Data Science Dojo

JULY 27, 2023

16 Python projects you need to master for success Top Python projects to elevate your skills 1. In practical use, the School Management project can benefit educational institutions by offering a digital platform for organizing student data. Python is a versatile programming language known for its simplicity and readability.

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ODSC - Open Data Science

AUGUST 14, 2023

Given the countless free, approachable, and resource-friendly tools out there, there’s really no reason why someone who could benefit from AI shouldn’t be using it — including businesses seeking to turn a profit. Funny enough, you can use AI to explain AI. That’s not too bad.

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Smart Data Collective

NOVEMBER 18, 2020

The Data Scientist profession today is often considered to be one of the most promising and lucrative. The Bureau of Labor Statistics estimates that the number of data scientists will increase from 32,700 to 37,700 between 2019 and 2029. What is Data Science? Definition: Data Mining vs Data Science.

Data Mining 143

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Pickl AI

MAY 31, 2023

With the expanding field of Data Science, the need for efficient and skilled professionals is increasing. Its efficacy may allow kids from a young age to learn Python and explore the field of Data Science. Its efficacy may allow kids from a young age to learn Python and explore the field of Data Science.

Data Science 52

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PyImageSearch

MARCH 20, 2023

Jump Right To The Downloads Section Training and Making Predictions with Siamese Networks and Triplet Loss In the second part of this series, we developed the modules required to build the data pipeline for our face recognition application. Furthermore, we will discuss how we can use our model to make predictions in real-time.

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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

APRIL 24, 2023

Project Structure Accelerating DNNs Using TorchDynamo TorchDynamo vs. TorchScript vs. FX Tracing Data-Dependent Control Flow Non-PyTorch Libraries TorchDynamo FX Graphs Summary Citation Information What’s Behind PyTorch 2.0? data-dependent flows or non-PyTorch libraries). torch.compile What’s Behind PyTorch 2.0?

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Deep Learning Deep Learning Python Computer Science 103

Heartbeat

DECEMBER 22, 2023

They can retrieve and integrate real-time data from external systems through APIs, enabling them to provide up-to-date and accurate responses. Real-time Information: If your application requires access to real-time information or external systems through APIs, a conversational agent can facilitate the retrieval and integration of such data.

Data Science 52

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PyImageSearch

JANUARY 9, 2023

We will start by loading the Labeled faces in the wild data and prepare for our face recognition application. Jump Right To The Downloads Section Face Recognition with Siamese Networks, Keras, and TensorFlow Deep learning models tend to develop a bias toward the data distribution on which they have been trained.

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PyImageSearch

FEBRUARY 20, 2023

Table of Contents Learning JAX in 2023: Part 1 — The Ultimate Guide to Accelerating Numerical Computation and Machine Learning ?? Introduction Configuring Your Development Environment Having Problems Configuring Your Development Environment? ? What Is JAX? autograd XLA ? What Is JAX (revisited)? ⬇️ Import JAX ?

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Flipboard

MARCH 6, 2023

In the previous tutorial of this series, we built the dataset and data pipeline for our Siamese Network based Face Recognition application. Specifically, we looked at an overview of triplet loss and discussed what kind of data samples are required to train our model with the triplet loss. Looking for the source code to this post?

Deep Learning 97

Deep Learning Deep Learning Data Pipeline Computer Science 97

Flipboard

FEBRUARY 13, 2023

Project Structure Creating Our Configuration File Creating Our Data Pipeline Preprocessing Faces: Detection and Cropping Summary Citation Information Building a Dataset for Triplet Loss with Keras and TensorFlow In today’s tutorial, we will take the first step toward building our real-time face recognition application.

Data Pipeline 106

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PyImageSearch

APRIL 3, 2023

Jump Right To The Downloads Section Deploying a Custom Image Classifier on an OAK-D Introduction As a deep learning engineer or practitioner, you may be working in a team building a product that requires you to train deep learning models on a specific data modality (e.g., Looking for the source code to this post?

Deep Learning 64

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PyImageSearch

NOVEMBER 20, 2023

Tools and Methodologies for Studying Causal Effects Welcome back to Part 3 of the 5-part series on Causality in Machine Learning. Ignoring hidden causes in data can mean death for your model Counterfactual thinking is a must Randomization, Natural Experiments, and Conditioning are the tools of the trade Right. What do I mean by that?

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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?

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PyImageSearch

OCTOBER 2, 2023

While they can reconstruct input data effectively, they falter when generating new samples from the latent space unless specific points are manually chosen. Objective Functions of VAE Reparameterization Trick Configuring Your Development Environment Need Help Configuring Your Development Environment? Let’s get started!

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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 Over the last few years, PyTorch has evolved as a popular and widely used framework for training deep neural networks (DNNs). With continuous innovation from the PyTorch team, PyTorch has moved from version 1.0

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Hacker News

FEBRUARY 2, 2023

There were several shelves of physics books at the local bookstore. But what I coveted most was the largest physics book collection there: a series of five plushly illustrated college textbooks. But what I coveted most was the largest physics book collection there: a series of five plushly illustrated college textbooks.

Machine Learning 149

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AWS Machine Learning Blog

SEPTEMBER 14, 2023

Unstructured data accounts for 80% of all the data found within organizations, consisting of repositories of manuals, PDFs, FAQs, emails, and other documents that grows daily. Businesses today rely on continuously growing repositories of internal information, and problems arise when the amount of unstructured data becomes unmanageable.

