PyImageSearch

JULY 17, 2023

Home Table of Contents Implementing a Convolutional Autoencoder with PyTorch Configuring Your Development Environment Need Help Configuring Your Development Environment? 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

ML Review

FEBRUARY 11, 2019

It is lot easier and faster to use and it has an amazing documentation. If you don’t know it already, NLP had a huge hype of transfer learning in this past 1 year, starting with ULMFit , ELMo , GLoMo , OpenAI transformer , BERT and recently Transformer-XL for further improving language modeling capabilities of the current state of the art.

Deep Learning 52

Deep Learning Deep Learning ML ML 52

Heartbeat

MAY 29, 2023

These models have the potential to revolutionize industries ranging from customer service to scientific research, but their capabilities and limitations are still not fully understood. We will explore how to better understand the data that these models are trained on, and how to evaluate and optimize them for real-world use.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

The MLOps Blog

APRIL 20, 2023

With over 3 years of experience in designing, building, and deploying computer vision (CV) models , I’ve realized people don’t focus enough on crucial aspects of building and deploying such complex systems. Domain-specific preprocessing: for certain tasks, domain-specific preprocessing can lead to better model performance.

ML 64

ML ML Cross Validation Data Quality 64

Mlearning.ai

JANUARY 24, 2024

Transformer — A detailed explanation from perspectives of tensor shapes and PyTorch implementation. A detailed explanation to transformer based on tensor shapes and PyTorch implementation. We are in the era of generative AI and many Large Language Models (LLMs), like GPTs, Llama, and Palm, etc., Overall Architecture Figure 1.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Deep Learning Deep Learning 52

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.

Deep Learning 56

Deep Learning Deep Learning Computer Science Computer Science 56

PyImageSearch

JULY 24, 2023

Going Beyond with Keras Core The Power of Keras Core: Expanding Your Deep Learning Horizons Show Me Some Code JAX Harnessing model.fit() Imports and Setup Data Pipeline Build a Custom Model Build the Image Classification Model Train the Model Evaluation Summary References Citation Information What Is Keras Core?

Deep Learning 98

Deep Learning Deep Learning Machine Learning Machine Learning 98

PyImageSearch

MAY 1, 2023

YOLO, or YOLOv1, was the first single-stage object detection model. Redmon and Farhadi (2017) published YOLOv2 at the CVPR Conference and improved the original model by incorporating batch normalization, anchor boxes, and dimension clusters. And then came the YOLO model wave. Looking for the source code to this post?

Deep Learning 107

Deep Learning Deep Learning Python Algorithm 107