neural Networks BERT Deep Learning 158

ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out his talk, “ Bagging to BERT — A Tour of Applied NLP ,” there! In this post, I’ll be demonstrating two deep learning approaches to sentiment analysis. Deep learning refers to the use of neural network architectures, characterized by their multi-layer design (i.e.

Deep Learning 98

Deep Learning Convolutional Neural Networks NLP Neural Network 98

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.

Generative AI 243

Generative AI ChatGPT Neural Network Convolutional Neural Networks 243

ODSC - Open Data Science

MARCH 10, 2025

By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow. Sessions on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) started gaining popularity, marking the beginning of data sciences shift toward AI-driven methods.

Data Science 52

Data Science Neural Network Convolutional Neural Networks Large Language Models 52

Unite.AI

OCTOBER 22, 2024

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. 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 144

Neural Network Convolutional Neural Networks Deep Learning Natural Language Processing 144

Unite.AI

JUNE 21, 2024

Models such as GPT, BERT , and more recently Llama , Mistral are capable of understanding and generating human-like text with unprecedented fluency and coherence. The Rise of CUDA-Accelerated AI Frameworks GPU-accelerated deep learning has been fueled by the development of popular AI frameworks that leverage CUDA for efficient computation.

Deep Learning 130

Deep Learning Neural Network Convolutional Neural Networks Large Language Models 130

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.

Natural Language Processing 57

Natural Language Processing Convolutional Neural Networks Neural Network Computer Vision 57

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

AssemblyAI

SEPTEMBER 22, 2023

Long-term coherence (semantic modeling) tokens :  A second component based on w2v-BERT , generates 25 semantic tokens per second that represent features of large-scale composition , such as motifs, or consistency in the timbres. It was pre-trained to generate masked tokens in speech and fine-tuned on 8,200 hours of music.

Convolutional Neural Networks 246

Convolutional Neural Networks AI AI Data Scarcity 246

Mlearning.ai

APRIL 8, 2023

2003) “ Support-vector networks ” by Cortes and Vapnik (1995) Significant people : David Blei Corinna Cortes Vladimir Vapnik 4. Deep Learning (Late 2000s — early 2010s) With the evolution of needing to solve more complex and non-linear tasks, The human understanding of how to model for machine learning evolved.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

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. In summary, ReffAKD offers a valuable contribution to the deep learning community by democratizing knowledge distillation.

Machine Learning 102

Machine Learning Neural Network Convolutional Neural Networks Computer Vision 102

Viso.ai

MARCH 26, 2024

The introduction of the Transformer model was a significant leap forward for the concept of attention in deep learning. Uniquely, this model did not rely on conventional neural network architectures like convolutional or recurrent layers. without conventional neural networks. Vaswani et al.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

AWS Machine Learning Blog

FEBRUARY 24, 2023

Solution overview For an introduction to MMEs and MMEs with GPU, refer to Create a Multi-Model Endpoint and Run multiple deep learning models on GPU with Amazon SageMaker multi-model endpoints. The impact is more for models using a convolutional neural network (CNN). Deploy a SageMaker MME on a GPU instance.

BERT 104

BERT NLP Computer Vision Neural Network 104

Unite.AI

APRIL 2, 2024

Recent deep learning methods have displayed stronger and more consistent performance when compared to traditional image restoration methods. These deep learning image restoration models propose to use neural networks based on Transformers and Convolutional Neural Networks.

Computer Vision 130

Computer Vision Neural Network Convolutional Neural Networks Deep Learning 130

Viso.ai

APRIL 26, 2024

Object detection systems typically use frameworks like Convolutional Neural Networks (CNNs) and Region-based CNNs (R-CNNs). Concept of Convolutional Neural Networks (CNN) However, in prompt object detection systems, users dynamically direct the model with many tasks it may not have encountered before.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

Topbots

OCTOBER 4, 2023

Image processing : Predictive image processing models, such as convolutional neural networks (CNNs), can classify images into predefined labels (e.g., They can be based on basic machine learning models like linear regression, logistic regression, decision trees, and random forests. sales volume) and binary variables (e.g.,

Generative AI 119

Generative AI Machine Learning Natural Language Processing Convolutional Neural Networks 119

Heartbeat

JUNE 6, 2023

GCNs use a combination of graph-based representations and convolutional neural networks to analyze large amounts of textual data. A GCN consists of multiple layers, each of which applies a graph convolution operation to the input graph. References Paperwithcode | Graph Convolutional Network Kai, S.,

NLP 59

NLP Convolutional Neural Networks Neural Network Natural Language Processing 59

Viso.ai

MARCH 20, 2024

Models like GPT 4, BERT, DALL-E 3, CLIP, Sora, etc., Use Cases for Foundation Models Applications in Pre-trained Language Models like GPT, BERT, Claude, etc. Examples include GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), Claude, etc. with labeled data.

AI Developer 52

AI Developer AI Development Computer Vision BERT 52

Pickl AI

AUGUST 14, 2024

Neural networks come in various forms, each designed for specific tasks: Feedforward Neural Networks (FNNs) : The simplest type, where connections between nodes do not form cycles. The resurgence of neural networks in the 1980s was marked by the development of backpropagation, a method for training multi-layer networks.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Viso.ai

JULY 9, 2024

Efficient, quick, and cost-effective learning processes are crucial for scaling these models. Transfer Learning is a key technique implemented by researchers and ML scientists to enhance efficiency and reduce costs in Deep learning and Natural Language Processing. Why do we need transfer learning?

