Study of leNet implementation in Python3.6 with Keras+Tensorflow backend.
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Updated
Sep 12, 2018
Study of leNet implementation in Python3.6 with Keras+Tensorflow backend.
A 2D Conv Network trained to classify birdcall of 10 different species.
LGMVIP Data Science Internship 2020
How to create a caption that describes and images with Deep Learning? With a CNN and a RNN combined.
Implemented a CNN model using keras and tensorflow on fashion mnist. Achieved more than 93% accuracy.
Style transfer: A computer vision technique that allows you to recompose the content of an image in the style of another.
Diabetic retinpopath classfiction using trnasfer learning and Data augmentation
Detect Hands and change the system Volume.
This project is used to identify the fake vehicle by using number plate and face recognition . Problem statement provided by KAVACH HACKATHON 2023 PSID = KVH-005
This tutorial shows how to classify images of flowers using a tf.keras.Sequential model and load data using tf.keras.utils.image_dataset_from_directory. It demonstrates the following concepts: Efficiently loading a dataset off disk. Identifying overfitting and applying techniques to mitigate it, including data augmentation and dropout.
CNN implementation for dog breed identification
ML texture analysis research in UF SSTP
Multiclass Image classification using Convolutional Neural Networks.
🔶 Covered the basics of Linear Algebra and related concepts in Deep Learning and Neural Networks and then implementing models based on the concepts learned.
CNN model trained by transferring learning from YOLOv5 which recognizes and censors numberplates in images in order to protect the identity of original owner of a car.
This project uses Keras and OpenCV to detect whether a person is wearing a mask through image or video input.
Human Face Recognition. Mini Project for Deep Learning which can detect human face. With Mini Project Report. For demo watch video. Thank you for visit.
If you want to understand how CNN(resnet50) works layer by layer. you at the right place.
My solution to stanford cs231n: CNN for visual recognition
The repo contains all my codes for Data Analysis on MNIST dataset. The algorithms used were as follows: Random Forest, SVM, KNN, Convolution Neural Nets.
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