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This repository contains my solutions for the Coursera course TensorFlow: Advanced Techniques Specialization. Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs.
Used the Functional API to built custom layers and non-sequential model types in TensorFlow, performed object detection, image segmentation, and interpretation of convolutions. Used generative deep learning including Auto Encoding, VAEs, and GANs to create new content.
Study Case API with SOLID and no OOP
The goal of this project is to build a neural network that takes an MNIST handwritten digit (0-9) image and a random number (digit 0-9) as inputs and returns the predicted class label (0-9) for the input image and its addition (sum) with the input random number as summed output (range 0-18) label as outputs.
This deep learning project focuses on building a robust air quality classifier from photos using two different deep learning architectures.
RNN and CNN based Relation Extraction models in tensorflow 2.0
Neural Network to estimate wine's quality and its type
Prediction of acute inflammations of urinary bladder and acute nephritises using UCI Acute Inflammations Data Set.
TensorFlow: Advanced Techniques Specialization by Laurence Moroney, and Eddy Shyu in Coursera by DeapLearning.AI
Keras functional api on DonorsChoose dataset.
Artificial Neural Networks with R using keras, neuralnet and functional API.
Digital Image Processing Course | Home Works Design| Fall 2021 | Dr. MohammadReza Mohammadi
Design Deep Learning Models
Predict whether a DonorsChoose.org project proposal submitted by a teacher will be approved.
This repository is a testament to my growth and understanding of deep learning. It highlights foundational skills in TensorFlow, demonstrates hands-on applications like car price prediction, and culminates in an advanced medical imaging project for malaria diagnosis.
Tensorflow 2: for Deep Learning Specialization | Imperial College London
I showcase that I have broad set of skills regarding machine learning algorithms since I use Logistic Regression, XGBoost and Neural Networks in this project. Especially that I have a good understanding regarding neural networks and the Keras library.
This is my take on the Wine Quality Dataset. (Multi Label problem)
Age and Gender prediction from facial images using VGG16 and the Keras Functional API.
Streamlit Demo of CIFAR10 Data Classifiers
Building Advanced Neural Networks with Tensorflow: A Deepdive
Convolutional Neural Network project for sign language detection.
This repository contains application of functional api and sequential api for the hello world dataset in computer vision
Git repository for the following video: https://www.youtube.com/watch?v=qj4NHdX2wfI&feature=youtu.be