Name:Computer Vision Fundamentals with Google Cloud Description:Offered by Google Cloud. This course describes different types of computer vision use cases and then highlights different machine learning ... Enroll for free. Course Link: https://www.coursera.org/learn/sequence-models-tensorflow-gcp ...
Beginner Computer Vision Projects Let’s explore some project ideas, starting with the beginner level. At this level, most projects are related to classification or detection techniques, such as face emotion recognition or determining whether an object is in the image or not. 1. Face Mask Detecti...
To work with YOLOv, the requirements are a computer equipped with a GPU, deep learning frameworks (like PyTorch or TensorFlow), and access to the YOLOv8 repository on GitHub. Conclusion This blog post highlighted the advancements of YOLOv8, the most recent iteration of the YOLO algorithm, whic...
Advanced Deep Learning with TensorFlow 2 and Keras (Updated for 2nd Edition) This is the code repository forAdvanced Deep Learning with TensorFlow 2 and Keras, published byPackt. It contains all the supporting project files necessary to work through the book from start to finish. ...
NVIDIA TAO is a low-code AI toolkit, containing solutions to train, fine tune, and optimize Deep Learning models for various computer vision use cases. These Deep Learning solutions are implemented across many popular training frameworks, such as TensorFlow (version 2.11.x and version 2.x), ...
“Advanced Deep Learning with TensorFlow 2 and Keras – Second Edition is a good and big step into an advanced practice direction. It’s a brilliant book and consider this as a must-read for all.” — Dr. Tristan Behrens, Founding Member of AI Guild and Independent Deep Learning Hands-On...
The network model is implemented using TensorFlow V2.4 (Google LLC) running on a desktop computer (Nvidia GTX 1080 Ti 11 G, Intel i7-6800K CPU with 6 cores, 128 GB RAM and the Microsoft Windows 10 operating system). The network parameters were optimized using the Adam optimizer58. Due...
Conventional methods for bridge inspection are labor intensive and highly subjective. This study introduces an optimized approach using real-time learning-based computer vision algorithms on edge devices to assist inspectors in localizing and quantifying
projectssuchasbuildingpowerfulmachinelearningmodelswithensemblestopredictemployeeattrition.You'llexploredifferentclusteringtechniquestosegmentcustomersusingwholesaledataanduseTensorFlowandKeras-Rforperformingadvancedcomputations.You’llalsobeintroducedtoreinforcementlearningalongwithitsvarioususecasesandmodels.Additionally,itshows...
Playing with TensorFlow playground Convolutional neural network Kernel Max pooling Recurrent neural networks (RNN) Long short-term memory (LSTM) Deep learning for computer vision Classification Detection or localization and segmentation Similarity learning Image captioning Generative models Video analysis Developm...