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. Models such as Long Short-Term Memory (LSTM) networks and Transformers (e.g., Data moves in one direction—from input to output.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Mlearning.ai

JUNE 8, 2023

Vector Embeddings for Developers: The Basics | Pinecone Used geometry concept to explain what is vector, and how raw data is transformed to embedding using embedding model. Pinecone Used a picture of phrase vector to explain vector embedding. What are Vector Embeddings? using its Spectrogram ). All we need is the vectors for the words.

Machine Learning 52

Machine Learning BERT Neural Network Convolutional Neural Networks 52

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

DECEMBER 27, 2023

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. It typically uses a convolutional neural network (CNN) architecture, like ResNet , for extracting image features.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network NLP 52

Mlearning.ai

MARCH 30, 2023

Let me explain this in simple words. Learning is nothing but just getting the value of weights and biases for a neural network that gives you the desired result. A pre-trained model such as BERT or GPT can be used as a starting point and fine-tuned on a specific dataset to perform these tasks.

Neural Network 52

Neural Network AI Development AI Developer Convolutional Neural Networks 52

Viso.ai

MARCH 26, 2024

Furthermore, attention mechanisms work to enhance the explainability or interpretability of AI models. Source ) This has led to groundbreaking models like GPT for generative tasks and BERT for understanding context in Natural Language Processing ( NLP ).

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

ML Review

DECEMBER 16, 2019

NeurIPS’18 presented several papers with deep theoretical studies of building hyperbolic neural nets. Source: Chami et al Chami et al present Hyperbolic Graph Convolutional Neural Networks (HGCN) and Liu et al propose Hyperbolic Graph Neural Networks (HGNN).

Machine Learning 52

Machine Learning Neural Network Explainability NLP 52

Viso.ai

MARCH 26, 2024

Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. On the other hand, NLP frameworks like BERT help in understanding the context and content of documents. Overcoming the ‘black box’ nature of AI for transparent and explainable AI systems.

Computer Vision 52

Computer Vision Neural Network Machine Learning Convolutional Neural Networks 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. Apply random dropout per input sequence.

Neural Network 52

Neural Network NLP Explainability 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

Marek Rei

NOVEMBER 12, 2019

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. link] Constructing a system for NLI that explains its decisions by pointing to the most relevant parts of the input. NAACL 2019. Cambridge, Amazon. NAACL 2019. Tison, Codie Bourn, Mintu P.

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

Heartbeat

FEBRUARY 8, 2024

Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively. Building the Model Deep learning techniques have proven to be highly effective in performing cross-modal retrieval.

Neural Network 52

Neural Network Deep Learning Convolutional Neural Networks Computer Vision 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