PyImageSearch

FEBRUARY 6, 2023

Jump Right To The Downloads Section Training a Custom Image Classification Network for OAK-D Before we start data loading, analysis, and training the classification network on the data, we must carefully pick the suitable classification architecture as it would finally be deployed on the OAK. tomato, brinjal, and bottle gourd).

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PyImageSearch

FEBRUARY 20, 2023

We’ll keep the language simple and avoid using jargon, but if you need help understanding anything, please let us know , and we’ll do our best to help. Table of Contents Learning JAX in 2023: Part 1 — The Ultimate Guide to Accelerating Numerical Computation and Machine Learning ?? What Is JAX? autograd XLA ? ⬇️ Import JAX ?

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PyImageSearch

MARCH 20, 2023

Furthermore, we will discuss how we can use our model to make predictions in real-time. In the previous tutorial of this series, we tried to understand the formulation of triplet loss. We discussed how it could be used to learn an embedding space where “similar faces” (i.e., from different people) reside farther apart.

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PyImageSearch

APRIL 24, 2023

Additionally, TorchDynamo is designed to mix Python execution with compiled backends to get the best of both worlds: usability and performance. introduces the following new technologies: TorchDynamo TorchInductor AOT Autograd PrimTorch These technologies make the PyTorch 2.0 code run faster (with less memory) by JIT-compiling the PyTorch 2.0

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PyImageSearch

JANUARY 9, 2023

We will discuss the different types of facial recognition approaches and take an in-depth dive into the conceptual details of siamese networks, which make them an integral part of robust facial recognition applications. We will start by loading the Labeled faces in the wild data and prepare for our face recognition application.

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Flipboard

MARCH 6, 2023

Project Structure Implementing Siamese Model and Triplet Loss Summary Credits Citation Information Triplet Loss with Keras and TensorFlow In today’s tutorial, we will try to understand the formulation of the triplet loss and build our Siamese Network Model in Keras and TensorFlow, which will be used to develop our Face Recognition application.

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PyImageSearch

JANUARY 2, 2023

It is estimated that over $140 billion is required annually to meet the health-related sustainable development goal objectives. Further, significant health technology, digital technology, and artificial intelligence (AI) investments are needed to bridge the health service gap in emerging markets.

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Flipboard

FEBRUARY 13, 2023

Table of Contents Building a Dataset for Triplet Loss with Keras and TensorFlow Labeled Faces in the Wild Dataset Configuring Your Development Environment Having Problems Configuring Your Development Environment? Specifically, we will build a dataset for training our Siamese network-based recognition model. identification and verification).

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PyImageSearch

NOVEMBER 20, 2023

Picking up where we left off, let us revise what we learned in Part 1. Tools and Methodologies for Studying Causal Effects Welcome back to Part 3 of the 5-part series on Causality in Machine Learning. The previous one was all about introducing Causality in the briefest way possible. You can find it right here. What do I mean by that?

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PyImageSearch

APRIL 3, 2023

However, optimizing and deploying those best models onto some edge device allows you to put your deep learning models to actual use in an industry where deployment on edge devices is mandatory and can be a cost-effective solution. The converted blob file would then run image classification inference on the OAK-D using the DepthAI API.

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O'Reilly Media

FEBRUARY 13, 2024

Second, the examples in a case like this are always cherry-picked. Since The New York Times sued OpenAI for infringing its copyrights by using Times content for training, everyone involved with AI has been wondering about the consequences. How will this lawsuit play out? There are two components to this suit. Here’s mine.

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Flipboard

MARCH 27, 2023

It has provided some of the best abstractions for distributed training, data loading, and automatic differentiation. torch.compile torch.compile Definition Accelerating DNNs with PyTorch 2.0 torch.compile Over the last few years, PyTorch has evolved as a popular and widely used framework for training deep neural networks (DNNs).

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Marktechpost

JULY 11, 2023

The add-on allows picking every cell that matches a specific tag, enabling the execution of any operation on those cells. As a result, our laptop often freezes and stops functioning because of memory issues. Any component of JupyterLab can be enhanced or customized using JupyterLab extensions.

