Unite.AI

DECEMBER 19, 2023

Carl Froggett, is the Chief Information Officer (CIO) of Deep Instinct , an enterprise founded on a simple premise: that deep learning , an advanced subset of AI, could be applied to cybersecurity to prevent more threats, faster. DL is built on a neural network and uses its “brain” to continuously train itself on raw data.

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Unite.AI

JULY 31, 2023

SEER or SElf-supERvised Model: An Introduction Recent trends in the AI & ML industry have indicated that model pre-training approaches like semi-supervised, weakly-supervised, and self-supervised learning can significantly improve the performance for most deep learning models for downstream tasks.

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ODSC - Open Data Science

JANUARY 26, 2023

Deep learning is one of the most crucial tools for analyzing massive amounts of data. However, there is such a prospect as too much information, as deep learning’s job is to find patterns and connections between data points to inform humanity’s questions and affirm assertions.

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AWS Machine Learning Blog

JUNE 22, 2023

The final ML model combines CNN and Transformer, which are the state-of-the-art neural network architectures for modeling sequential machine log data. The ML model takes in the historical sequence of machine events and other metadata and predicts whether a machine will encounter a failure in a 6-hour future time window.

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Neural Network Metadata ML Machine Learning 110

Sebastian Ruder

FEBRUARY 23, 2023

This post gives a brief overview of modularity in deep learning. Fuelled by scaling laws, state-of-the-art models in machine learning have been growing larger and larger. We give an in-depth overview of modularity in our survey on Modular Deep Learning. Case studies of modular deep learning.

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IBM Journey to AI blog

FEBRUARY 14, 2024

Companies also take advantage of ML in smartphone cameras to analyze and enhance photos using image classifiers, detect objects (or faces) in the images, and even use artificial neural networks to enhance or expand a photo by predicting what lies beyond its borders.

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Marktechpost

JULY 7, 2024

Two-Tower Model The two-tower model, also known as the dual-tower model, is a deep learning architecture widely used in recommendation systems. It consists of two separate neural networks: one for encoding user features and the other for encoding item features.

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The MLOps Blog

JUNE 27, 2023

When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Can you compare images?

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The MLOps Blog

MAY 10, 2023

In this section, you will see different ways of saving machine learning (ML) as well as deep learning (DL) models. Saving deep learning model with TensorFlow Keras TensorFlow is a popular framework for training DL-based models, and Ker as is a wrapper for TensorFlow. Now let’s see how we can save our model.

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Python Metadata ML Machine Learning 105

Heartbeat

MARCH 28, 2023

A complete guide to building a deep learning project with PyTorch, tracking an Experiment with Comet ML, and deploying an app with Gradio on HuggingFace Image by Freepik AI tools such as ChatGPT, DALL-E, and Midjourney are increasingly becoming a part of our daily lives. These tools were developed with deep learning techniques.

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AWS Machine Learning Blog

APRIL 5, 2023

Contrastive Language-Image Pre-Training (CLIP) is a neural network trained on a variety of image and text pairs. The CLIP neural network is able to project both images and text into the same latent space , which means that they can be compared using a similarity measure, such as cosine similarity.

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AssemblyAI

SEPTEMBER 22, 2023

The model is trained conditionally on text metadata alongside audio file duration and initiation time. As for any diffusion model , Stable Audio adds noise to the audio vector, which a U-Net Convolutional Neural Network learns to remove, guided by the text and timing embeddings.

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AWS Machine Learning Blog

JANUARY 6, 2023

This mapping can be done by manually mapping frequent OOC queries to catalog content or can be automated using machine learning (ML). In this post, we illustrate how to handle OOC by utilizing the power of the IMDb dataset (the premier source of global entertainment metadata) and knowledge graphs.

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Metadata Machine Learning Data Scientist ML 101

Towards AI

JUNE 4, 2024

Google Deepmind is also working on AI weather models, including graphcast based on graph neural networks last year. Accelerating Transformers with NVIDIA cuDNN 9 The NVIDIA cuDNN is a GPU-accelerated library for accelerating deep learning primitives with state-of-the-art performance. Why should you care?

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ODSC - Open Data Science

MARCH 27, 2023

You will also discuss the interactive plots and tools that help you better understand the learning process in real-time and understand how the models work out-of-sample.

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Flipboard

AUGUST 21, 2023

yolov8n.json : A JSON file inside the yolov8n directory containing the configuration or metadata of the YOLOv8 model. This dictionary holds configurations specific to the neural network, established on Lines 30-35. . ├── main.py ├── output │ └── tracking_result_long.mp4 ├── pyimagesearch │ ├── __init__.py mp4 │ └── example_02.mp4

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Computer Vision Python Neural Network Deep Learning 123

Viso.ai

DECEMBER 18, 2023

As an Edge AI implementation, TensorFlow Lite greatly reduces the barriers to introducing large-scale computer vision with on-device machine learning, making it possible to run machine learning everywhere. TensorFlow Lite is an open-source deep learning framework designed for on-device inference ( Edge Computing ).

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AWS Machine Learning Blog

JANUARY 16, 2024

These artifacts refer to the essential components of a machine learning model needed for various applications, including deployment and retraining. They can include model parameters, configuration files, pre-processing components, as well as metadata, such as version details, authorship, and any notes related to its performance.

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Bugra Akyildiz

FEBRUARY 22, 2025

These limitations motivated the development of an embedding-based retrieval system that could learn representations directly from user engagement data, mirroring the success of deep learning in ranking systems.

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LLM Metadata Large Language Models Artificial Intelligence 59

PyImageSearch

SEPTEMBER 25, 2023

Noise: The metadata associated with the content doesn’t have a well-defined ontology. To address all these challenges, YouTube employs a two-stage deep learning-based recommendation strategy that trains large-scale models (with approximately one billion parameters) on hundreds of billions of examples. RecSys’16 ). RecSys’16 ).

