A five-course specialization covering the foundations of Deep Learning, from building CNNs, RNNs & LSTMs to choosing model configurations & paramaters like Adam, Dropout, BatchNorm, Xavier/He initialization, and others.

Deep Deterministic Policy Gradient(DDPG) - Deep Reinforcement Learning

Applying neural network with adam optimizer on heart failure clinical records dataset to compare test errors of sigmoid, tanh, and relu activation functions

Dog Vs Cat Classification using Convolutional Neural Network (CNN)

This project is about building a artificial neural network using pytorch library. I am sharing the code and output for my project.

Image caption generator project is automatically describes images with coherent and relevant textual captions.

A collection of projects introducing neural networks and data analysis concepts. From search and genetic algorithms to autoencoders and VAEs.

A Generative Adversarial Network for the generation of new synthetic art.

Brain Tumor Detection using Deep learning.

An AI agent for Pocket Tanks

Neural style transfer in PyTorch using the VGG-19 Pretrained model to create artistic style image

It predicts whether the patient has Heart Disease or not

Deep Learning: CNN Model

Модуль 9. Підбір гіперпараметрів НМ. Глибоке навчання. Tensorflow. Keras.

This repository contains various tools of machine learning like the GRADIENT DESCENT and others, implemented from scratch using packages like numPy, matplotlib, pandas

Logistic Regression with different optimizers in Python from scratch

Using advanced deep learning techniques on the MNIST dataset. Over 98% validation set accuracy.

Our custom AI Pipeline on image classification for 2019 Chung-ang-University-hackathon.

Implement numerical optimization algorithms for data science.

Hand written digit recognition model done with MNIST dataset using tensorflow keras library. the model can detect the handwritten number and achieved 97% accuracy with 10000 data samples provided with MNIST