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PyImageSearch

JULY 3, 2023

In this blog post, we will dive deeper into the Netflix movies and series recommendation systems ( Figure 1 ). The Internet has revolutionized how we consume television through Over-the-Top (OTT) content streaming platforms like Netflix, Amazon Prime, Disney, HBO, etc. Therefore, everything you see on Netflix is a recommendation.

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ODSC - Open Data Science

OCTOBER 18, 2023

They may use social monitoring tools to learn about the most desirable laptops or determine how much parents have budgeted for school supplies. What are the chances that someone who typically orders from a grocery website once per week would rather pick up their goods on the way home from work rather than wait for delivery?

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PyImageSearch

OCTOBER 2, 2023

Why Does the Encoder of a VAE Follow a Gaussian Distribution? Objective Functions of VAE Reparameterization Trick Configuring Your Development Environment Need Help Configuring Your Development Environment? Using the renowned Fashion-MNIST dataset, we’ll guide you through understanding its nuances. Let’s get started!

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PyImageSearch

OCTOBER 23, 2023

Home Table of Contents Generating Faces Using Variational Autoencoders with PyTorch Configuring Your Development Environment Need Help Configuring Your Development Environment? Our journey begins with training a VAE on this dataset, setting the stage for a series of captivating experiments. Let’s dive in!

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PyImageSearch

JUNE 12, 2023

Home Table of Contents DETR Breakdown Part 2: Methodologies and Algorithms The DETR Model ?️ Object Detection Set Prediction Loss ? Optimal Bipartite Matching ? Optimal Bipartite Matching for Objects ? Optimize Object Specific Losses ? Quiz Time! ? Looking for the source code to this post? The DETR Model ?️ Therefore, Carion et al.

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Snorkel AI

FEBRUARY 6, 2023

  Anirudh Koul is Machine Learning Lead for the NASA Frontier Development Lab and the Head of Machine Learning Sciences at Pinterest. He presented at Snorkel AI’s 2022 Future of Data Centric AI (FDCAI) Conference. A transcript of Koul’s talk is provided below. It has been lightly edited for reading clarity. And it’s somewhere in there.

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Snorkel AI

FEBRUARY 6, 2023

Anirudh Koul is Machine Learning Lead for the NASA Frontier Development Lab and the Head of Machine Learning Sciences at Pinterest. He presented at Snorkel AI’s 2022 Future of Data Centric AI (FDCAI) Conference. A transcript of Koul’s talk is provided below. It has been lightly edited for reading clarity. And it’s somewhere in there.

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PyImageSearch

JANUARY 15, 2024

Home Table of Contents Adversarial Learning with Keras and TensorFlow (Part 2): Implementing the Neural Structured Learning (NSL) Framework and Building a Data Pipeline Adversarial Learning with NSL CIFAR-10 Dataset Configuring Your Development Environment Need Help Configuring Your Development Environment? Looking for the source code to this post?

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Heartbeat

APRIL 18, 2023

In this article, we will be talking about human action recognition. We will make use of the VGG model or VGGNet , and we will log our model, artifacts, and metrics to Comet. This is going to be a hands-on tutorial, so I urge you to read and code along, and I will add the link to the code at the end of the article. Zisserman and K.

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PyImageSearch

JANUARY 16, 2023

Setting Up Our Project Comparing XGboost and Gradient Boost Results Summary Citation Information Scaling Kaggle Competitions Using XGBoost: Part 3 We continue our journey into understanding XGBoost, but there is one penultimate stop we need to make before deep diving into the nitty gritty of Extreme Gradient Boosting. Each sample is given a weight.

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The MLOps Blog

JANUARY 5, 2023

This article was originally an episode of the MLOps Live , an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Kuba Cieślik, founder and AI Engineer at tuul.ai , about building visual search engines. Good job there.

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PyImageSearch

JANUARY 23, 2023

Table of Contents Scaling Kaggle Competitions Using XGBoost: Part 4 What Is XGBoost? Our Dummy Dataset Breaking Down the Math Configuring Your Development Environment Having Problems Configuring Your Development Environment? If you have stuck with us through the entire journey till now (Great work!), Looking for the source code to this post?

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PyImageSearch

SEPTEMBER 18, 2023

This lesson is the last of a 2-part series on Segment Anything Model (SAM) from Meta AI : SAM from Meta AI (Part 1): Segmentation with Prompts SAM from Meta AI (Part 2): Integration with CLIP for Downstream Tasks (this tutorial) To learn how to integrate SAM with CLIP for downstream tasks, just keep reading. Looking for the source code to this post?

