mahditiba Goto Github PK

alpaca-classification-mobilenet-with-transfer-learning

using transfer learning on a pre-trained CNN to build an Alpaca/Not Alpaca classifier!

art-generation-with-neural-style-transfer

Generate novel artistic images using neural style transfer algorithm

braintumordetection

cat-noncat-classifier

building a deep L-layer neural network From Scratch, and apply it to the problem of classifying cat images from non-cat images.

cifar-image-classifier

conv-net-application

Create a mood classifer using the TF Keras Sequential API - Build a ConvNet to identify sign language digits using the TF Keras Functional API

debiasing-word-vector

modifying word embeddings to reduce their gender bias. Reducing bias is an important consideration in NLP

deep-neural-network-from-scratch

implementation of all the functions required to build a deep neural network.

emojify

Have you ever wanted to make your text messages more expressive? emojifier app will help you do that.

face-recognition-system

State-of-art face recognition system

face-recognition-with-facenet

Perform face verification and face recognition with encodings

facial-emotions-recognition

Facial emotions recognition based on deep learning algorithm

facial-expression-recognition

gradient-checking-in-backprop

Implement gradient checking to verify the accuracy of your backprop implementation.

happysadclassification

human-presence-detection

In this Project we are going to conduct a project to detect human presence in the scene.

infertility-analysis

Using deep learning methods for semen analysis which includes sperm motility, morphology and vitality etc.

initialization-methods-for-dnns

A few different initialization methods, including random, zeros, and He initialization, and investigate how each leads to different results.

one-layer-neural-network-mathematics

Implementation of one-layer neural network from scratch

optimization-algorithms

Apply optimization methods such as (Stochastic) Gradient Descent, Momentum, RMSProp and Adam

reqularization-methods-for-dnns

investigate how Regularization methods can affect on generalization of the models. methods like L2 Regularization, Dropout

residual-network-implimentation

building a very deep convolutional network, using Residual Networks (ResNets).

road-surface-semantic-segmentation

building U-Net Model on a self-driving car dataset.

vehicle-detection

implementation object detection using the very powerful YOLO model.