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2d-cnn-in-tensorflow-2.0-for-cifar10-dataset-classification-

airline-passenger-satisfaction

boston-housing-price-prediction-

brain-tumor-detection-using-pytorch-cnn

breast-cancer-detection-using-cnn-in-python-tensorflow-2.0

bulding-a-cnn-for-image-classification--cifar-10

business-analysis-absenteeism-at-work-

car-and-pedistrian-detection

car-price-prediction

chat-bot-seq2seq-model-deep-nlp

chatbot using dnlp using seq2seq model lstms cells

chatbot

AL Chat Bot with Deep Learning

cocoapi

COCO API - Dataset @ http://cocodataset.org/

covid-19-face-mask-detector-with-opencv-keras-tensorflow-and-deep-learning-code-

first we will train our model then used for detections Training: Here we’ll focus on loading our face mask detection dataset from disk, training a model (using Keras/TensorFlow) on this dataset, and then serializing the face mask detector to disk Deployment: Once the face mask detector is trained, we can then move on to loading the mask detector, performing face detection, and then classifying each face as with_mask or without_mask

covid-19-face_mask_detector-deep-learning-opencv-

first we will train our model then used for detections Training: Here we’ll focus on loading our face mask detection dataset from disk, training a model (using Keras/TensorFlow) on this dataset, and then serializing the face mask detector to disk Deployment: Once the face mask detector is trained, we can then move on to loading the mask detector, performing face detection, and then classifying each face as with_mask or without_mask

covid-19_x-ray-detection

using tensorflow 2-gpu , VGG-16, deep learninig to detect the covid-19 using x-ray . to analysis in x-ray and classify them as corona or no-corona and finally i deploy it using flask

credit-card-fraud-detection-using-cnn-in-tensorflow-2.0-

customer-churn-modelling-

deep-learning-based-object-detection-with-opencv-mobilenet-ssdeep

detection objects using pre-trained models with SSD method. we use pre-trained models MobileNets to impliment with SSD Model.

deepstream-application-object-detection

diabetes-prediction-using-deep-learning-pytorch-ann-model-

employee_future_prediction

face-detection-in-python-using-opencv

face-recognition-using-facenet-deep-learning-opencv

Face Recognition using FaceNET ,Deep Learning & dnn-opencv which train on 5 type of classes . which there is also essential class that is unknown.

fake-news-classifier-using-lstm-bi-lstm

Fake News Classification using both LSTM AND BiDIRECTIONAL LSTM where i have use both and got accuracy about 98% in training data and 90% in validation data

footballplayer-qml-qt

QML/QT C++ dynamic update the list for football team player using qml qt c++

gan-implementation-from-scratch-using-pytorch

Generative adversarial networks (GANs) are a powerful type of machine learning model that can be used to generate new, synthetic data that is similar to a training datasets. They consist of two networks: a generator network and a discriminator network. The generator network is responsible for generating new data, while the discriminator network is

image-captioning-using-tensorflow-2-inceptionnet_v3-lstm

keras

Deep Learning for humans

lstm

google stock price predection

malaria-parasite-detection-using-cnn-using-tensorflow-2.0