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PyImageSearch

JANUARY 8, 2024

Project Structure Creating Adversarial Examples Robustness Toward Adversarial Examples Summary Citation Information Adversarial Learning with Keras and TensorFlow (Part 1): Overview of Adversarial Learning In this tutorial, you will learn about adversarial examples and how they affect the reliability of neural network-based computer vision systems.

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Mlearning.ai

JUNE 5, 2023

Highly useful for deployments) Linux can run on various types of computers, from desktops and laptops to servers and embedded systems. Whatever your goal is, there is a tool for you. In this blog, I will introduce you to some of the most popular and powerful computer vision tools that you can use to unleash your creativity and have fun.

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PyImageSearch

DECEMBER 19, 2022

This is the 2nd lesson in our 4-part series on OAK-101 : Introduction to OpenCV AI Kit (OAK) OAK-D: Understanding and Running Neural Network Inference with DepthAI API (today’s tutorial) OAK 101: Part 3 OAK 101: Part 4 To learn how DepthAI API works and run neural network inference on OAK-D, just keep reading.

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Snorkel AI

APRIL 11, 2023

The goal of active learning is to select the best examples to improve model quality. Cody Coleman, CEO and co-founder of Coactive AI gave a presentation entitled “Data Selection for Data-Centric AI: Quality over Quantity” at Snorkel AI’s Future of Data-Centric AI Event in August 2022. I’m super excited to chat with you all today.

Deep Learning 52

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Snorkel AI

APRIL 11, 2023

The goal of active learning is to select the best examples to improve model quality. Cody Coleman, CEO and co-founder of Coactive AI gave a presentation entitled “Data Selection for Data-Centric AI: Quality over Quantity” at Snorkel AI’s Future of Data-Centric AI Event in August 2022. I’m super excited to chat with you all today.

Deep Learning 52

Deep Learning Algorithm BERT ML 52

Snorkel AI

APRIL 11, 2023

The goal of active learning is to select the best examples to improve model quality. Cody Coleman, CEO and co-founder of Coactive AI gave a presentation entitled “Data Selection for Data-Centric AI: Quality over Quantity” at Snorkel AI’s Future of Data-Centric AI Event in August 2022. I’m super excited to chat with you all today.

Deep Learning 52

Deep Learning Algorithm BERT ML 52

PyImageSearch

NOVEMBER 6, 2023

Home Table of Contents Image Segmentation with U-Net in PyTorch: The Grand Finale of the Autoencoder Series Introduction U-Net Framework Configuring Your Development Environment Need Help Configuring Your Development Environment? Let’s embark on this grand finale together! Let’s embark on this grand finale together!

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PyImageSearch

DECEMBER 12, 2022

Table of Contents Scaling Kaggle Competitions Using XGBoost: Part 2 AdaBoost The Dataset Sample Weights Choosing the Right Feature Significance of a Stump Calculating the New Sample Weights Moving Forward: The Subsequent Stumps Piecing It Together Configuring Your Development Environment Having Problems Configuring Your Development Environment?

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PyImageSearch

MAY 29, 2023

Project Structure Creating Our Configuration File Preprocessing Our Dataset Implementing the CycleGAN Architecture Summary Citation Information CycleGAN: Unpaired Image-to-Image Translation (Part 2) In this tutorial, we will implement our CycleGAN model for unpaired image-to-image translation tasks using TensorFlow and Keras.

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PyImageSearch

JULY 17, 2023

Home Table of Contents Implementing a Convolutional Autoencoder with PyTorch Configuring Your Development Environment Need Help Configuring Your Development Environment? We will then explore different testing situations (e.g., If we got a nickel every time a dataset disappeared from the web, we’d have enough to buy a Tesla.

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Flipboard

APRIL 17, 2023

And best of all, these Jupyter Notebooks will run on Windows, macOS, and Linux! ? Training with PyTrees Train the MLP Summary Citation Information Learning JAX in 2023: Part 3 — A Step-by-Step Guide to Training Your First Machine Learning Model with JAX In this tutorial, you will learn how to train your first machine learning model with JAX.

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