Pinned Repositories
Noodle
Advanced Distributed repo @ Weather Forecast App
cloud-faas
Web services use FaaS on Google Cloud for processing and visualizing textual data from books
cloud-vision-faas
Webservices using FaaS ,Vision API for facial and landmark detection in images.
Coursera-Certificates
Coursera Certificates
crs-project
FocusMetrics
Computed Focus metrics by implementing brenner's algorithm in C++ using OpenCV.
hetergoneous-graph-indexing
Developed an API which leverages graph-based cross domain ranking , community detection to make recommendations from a heterogeneous graph ecosystems.
map-reduce
Implemented distributed map-reduce system and deployed microservices for mapper ,reducer, master nodes on cloud VMs for building a dynamic data processing service.
Recommendation-System
Restaurant Recommendation system
self-assesment
Full stack containerized services deployed on GCP using REST, ReactJS, Flask, Docker for registering user's self assessment data, evaluating the algorithm for course recommendation.
naga-anjaneyulu's Repositories
naga-anjaneyulu/FocusMetrics
Computed Focus metrics by implementing brenner's algorithm in C++ using OpenCV.
naga-anjaneyulu/hetergoneous-graph-indexing
Developed an API which leverages graph-based cross domain ranking , community detection to make recommendations from a heterogeneous graph ecosystems.
naga-anjaneyulu/Recommendation-System
Restaurant Recommendation system
naga-anjaneyulu/cloud-faas
Web services use FaaS on Google Cloud for processing and visualizing textual data from books
naga-anjaneyulu/cloud-vision-faas
Webservices using FaaS ,Vision API for facial and landmark detection in images.
naga-anjaneyulu/Coursera-Certificates
Coursera Certificates
naga-anjaneyulu/crs-project
naga-anjaneyulu/map-reduce
Implemented distributed map-reduce system and deployed microservices for mapper ,reducer, master nodes on cloud VMs for building a dynamic data processing service.
naga-anjaneyulu/self-assesment
Full stack containerized services deployed on GCP using REST, ReactJS, Flask, Docker for registering user's self assessment data, evaluating the algorithm for course recommendation.
naga-anjaneyulu/DataSets
naga-anjaneyulu/EyeDetect-Color
Eye Detection and coloring using OpenCV in Java .
naga-anjaneyulu/Happiness-Detection
Facial recogniton and smile detection using openCV.
naga-anjaneyulu/Hello-World
naga-anjaneyulu/Image-Creation---GANs
I have used an inverted CNN as a generator and CNN as a Discriminator .Firstly,Trained the discriminator using real images , then with fake images through the 3 channels provided by the generator.Used ReLU activation for forward propogation in Generator network, LeakyReLU for forward propogation in Discriminator network and then computed the losses using binary cross entropy and update the weights through backpropogation .Check Results folder for generated images .
naga-anjaneyulu/Keyword-Extraction
I have extracted keywords from a pdf document using gensim .
naga-anjaneyulu/Machine_Learning
Machine Learning Use Cases .
naga-anjaneyulu/Object-Detection-using-SSD
I have used a pre tarined SSD neural network which can dectect over 8 objects for object detection.The SSD network detects objects in a drone vedio which has 67 frames using Pytorch .
naga-anjaneyulu/POS-Tagging
Parts of speech tagging through recurrent neural netowrks in Tensorflow.
naga-anjaneyulu/raserei
naga-anjaneyulu/Resnet-BloodCells
ResNet -50 _ Transfer Learning
naga-anjaneyulu/Skip-Gram-
Skip gram model modification for the Word2Vec model using the negative sampling
naga-anjaneyulu/StyleTransfering
StyleTransfering .
naga-anjaneyulu/terragoat
TerraGoat is Bridgecrew's "Vulnerable by Design" Terraform repository. TerraGoat is a learning and training project that demonstrates how common configuration errors can find their way into production cloud environments.
naga-anjaneyulu/TransferLearning-with-VGG16
Implemented transfer learning in keras with the example of training a 3 class classification model using VGG-16 pre-trained weights.The vgg-16 isthe CNN model trained on more than a million images of 1000 different categories.