Pickl AI

JANUARY 12, 2023

Due to the growing application of Data Science in different industries, companies are now looking forward to hiring individuals and training their employees on newer technologies that can eventually help the organization attain its goals. Best Data Science courses for working professionals 1.

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Applied Data Science

OCTOBER 25, 2021

How I learned to stop worrying and love the field This blog covers all the core themes to starting your career in data science: ? Based on current predictions (enabled by data science), this trend will continue, as more and more industries shift towards data-driven and automated solutions.

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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.

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How to Learn Machine Learning

MAY 14, 2023

Our article will guide you through the best free online courses to help you achieve your goals as a machine learning engineer. Python is widely regarded as the most popular programming language for machine learning due to its readability and extensive library support.

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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? Figure 1: The Default Python vs. TorchDynamo behavior (source: PyTorch 2.0 ). just keep reading.

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ODSC - Open Data Science

FEBRUARY 17, 2023

What to Expect in 2023: A Data Scientist’s Top 5 AI Predictions Between improved NLP and increased use of AI in finance, here are one data scientist’s 2023 AI predictions. Master the fundamentals before ODSC East 2023 with the pre-conference virtual live training included in a Mini-Bootcamp Pass.

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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

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 20, 2023

Many people have asked us to create a course about JAX, so we decided to take on the challenge. Once you complete this course, you’ll be able to understand and work with any code written in JAX/FLAX. Table of Contents Learning JAX in 2023: Part 1 — The Ultimate Guide to Accelerating Numerical Computation and Machine Learning ??

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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.

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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?

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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?

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PyImageSearch

FEBRUARY 19, 2024

vs. Gemini Pro for Image Classification Summary and Key Takeaways Citation Information Image Classification with Gemini Pro In this tutorial, you’ll learn how to use the Gemini Pro generative model with the Google AI Python SDK (software development kit) to generate code for image classification in PyTorch. vs. ChatGPT-3.5

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Flipboard

MARCH 27, 2023

The success of PyTorch is attributed to its simplicity, first-class Python integration, and imperative style of programming. It has provided some of the best abstractions for distributed training, data loading, and automatic differentiation. is available as a Python pip package. is available as a Python pip package.

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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

FEBRUARY 5, 2024

Home Table of Contents Evaluating Siamese Network Accuracy (F1-Score, Precision, and Recall) with Keras and TensorFlow Building the Face Recognition Application with Siamese Networks Introduction to Model Evaluation in Face Recognition Introduction to Siamese Networks in Facial Recognition Systems Utilizing Siamese Networks for Face Verification Overview (..)

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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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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. Tools and Methodologies for Studying Causal Effects Welcome back to Part 3 of the 5-part series on Causality in Machine Learning. How do we use them with our data, and why do we need to care?

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PyImageSearch

FEBRUARY 12, 2024

Introduction to Gemini Pro Vision Image Processing with Gemini Pro (this tutorial) Lesson 3 Lesson 4 Lesson 5 Lesson 6 To learn how to use Gemini Pro for generating various image processing techniques and to understand its comparative performance against ChatGPT-3.5, offering insights into the strengths and nuances of each. just keep reading.

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PyImageSearch

JANUARY 22, 2024

Fundamentally, Diffusion Models work by destroying training data through the successive addition of Gaussian noise and then learning to recover the data by reversing this noising process. After training, we can use the Diffusion Model to generate data by simply passing randomly sampled noise through the learned denoising process.

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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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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 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.

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PyImageSearch

DECEMBER 19, 2022

We believe this would be a great way to get your hands dirty with the DepthAI Python API and practically understand through code what happens underneath the hood of an OAK device. Looking for the source code to this post?

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PyImageSearch

SEPTEMBER 18, 2023

Home Table of Contents SAM from Meta AI (Part 2): Integration with CLIP for Downstream Tasks SAM and CLIP Integration Configuring Your Development Environment Need Help Configuring Your Development Environment? Looking for the source code to this post? zero-shot image classification, text-to-image retrieval, and image similarity).

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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

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

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

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.

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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

DECEMBER 12, 2022

The features of this dataset are mainly “courses” and “Project,” while the labels in the “Job” column tell us whether the person has a job right now or not. We went through the core essentials required to understand XGBoost, namely decision trees and ensemble learners. But the process is a step-by-step one. Table 1: The Dataset.

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PyImageSearch

NOVEMBER 13, 2023

For example, image classification, image search engines (also known as content-based image retrieval, or CBIR), simultaneous localization and mapping (SLAM), and image segmentation, to name a few, have all been changed since the latest resurgence in neural networks and deep learning. This leads to less accurate localizations.

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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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PyImageSearch

MARCH 13, 2023

Feel free to jump over to the notebook or create a new notebook and code along! Bonus Hugging Face has multiple Python libraries under its umbrella: datasets , transformers , evaluate , and accelerate , just to name a few! To familiarize yourself with these libraries, feel free to jump into their documentation. We will use the

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PyImageSearch

JANUARY 29, 2024

Home Table of Contents Adversarial Learning with Keras and TensorFlow (Part 3): Exploring Adversarial Attacks Using Neural Structured Learning (NSL) Introduction to Advanced Adversarial Techniques in Machine Learning Harnessing NSL for Robust Model Training: Insights from Part 2 Deep Dive into Adversarial Attack Formulations: PGD and FGSM Explored (..)

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PyImageSearch

MAY 15, 2023

By the end of this tutorial, you will have a good understanding of the process involved in deploying an object detection model on the OAK-D platform , as well as the skills to recognize hand gestures using OAK-D ’s camera with the help of the DepthAI API in Python. We will take it a step further by deploying the model on the OAK-D device.

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DrivenData Labs

DECEMBER 10, 2023

It can be an arduous task, and it is a real skill to master the art of literature review, one that researchers hone throughout their careers. We are very interested in how AI-based research assistants can help NASA, and we received a diverse variety of cutting-edge AI approaches from around the globe in the Research Rovers challenge.

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