#resnet50

Here’s an advanced Keras project that builds on self‑supervised contrastive learning—going beyond conventional supervised tasks. It will challenge your expertise and stretch your skills. 🧠 Project Title ai-ml-ds‑XjKLmPqRZb – “Self‑Supervised Contrastive Representation Learning with SimCLR in Keras”Filename: simclr_contrastive_learning_keras.py 🗂 Description Implement the SimCLR framework to…

Here’s an advanced Keras project that builds on self‑supervised contrastive learning—going beyond conventional supervised tasks. It will challenge your expertise and stretch your skills. 🧠 Project Title ai-ml-ds‑XjKLmPqRZb – “Self‑Supervised Contrastive Representation Learning with SimCLR in Keras”Filename: simclr_contrastive_learning_keras.py 🗂 Description Implement the SimCLR framework to…

Here’s an advanced Keras project that builds on self‑supervised contrastive learning—going beyond conventional supervised tasks. It will challenge your expertise and stretch your skills. 🧠 Project Title ai-ml-ds‑XjKLmPqRZb – “Self‑Supervised Contrastive Representation Learning with SimCLR in Keras”Filename: simclr_contrastive_learning_keras.py 🗂 Description Implement the SimCLR framework to…

Here’s an advanced Keras project that builds on self‑supervised contrastive learning—going beyond conventional supervised tasks. It will challenge your expertise and stretch your skills. 🧠 Project Title ai-ml-ds‑XjKLmPqRZb – “Self‑Supervised Contrastive Representation Learning with SimCLR in Keras”Filename: simclr_contrastive_learning_keras.py 🗂 Description Implement the SimCLR framework to…

Here’s an advanced Keras project that builds on self‑supervised contrastive learning—going beyond conventional supervised tasks. It will challenge your expertise and stretch your skills. 🧠 Project Title ai-ml-ds‑XjKLmPqRZb – “Self‑Supervised Contrastive Representation Learning with SimCLR in Keras”Filename: simclr_contrastive_learning_keras.py 🗂 Description Implement the SimCLR framework to…

This is an image Classification tutorial using Python, TensorFlow, and Keras with Convolutional Neural Networks (CNNs).

In this video, we'll learn how to use pre-trained models to classify images based on Resnet50 and Mobilenet.

Introduction to image classification and CNNs.

Using TensorFlow and Keras for building the classification process.

Loading pre-trained models from the Keras application library (such as ResNet50 and MobileNet).

Explaining how to prepare a fresh image for classification, including resizing it to the model's shape and converting it to a batch of images using the Numpy expand_dims function.

Running the prediction process on the pre-trained models (ResNet50 and MobileNet) for the given image.

Comparing and analyzing the quality of predictions between the two models.

If you are interested in learning modern Computer Vision course with deep dive with TensorFlow , Keras and Pytorch , you can find it here  : http://bit.ly/3HeDy1V

Perfect course for every computer vision enthusiastic

A recommended book , https://amzn.to/44GnlLW - "Make Your Own Neural Network - An In-depth Visual Introduction For Beginners "

The link for the video : https://youtu.be/40_NC2Ahs_8

I also shared the Python code in the video description .

Enjoy

Eran