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PyImageSearch

OCTOBER 23, 2023

We’ll explore tasks ranging from reconstructing the validation set to sampling from the standard normal distribution, probing the first 50 latent dimensions, and even enhancing visual attributes through the magic of latent space arithmetic. Let’s dive in! Looking for the source code to this post? All that said, are you: Short on time?

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PyImageSearch

NOVEMBER 6, 2023

Now, as we approach the culmination, we’ll cover essential steps such as data preprocessing, model initialization, and iterative training. In this concluding tutorial, we’ll delve deep into the captivating world of image segmentation, harnessing the power of the U-Net architecture. Let’s embark on this grand finale together!

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PyImageSearch

JUNE 19, 2023

These engines utilize user data (e.g., Each service uses unique techniques and algorithms to analyze user data and provide recommendations that keep us returning for more. By the end of the lesson, readers will have a solid grasp of the underlying principles that enable these applications to make suggestions based on data analysis.

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The MLOps Blog

OCTOBER 3, 2023

Mikiko is a very well-known figure in the data community. So I tell people honestly, I’ve spent the last eight years working up and down the data and ML value chain effectively – a fancy way of saying “job hopping.” How to transition from data analytics to MLOps engineering Piotr: Miki, you’ve been a data scientist, right?

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PyImageSearch

JANUARY 23, 2023

Our Dummy Dataset Breaking Down the Math Configuring Your Development Environment Having Problems Configuring Your Development Environment? The goal is to nullify the abstraction created by packages as much as possible. Our final stop of this series will have a similar outlook. Looking for the source code to this post?

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PyImageSearch

JANUARY 8, 2024

We will understand the dataset and the data pipeline for our application and discuss the salient features of the NSL framework in detail. We will discuss the relationship between the robustness and reliability of deep learning models and understand how engineered noise samples, when added to input images, can change model predictions.

Deep Learning 64

Deep Learning Deep Learning Data Pipeline Computer Science 64

AWS Machine Learning Blog

NOVEMBER 15, 2023

Llama 2 stands at the forefront of AI innovation, embodying an advanced auto-regressive language model developed on a sophisticated transformer foundation. It’s tailored to address a multitude of applications in both the commercial and research domains with English as the primary linguistic concentration.

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Heartbeat

OCTOBER 26, 2023

Try this free LLMOps course from industry-expert Elvis Saravia of DAIR.AI! ?️ Imagine harnessing the power of multiple state-of-the-art language models through one unified interface. Imagine harnessing the power of multiple state-of-the-art language models through one unified interface. The beauty of LangChain is its inherent adaptability.

Machine Learning 52

Machine Learning Machine Learning Computer Science Computer Science 52

PyImageSearch

JUNE 5, 2023

We also discussed the formulation and principles that allow it to perform image-to-image translation from unpaired data. Specifically, we will develop our data pipeline, implement the loss functions discussed in Part 1 and write our own code to train the CycleGAN model end-to-end using Keras and TensorFlow.

Deep Learning 56

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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. Table 1: AdaBoost Table.

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PyImageSearch

DECEMBER 26, 2022

This lesson is the last of a 2-part series on Autodiff 101 — Understanding Automatic Differentiation from Scratch : Automatic Differentiation Part 1: Understanding the Math Automatic Differentiation Part 2: Implementation Using Micrograd (today’s tutorial) To learn how to implement automatic differentiation using Python, just keep reading.

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PyImageSearch

JULY 17, 2023

visualizing the latent space, uniform sampling of data points from this latent space, and recreating images using these sampled points). It keeps our datasets safe, available, and hassle-free. It’s free, easy to create, and won’t demand your firstborn in return. We will then explore different testing situations (e.g.,

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PyImageSearch

SEPTEMBER 18, 2023

SAM and CLIP Integration As we briefly discussed in the previous tutorial, the CLIP model is a recent foundational model for computer vision that utilizes large-scale web-based image-text data to train strong representations that encode semantic knowledge. Looking for the source code to this post?

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PyImageSearch

MAY 29, 2023

Furthermore, we look closer at the Apples2Oranges Dataset and discuss dataset preprocessing techniques, allowing us to process our input data and build our end-to-end image translation model. Looking for the source code to this post? It was used to show the unpaired image-to-image translation performance and capabilities of CycleGAN.

Deep Learning 56

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PyImageSearch

DECEMBER 19, 2022

Table of Contents OAK-D: Understanding and Running Neural Network Inference with DepthAI API Introduction Configuring Your Development Environment Having Problems Configuring Your Development Environment? Looking for the source code to this post?

Deep Learning 52

Deep Learning Deep Learning Python AI 52

Heartbeat

NOVEMBER 9, 2023

Enter the world of Document Chains in LangChain, a revolutionary approach that promises to redefine how we interact with expansive textual data. Whether you’re a developer, data scientist, or just a curious enthusiast, this guide will walk you through the intricacies of Document Chains, showcasing their potential and practical applications.

Deep Learning 52

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PyImageSearch

MARCH 13, 2023

Feel free to jump over to the notebook or create a new notebook and code along! To familiarize yourself with these libraries, feel free to jump into their documentation. Table of Contents Train a MaskFormer Segmentation Model with Hugging Face ? Transformers Ecosystem How to Train an Instance Segmentation Model with MaskFormer?

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PyImageSearch

SEPTEMBER 11, 2023

This affects the practical usability of these models since it is not always possible or feasible to have access to or collect large amounts of data for every task at hand. In computer vision, the recent foundational models mainly rely on utilizing large scale web-data and aligning image and text pairs to train strong representation models.

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