Dataconomy

JULY 24, 2023

We’ll dissect their role, delve into their day-to-day responsibilities, and explore the unique skill set that sets them apart in the tech universe. We’ll dissect their role, delve into their day-to-day responsibilities, and explore the unique skill set that sets them apart in the tech universe. Their mission?

Data Scientist 172

Data Scientist Data Analyst Data Science Machine Learning 172

PyImageSearch

MARCH 20, 2023

We discussed how it could be used to learn an embedding space where “similar faces” (i.e., We discussed how it could be used to learn an embedding space where “similar faces” (i.e., In this tutorial, we will take this further and learn how to train our face recognition model using Keras and TensorFlow.

Deep Learning 117

Deep Learning Deep Learning Data Pipeline Python 117

Pickl AI

NOVEMBER 8, 2023

Python , a versatile programming language, finds widespread real-world applications across multiple domains. Python’s data analysis and visualization libraries, such as Pandas and Matplotlib, empower Data Scientists and analysts to derive valuable insights. A Python developer gets ₹5,00000 per year in India.

Python 52

Python Data Analysis Data Analysis Internet of Things 52

Pickl AI

JANUARY 12, 2023

And so, Data Science courses for working professionals have become a prominent choice for working professionals to upgrade their knowledge base. Best Data Science courses for working professionals 1. The course is structured in a sequence, and upon completion of the entire courses, students get a specialization certificate.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Smart Data Collective

NOVEMBER 18, 2020

You should learn what a big data career looks like , which involves knowing the differences between different data processes. You should learn what a big data career looks like , which involves knowing the differences between different data processes. What is Data Science? Where to Use Data Science? Where to Use Data Mining?

Data Mining 143

Data Mining Data Mining Data Mining Data Science 143

Flipboard

MARCH 6, 2023

Jump Right To The Downloads Section Triplet Loss with Keras and TensorFlow In the first part of this series, we discussed the basic formulation of a contrastive loss and how it can be used to learn a distance measure based on similarity. Looking for the source code to this post?

Deep Learning 97

Deep Learning Deep Learning Data Pipeline Computer Science 97

Mlearning.ai

JANUARY 27, 2023

Save this blog for comprehensive resources for computer vision Source: appen Working in computer vision and deep learning is fantastic because, after every few months, someone comes up with something crazy that completely changes your perspective on what is feasible. They are free to use and millions of images are present on the below site.

Deep Learning 98

Deep Learning Deep Learning Python Data Scientist 98

Explosion

DECEMBER 30, 2021

Jan 19: The new year started with the Portuguese translation of our free spaCy online course: PLN avançado com spaCy. Mar 29: Ines joined the at the German Python Podcast to talk about Natural Language Processing with spaCy. ? For Explosion, it was a very productive year. Special thanks to Cristiana Straccialana Parada. ?

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Python 52

Flipboard

FEBRUARY 13, 2023

In addition, we discussed metric learning and how contrastive losses can be used to learn a distance measure in the embedding space, which can help us effectively quantify the similarity between input images. We tried to understand how these losses can help us learn a distance measure based on similarity.

Data Pipeline 106

Data Pipeline Deep Learning Deep Learning Python 106

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

Deep Learning Deep Learning AI AI 64

Flipboard

MARCH 27, 2023

The success of PyTorch is attributed to its simplicity, first-class Python integration, and imperative style of programming. In this series, you will learn about Accelerating Deep Learning Models with PyTorch 2.0. TorchDynamo and TorchInductor To learn what’s new in PyTorch 2.0, is available as a Python pip package.

Deep Learning 117

Deep Learning Deep Learning Python Natural Language Processing 117

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

Deep Learning 60

Deep Learning Deep Learning Python AI 60

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?

Data Pipeline 64

Data Pipeline Deep Learning Deep Learning Computer Science 64

PyImageSearch

FEBRUARY 5, 2024

Introduction to Model Evaluation in Face Recognition In this tutorial, we will take this further and learn how to evaluate our trained model using Keras and TensorFlow. Introduction to Model Evaluation in Face Recognition In this tutorial, we will take this further and learn how to evaluate our trained model using Keras and TensorFlow.

Database 60

Database Data Pipeline Deep Learning Deep Learning 60

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. In our previous tutorial on autoencoders , we learned that they are not inherently generative. Let’s get started!

Deep Learning 120

Deep Learning Deep Learning Clustering Computer Science 120

PyImageSearch

JANUARY 22, 2024

To learn how to get started with using diffusers, just keep reading. 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. Setup and Imports Diffusers But What Is AutoPipeline ?

Deep Learning 64

Deep Learning Deep Learning Algorithm Artificial Intelligence 64

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. Picking up where we left off, let us revise what we learned in Part 1. The previous one was all about introducing Causality in the briefest way possible. You can find it right here.

Deep Learning 67

Deep Learning Deep Learning Machine Learning Machine Learning 67

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.

Python 64

Python Deep Learning Deep Learning AI 64

PyImageSearch

JANUARY 8, 2024

Home Table of Contents Adversarial Learning with Keras and TensorFlow (Part 1): Overview of Adversarial Learning Configuring Your Development Environment Need Help Configuring Your Development Environment?