Neural Network 52

Neural Network BERT Deep Learning Computer Vision 52

NLP People

JULY 24, 2019

The rise of NLP in the past decades is backed by a couple of global developments – the universal hype around AI, exponential advances in the field of Deep Learning and an ever-increasing quantity of available text data. This is especially relevant for the advanced, complex algorithms of the Deep Learning family.

NLP 52

NLP Neural Network Deep Learning Natural Language Processing 52

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. No 2018 Oct BERT Pre-trained transformer models started dominating the NLP field.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

DataRobot Blog

MARCH 9, 2022

With that said, recent advances in deep learning methods have allowed models to improve to a point that is quickly approaching human precision on this difficult task. Since convolutions occur on adjacent words, the model can pick up on negations or n-grams that carry novel sentiment information. Sentiment analysis datasets.

Machine Learning 52

Machine Learning Neural Network Convolutional Neural Networks Deep Learning 52

Viso.ai

DECEMBER 18, 2023

ONNX (Open Neural Network Exchange) is an open-source format that facilitates interoperability between different deep learning algorithms for simple model sharing and deployment. ONNX (Open Neural Network Exchange) is an open-source format. A deep learning framework from Microsoft.

Explainability 52

Explainability Neural Network Deep Learning Machine Learning 52

Viso.ai

DECEMBER 27, 2023

The concept of CLIP is based on contrastive learning methods What is an example of contrast learning? In a computer vision example of contrast learning, we aim to train a tool like a convolutional neural network to bring similar image representations closer and separate the dissimilar ones.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network NLP 52

Viso.ai

DECEMBER 22, 2023

This leap forward is due to the influence of foundation models in NLP, such as GPT and BERT. These models revolutionized how machines understand and generate human language by learning from vast data, allowing them to generalize across various tasks. In this free live instance , the user can interactively segment objects and instances.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Auto-classification 52

Mlearning.ai

JUNE 8, 2023

A few embeddings for different data type For text data, models such as Word2Vec , GLoVE , and BERT transform words, sentences, or paragraphs into vector embeddings. Images can be embedded using models such as convolutional neural networks (CNNs) , Examples of CNNs include VGG , and Inception. using its Spectrogram ).

Machine Learning 52

Machine Learning BERT Neural Network Convolutional Neural Networks 52

Pickl AI

DECEMBER 9, 2024

Neural Networks Neural networks, particularly deep learning models, introduce a strong inductive bias favouring the discovery of complex, non-linear relationships in large datasets. deep learning models with insufficient data) might overfit the training data. A high-bias model (e.g.,

Machine Learning 52

Machine Learning Neural Network Convolutional Neural Networks Natural Language Processing 52

Heartbeat

FEBRUARY 8, 2024

Building the Model Deep learning techniques have proven to be highly effective in performing cross-modal retrieval. 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

TensorFlow

MARCH 29, 2023

ZenDNN ZenDNN, which is available open-source from GitHub , is a low-level AMD deep neural network library that includes basic neural network building blocks optimized for AMD EPYC CPUs. For the ZenDNN plug-in, AOCL BLIS 3.0.6, Tensorflow 2.12, ZenDNN version 3.3; for Direct Integration AOCL BLIS 4.0,

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Viso.ai

MARCH 26, 2024

With advancements in machine learning (ML) and deep learning (DL), AI has begun to significantly influence financial operations. Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes.

Computer Vision 52

Computer Vision Neural Network Machine Learning Convolutional Neural Networks 52

Heartbeat

OCTOBER 12, 2023

One of the standout achievements in this domain is the development of models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers). They owe their success to many factors, including substantial computational resources, vast training data, and sophisticated architectures.

Large Language Models 59

Large Language Models Neural Network Convolutional Neural Networks Artificial Intelligence 59

Mlearning.ai

MARCH 30, 2023

To understand this let’s take an example of a widely used neural network, CNN (Convolutional Neural Network) CNN is made of two parts: C — Convolution NN — Neural Network The most important part of training is feature extraction which is done by the convolutional part of the CNN.

Neural Network 52

Neural Network AI Developer AI Development Convolutional Neural Networks 52

Pragnakalp

JANUARY 1, 2024

The concept of a transformer, an attention-layer-based, sequence-to-sequence (“Seq2Seq”) encoder-decoder architecture, was conceived in a 2017 paper authored by pioneer in deep learning models Ashish Vaswani et al called “Attention Is All You Need”.

Neural Network 52

Neural Network Natural Language Processing Convolutional Neural Networks NLP 52

Heartbeat

SEPTEMBER 20, 2023

Object Detection Image from a personal computer Convolutional neural networks (CNNs) are utilized in object detection algorithms to identify and locate objects based on their visual attributes accurately. 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

ML Review

FEBRUARY 11, 2019

If you don’t know it already, NLP had a huge hype of transfer learning in this past 1 year, starting with ULMFit , ELMo , GLoMo , OpenAI transformer , BERT and recently Transformer-XL for further improving language modeling capabilities of the current state of the art. overfitting.[1,2]

Neural Network 52

Neural Network NLP Explainability Convolutional Neural Networks 52

Unite.AI

FEBRUARY 8, 2025

Models like BERT and GPT took language understanding to new depths by grasping the context of words more effectively. Vision Transformers (ViTs) have significantly advanced computer vision by using attention mechanisms instead of the traditional convolutional layers.

Convolutional Neural Networks 266

Convolutional Neural Networks Neural Network Natural Language Processing Robotics 266