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PyImageSearch

MARCH 13, 2023

dropout ratio) and other relevant metadata (e.g., Course information: 69 total classes • 73 hours of on-demand code walkthrough videos • Last updated: February 2023 ★★★★★ 4.84 (128 Ratings) • 15,800+ Students Enrolled I strongly believe that if you had the right teacher you could master computer vision and deep learning.

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Computer Vision Deep Learning Neural Network Metadata 102

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. LSTMs and other recurrent neural networks RNNs are probably the most commonly used deep learning models for NLP and with good reason.

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Machine Learning Neural Network Convolutional Neural Networks Deep Learning 52

PyImageSearch

AUGUST 7, 2023

Thus, LinkedIn leverages neural networks that can handle complex queries and capture the non-linear relationship between the query and the candidates. A member network embeds a given member to a fixed dimensional latent space. A cross-network that takes a query and member embeddings and computes their relevance score.

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Computer Vision Neural Network Deep Learning Algorithm 78

Unite.AI

JANUARY 23, 2024

LLMs are a class of deep learning models that are pretrained on massive text corpora, allowing them to generate human-like text and understand natural language at an unprecedented level. Word2Vec pioneered the use of shallow neural networks to learn embeddings by predicting neighboring words.

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Large Language Models Prompt Engineer Prompt Engineering LLM 100

ODSC - Open Data Science

FEBRUARY 25, 2025

Deep Learning & Multi-Modal Models TrackPush Neural NetworksFurther Dive into the latest advancements in neural networks, multimodal learning, and self-supervised models. This track provides practical guidance on building and optimizing deep learningsystems.

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Viso.ai

NOVEMBER 19, 2023

This includes various products related to different aspects of AI, including but not limited to tools and platforms for deep learning, computer vision, natural language processing, machine learning, cloud computing, and edge AI. The platform is easy to learn and use, even if you aren’t a specialized professional.

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Computer Vision Machine Learning Natural Language Processing Deep Learning 97

The MLOps Blog

APRIL 5, 2023

What is model packaging in machine learning? What is model packaging in machine learning? Source Model packaging is a process that involves packaging model artifacts, dependencies, configuration files, and metadata into a single format for effortless distribution, installation, and reuse.

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ML Machine Learning Deep Learning Metadata 69

PyImageSearch

DECEMBER 26, 2022

Table of Contents Automatic Differentiation Part 2: Implementation Using Micrograd Introduction What Is a Neural Network? Automatic Differentiation Part 2: Implementation Using Micrograd Introduction What Is a Neural Network? Having Problems Configuring Your Development Environment? b = Value(data=3.0) a => Value(data=6.0,

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Neural Network Computer Vision Deep Learning Python 52

AWS Machine Learning Blog

MAY 31, 2023

PyTorch supports dynamic computational graphs, enabling network behavior to be changed at runtime. This provides a major flexibility advantage over the majority of ML frameworks, which require neural networks to be defined as static objects before runtime. Triton uses TorchScript for improved performance and flexibility.

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PyImageSearch

OCTOBER 2, 2023

Jump Right To The Downloads Section A Deep Dive into Variational Autoencoder with PyTorch Introduction Deep learning has achieved remarkable success in supervised tasks, especially in image recognition. Before the rise of GANs, there were other foundational neural network architectures for generative modeling.

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The MLOps Blog

JANUARY 26, 2024

A feature store typically comprises a feature repository, a feature serving layer, and a metadata store. The metadata store manages the metadata associated with each feature, such as its origin and transformations. Some ML systems use deep learning, while others utilize more classical models like decision trees or XGBoost.

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Machine Learning Metadata ML Python 72

Marek Rei

NOVEMBER 4, 2024

Mind the gap: Challenges of deep learning approaches to Theory of Mind Jaan Aru, Aqeel Labash, Oriol Corcoll, Raul Vicente. link] An opinion paper on deep learning models in connection to the Theory of Mind – the skill of humans to understand the minds of others, imagine that they might have hidden knowledge or emotions.

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AWS Machine Learning Blog

MARCH 6, 2023

Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical. Regarding the scope of this post, note the following: We don’t cover neural network scientific design and associated optimizations.

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The MLOps Blog

NOVEMBER 30, 2022

These days enterprises are sitting on a pool of data and increasingly employing machine learning and deep learning algorithms to forecast sales, predict customer churn and fraud detection, etc., Most of its products use machine learning or deep learning models for some or all of their features.

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ML Metadata Machine Learning Software Development 52

Heartbeat

OCTOBER 19, 2023

These models are often neural networks, mainly variants of the Transformer architecture, which have proven highly effective for natural language understanding and generation tasks. At their core, LLMs employ deep learning techniques to understand and generate text. How Do LLMs Work?

Large Language Models 52

Large Language Models Metadata LLM Deep Learning 52

Bugra Akyildiz

MARCH 5, 2023

Google wrote about they built Knowledge Transfer Network(KTN) for Heteregenous Graph Neural Networks(HGNN)s. Main problem it solves: Industrial applications of deep learning is label scarcity, and with their diverse node types, HGNNs are even more likely to face this challenge.

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Neural Network Metadata Large Language Models Deep Learning 52

Mlearning.ai

FEBRUARY 21, 2023

A Machine Learning framework is a tool that enables a developer to create models to achieve a given task such as image classification or natural language processing. Some of the most familiar examples of ML frameworks are PyTorch and TensorFlow. What is ONNX? The Promise of ONNX Fulfilment of the Promise Why was ONNX created?

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