Heartbeat

JULY 25, 2023

Introduction When it comes to practicing deep learning at home vs. industry, there’s a huge disconnect. If so, how much effort does it take to go from that system to a deep learning framework-ready system? To answer that question, I’m going to mix some technical jargon with some actual examples that explains the jargon.

Deep Learning 52

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Mlearning.ai

JANUARY 26, 2023

When it comes to implementing any ML model, the most difficult question asked is how do you explain it. Suppose, you are a data scientist working closely with stakeholders or customers, even explaining the model performance and feature selection of a Deep learning model is quite a task. How do we deal with this?

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Marktechpost

APRIL 5, 2024

In their paper, the researchers aim to propose a theory that explains how transformers work, providing a definite perspective on the difference between traditional feedforward neural networks and transformers. Despite their widespread usage, the theoretical foundations of transformers have yet to be fully explored.

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Neural Network Natural Language Processing Categorization Explainability 128

Unite.AI

DECEMBER 1, 2023

Exploring the Techniques of LIME and SHAP Interpretability in machine learning (ML) and deep learning (DL) models helps us see into opaque inner workings of these advanced models. However, the need for a distinct explainer for each model is a major limitation.

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LLM Machine Learning Explainability Algorithm 264

Flipboard

JUNE 20, 2023

In this guide , we explain the key terms in the field and why they matter. All of the definitions were written by a human. Machine learning Machine learning is when computers use experience to improve their performance. Dezeen's new editorial series, AItopia , is all about artificial intelligence.

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Heartbeat

JULY 27, 2023

Tensorflow’s Feature proto definition. Tensorflow’s “Features” proto definition Because our raw data is contained as either BytesList, FloatList, or Int64List and wrapped in a “oneof” Feature proto, that simplifies the map (and thus justifies the design choice). I’m not going to explain it. It’s a key-value data structure.

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Marktechpost

JULY 7, 2024

The specific architecture selected along with the dataset and learning algorithm used, is known to influence the neural patterns learned. Currently, a major challenge faced in the theory of deep learning is the issue of scalability.

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Flipboard

NOVEMBER 27, 2023

To understand how this plays out, we will first have to look at some of the definitions introduced by Pearl. Definitions Definition 1 The probability distribution of the outcome after the intervention is given by the equation: where the distribution of the outcome is defined as the probability assigned by the model to each outcome level.

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Becoming Human

JANUARY 23, 2023

- a beginner question Let’s start with the basic thing if I talk about the formal definition of Data Science so it’s like “Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis” , is the definition enough explanation of data science?

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Marktechpost

MAY 8, 2024

Among the main advancements in AI, seven areas stand out for their potential to revolutionize different sectors: neuromorphic computing, quantum computing for AI, Explainable AI (XAI), AI-augmented design and Creativity, Autonomous Vehicles and Robotics, AI in Cybersecurity and AI for Environmental Sustainability.

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AssemblyAI

NOVEMBER 9, 2023

The models are powered by advanced Deep Learning and Machine Learning research. In Natural Language Processing, or NLP, Text Summarization refers to the process of using Deep Learning and Machine Learning models to synthesize large bodies of texts into their most important parts.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

Essential ML capabilities such as hyperparameter tuning and model explainability were lacking on premises. In both cases, the evaluation and explainability report, if generated, are recorded in the model registry. Explain – SageMaker Clarify generates an explainability report. AWS_ACCOUNT] region = eu-central-1.

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Marktechpost

OCTOBER 18, 2023

The recent results of machine learning in drug discovery have been largely attributed to graph and geometric deep learning models. Like other deep learning techniques, they need a lot of training data to provide excellent modeling accuracy.

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Marktechpost

JULY 10, 2023

The traditional definition of creativity as a product-centered view may not fully capture the human creativity involved in text-to-image generation. The standard product-based definition of creativity may not fully capture the unique factors contributing to creative expression. Check out the Paper.

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Prompt Engineering Prompt Engineer Explainability Deep Learning 52

Flipboard

JULY 10, 2023

Figure 3 illustrates the visualization of the latent space and the process we discussed in the story, which aligns with the technical definition of the encoder and decoder. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? That’s not the case.

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Flipboard

MARCH 27, 2023

torch.compile torch.compile Definition Accelerating DNNs with PyTorch 2.0 In this series, you will learn about Accelerating Deep Learning Models with PyTorch 2.0. This lesson is the 1st of a 2-part series on Accelerating Deep Learning Models with PyTorch 2.0 : What’s New in PyTorch 2.0?

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Heartbeat

MARCH 14, 2023

Some of the methods used for scene interpretation include Convolutional Neural Networks (CNNs) , a deep learning-based methodology, and more conventional computer vision-based techniques like SIFT and SURF. photo from [link] As new algorithms are added to robotics, computer vision will definitely get better.

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Heartbeat

MARCH 13, 2024

Using Comet to monitor pre-trained models Photo by Daniel Korpai on Unsplash Transfer learning involves using a pre-trained model to solve a deep-learning problem. Comet extensively integrates with multiple libraries, including the most common ones for deep learning, such as TensorFlow, Keras, and Pytorch.