Deep Learning 64

Deep Learning Deep Learning Data Pipeline Computer Science 64

PyImageSearch

JANUARY 16, 2023

In this tutorial, you will learn about Gradient Boosting, the final precursor to XGBoost. Jump Right To The Downloads Section Scaling Kaggle Competitions Using XGBoost: Part 3 Gradient Boost at a Glance In the first blog post of this series, we went through basic concepts like ensemble learning and decision trees.

Decision Trees 102

Decision Trees Deep Learning Deep Learning Algorithm 102

PyImageSearch

JANUARY 23, 2023

The reasoning behind that is simple; whatever we have learned till now, be it adaptive boosting, decision trees, or gradient boosting, have very distinct statistical foundations which require you to get your hands dirty with the math behind them. In this tutorial, you will learn the magic behind the critically acclaimed algorithm: XGBoost.

Deep Learning 107

Deep Learning Deep Learning Algorithm Decision Trees 107

PyImageSearch

FEBRUARY 27, 2023

Table of Contents Learning JAX in 2023: Part 2 — JAX’s Power Tools grad , jit , vmap , and pmap ?? Summary Citation Information Learning JAX in 2023: Part 2 — JAX’s Power Tools grad , jit , vmap , and pmap In this tutorial, you will learn the power tools of JAX, grad , jit , vmap , and pmap.

Machine Learning 107

Machine Learning Machine Learning Deep Learning Deep Learning 107

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.

Deep Learning 52

Deep Learning Deep Learning Python AI 52

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.

AI 67

AI AI Deep Learning Deep Learning 67

PyImageSearch

NOVEMBER 6, 2023

By the end, you’ll not only have a comprehensive understanding of U-Net’s capabilities but also a holistic view of the autoencoder universe. In this concluding tutorial, we’ll delve deep into the captivating world of image segmentation, harnessing the power of the U-Net architecture. Looking for the source code to this post?

Deep Learning 67

Deep Learning Deep Learning Python Data Preparation 67

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.

Deep Learning 52

Deep Learning Deep Learning Python Artificial Intelligence 52

Explosion

MAY 8, 2016

But more importantly, teaching spaCy to speak German required us to drop some comfortable but English-specific assumptions about how language works and made spaCy fit to learn more languages in the future. When Germans learn English in school, one of the first things they are taught to memorize is Subject-Verb-Object or SVO.

Algorithm 40

Algorithm Clustering Python Natural Language Processing 40

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. Object detection is no different.

Deep Learning 120

Deep Learning Deep Learning Algorithm Support Vector Machines 120

PyImageSearch

DECEMBER 12, 2022

Setting Up the Prerequisites Building the Model Assessing the Model Summary Citation Information Scaling Kaggle Competitions Using XGBoost: Part 2 In our previous tutorial , we went through the basic foundation behind XGBoost and learned how easy it was to incorporate a basic XGBoost model into our project. Table 1: The Dataset.

Decision Trees 52

Decision Trees Deep Learning Deep Learning Exploratory Data Analysis 52

PyImageSearch

JUNE 5, 2023

Specifically, we will train our CycleGAN model using Keras and TensorFlow and also learn how we can use it to perform unpaired image translation on novel unseen images. Be sure to join PyImageSearch University — you’ll be up and running with this tutorial in minutes. Learning on your employer’s administratively locked system?

Deep Learning 56

Deep Learning Deep Learning Data Pipeline Python 56

PyImageSearch

JANUARY 29, 2024

We will discuss the two most common adversarial attacks, that is, the Projected Gradient Descent (PGD attack) and the Fast Gradient Sign Method (FGSM attack), and understand their mathematical formulation.

Deep Learning 56

Deep Learning Deep Learning Machine Learning Machine Learning 56

PyImageSearch

MARCH 13, 2023

Transformer s In this tutorial, we will learn how to train MaskFormer on a Colab Notebook to perform panoptic segmentation. You will learn how to: Load and preprocess the dataset Use the transformers Trainer class to train models Evaluate your trained segmentation model Project Structure For this tutorial, we will use a Colab Notebook.

Deep Learning 117

Deep Learning Deep Learning Python Computer Science 117

PyImageSearch

SEPTEMBER 11, 2023

Furthermore, you will learn how SAM can be used for making segmentation predictions in real-time and how you can integrate it with your own computer vision projects. To learn how to use SAM in your own projects, just keep reading. Looking for the source code to this post?

AI 117

AI AI Deep Learning Deep Learning 117

PyImageSearch

JULY 17, 2023

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., visualizing the latent space, uniform sampling of data points from this latent space, and recreating images using these sampled points).

Deep Learning 98

Deep Learning Deep Learning Python Machine Learning 98

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.

Deep Learning 102

Deep Learning Deep Learning Python Algorithm 102

Explosion

DECEMBER 28, 2019

Jan 28: Ines then joined the great lineup of Applied Machine Learning Days in Lausanne, Switzerland. Jan 28: Ines then joined the great lineup of Applied Machine Learning Days in Lausanne, Switzerland. Now their state-of-the-art Universal Dependencies models can be directly used in your spaCy pipeline. ? Got a question?

Machine Learning 52

Machine Learning Machine Learning Python Natural Language Processing 52

Explosion

JUNE 15, 2020

The vectors are trained using FastText with the same settings as FastText’s word vectors (CBOW, 300 dimensions, character n-grams of length 5). To find out if you need to update your models, you can run python -m spacy validate. Users can initialize the tokenizer with both pkuseg and custom models and customize the user dictionary.

Clustering 52

Clustering Natural Language Processing Machine Learning Machine Learning 52