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Heartbeat

JANUARY 31, 2024

By definition, it is the process of breaking down given text in natural language processing into the smallest unit in a sentence, called a token. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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AWS Machine Learning Blog

APRIL 12, 2023

They’ve built a deep-learning model ScarceGAN, which focuses on identification of extremely rare or scarce samples from multi-dimensional longitudinal telemetry data with small and weak labels. For each training job run, we generate four different models to Amazon S3 based on our custom business definition. cpu-py39-ubuntu20.04-sagemaker",

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Metadata Deep Learning ML Data Science 101

Mlearning.ai

JANUARY 17, 2024

al 600+: Key technological concepts of generative AI 300+: Deep Learning — the core of any generative AI model: Deep learning is a central concept of traditional AI that has been adopted and further developed in generative AI. Deep learning neural network. Image credit: Yang, Jingfeng et.

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PyImageSearch

NOVEMBER 13, 2023

Home Table of Contents Faster R-CNNs Object Detection and Deep Learning Measuring Object Detector Performance From Where Do the Ground-Truth Examples Come? One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al.

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Marktechpost

DECEMBER 6, 2023

It has definitely transformed our lives, streamlining processes and enhancing efficiency in ways we couldn’t have imagined before. Today, AI finds its application in almost every field imaginable.

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The Stanford AI Lab Blog

FEBRUARY 24, 2022

In the rest of this blogpost, we’ll share our theoretical analysis explaining why this is the case, showing that retraining in self-training provably improves accuracy compared to the original pseudo-labeler. Our theoretical analysis focuses on pseudo-label-based self-training, but there are also other variants. 2019 , Sohn et al.,

Deep Learning 40

Deep Learning Algorithm Explainability 40

Allen AI

OCTOBER 1, 2024

Background Parameter norm growth Classical machine learning (and perhaps conventional wisdom about deep learning) would suggest that, during training, parameters converge to a specific magnitude as they approach a local minimum. The two sparsity metrics, despite their different definitions, look remarkably similar!

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Flipboard

MARCH 6, 2023

In this tutorial, we will dive deeper into the definition of the triplet and discuss its mathematical formulation in detail. Here, “margin” is a hyperparameter similar to the one we discussed in the definition of pairwise contrastive loss in the first part of this series. In Deep Learning, we need to train Neural Networks.

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Bugra Akyildiz

SEPTEMBER 1, 2024

This refined definition aids in understanding how features develop and interact within the network, providing insights into the model's processing mechanisms. Google Deepmind wrote about Gemma scope on how it can use sparse autoencoders(SAE) to provide interpretability into the various deep learning models.

Neural Network 52

Neural Network Deep Learning Explainability Algorithm 52

Flipboard

FEBRUARY 13, 2023

Let us look at the definition of this call step by step. This function takes as input the model definition file (i.e., dnn.blobFromImage function, check out our blog post , which explains this function in detail. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

Computer Vision 145

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Ofemwire

MARCH 1, 2024

At its core, AI is designed to replicate or even surpass human cognitive functions, employing algorithms and machine learning to interpret complex data, make decisions, and execute tasks with unprecedented speed and accuracy. If you dont get that, let me explain what AI is, like I would do to a fifth grader.

Neural Network 52

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AWS Machine Learning Blog

OCTOBER 15, 2024

In this post, we discuss Bria’s family of models, explain the Amazon SageMaker platform, and walk through how to discover, deploy, and run inference on a Bria 2.3 HD – Optimized for high-definition, Bria 2.2 model using SageMaker JumpStart. Overview of Bria 2.3, HD, and Bria 2.3 Fast – Optimized for speed, Bria 2.3

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Viso.ai

APRIL 2, 2024

Throughout the article, you’ll gain the following insights: Definition and scope of 3D computer vision Fundamental concepts in 3D computer vision Passive and active techniques of 3D reconstruction in computer vision Deep learning approaches like 3D CNN, Point Cloud Processing, 3D Object Detection, etc. What’s Next?

Computer Vision 52

Computer Vision Deep Learning Robotics Convolutional Neural Networks 52

PyImageSearch

MARCH 20, 2023

In previous tutorials, we noted that the pyimagesearch folder contains the code for the dataset module ( dataset.py ), the model definition ( model.py ), and the configuration file ( config.py ), which we discussed in detail. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

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PyImageSearch

FEBRUARY 6, 2023

Furthermore, this tutorial aims to develop an image classification model that can learn to classify one of the 15 vegetables (e.g., If you are a regular PyImageSearch reader and have even basic knowledge of Deep Learning in Computer Vision, then this tutorial should be easy to understand. tomato, brinjal, and bottle gourd).

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Topbots

JANUARY 11, 2023

Now my resolve about no new school of epistemology is weakening and this new article explains why. Neural Networks and Deep Learning algorithms, combined with synthetic data, are near to the current frontier in artificial intelligence (AI). Professor Hannah Fry explains this point well.

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Heartbeat

FEBRUARY 9, 2023

If you are working as a developer and your work involves a lot of text, then you definitely need to know more about it. Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

Natural Language Processing 52

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Mlearning.ai

FEBRUARY 9, 2023

In industrial applications of Data Science, model complexity, model explainability, efficiency, and ease of deployment play a large role, even if that means you’re settling for a slightly less accurate model. In the industry, deep learning is not always the preferred approach.

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ODSC - Open Data Science

JANUARY 29, 2024

We don’t claim this is a definitive analysis but rather a rough guide due to several factors: Job descriptions show lagging indicators of in-demand prompt engineering skills, especially when viewed over the course of 9 months. The definition of a particular job role is constantly in flux and varies from employer to employer.

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