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Twitter Sentiment Analysis with Deep Learning using BERT and Hugging Face
Brain Tumor Classification with Efficient Net Convolutional Neural Network (CNNs)
We will build and train a Deep Convolutional Neural Network (CNN) with Residual Blocks to detect the type of scenery in an image. In addition, we will also use a technique known as Gradient-Weighted Class Activation Mapping (Grad-CAM) to visualize the regions of the inputs and help us explain how our CNN models think and make decision.
Build and train a convolutional neural network (CNN) in Keras from scratch to recognize to predict 7 types of facial expressions (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral)
In this work, we will approach the forecast of daily closing price series of the Bitcoin cryptocurrency using data on prices of prior years (January 2016 to August 2020).
Repositories
640Twitter Sentiment Analysis with Deep Learning using BERT and Hugging Face
Build and train a convolutional neural network (CNN) in Keras from scratch to recognize to predict 7 types of facial expressions (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral)
We will build and train a Deep Convolutional Neural Network (CNN) with Residual Blocks to detect the type of scenery in an image. In addition, we will also use a technique known as Gradient-Weighted Class Activation Mapping (Grad-CAM) to visualize the regions of the inputs and help us explain how our CNN models think and make decision.
Brain Tumor Classification with Efficient Net Convolutional Neural Network (CNNs)
In this work, we will approach the forecast of daily closing price series of the Bitcoin cryptocurrency using data on prices of prior years (January 2016 to August 2020).
No description provided.
In this project, we will identify the characteristics of women who are more likely to develop cervical cancer and use Automatic Machine Learning H2O AutoML to make prediction on whether or not a certain customer would buy a term deposit. We will also use Explainable AI (XAI) methods such as Variable Importance Plot, Partial Dependence Plot, SHAP Summary Plot, and LIME to explain how each of our feature input affects our model prediction.
Config files for my GitHub profile.
Build Bi-directional GRU to predict the degradation rates at each base of an RNA molecule which can be useful to develop models and design rules for RNA degradation to accelerate mRNA vaccine research and deliver a refrigerator-stable vaccine against SARS-CoV-2, the virus behind COVID-19.
We will leverage population's information on immunization, mortality, social- economic and other health related factors and use Automatic Machine Learning H2O AutoML to make prediction on life expectancy of a certain population. We will also use Variable Importance Plot, Partial Dependence Plot, and SHAP Summary Plot to explain how each of our feature input affects our model prediction.
No description provided.
We will use Biopython to handle biological sequence data stored in FASTA & PDB (Protein Data Bank) and XML format. Using this sequence data, we will explore and create an interactive three-dimensional (3D) representation of SARS-CoV-2 (Coronavirus) protein structures.
Chest X-rays image classification for early Pneumonia detection using deep neural networks
Real estate value prediction using multivariate regression models
No description provided.
Awesome CryptoPunks Bubble (Anno 2021) - Modern 24x24 Pixel Crypto Art on the Blockchain since 2017 - 10 000 Unique Collectible Characters Generated Algorithmically ++ Bonus: Inside the CryptoPunksMarket Contract Service
Collection of interactive Jupiter Notebook widgets and graphs.
Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1
Investigation of factors associated with commercially successful movies with Exploratory Data Analysis
Water point functionality prediction using binary classification models
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Data Science Bootcamp Labs and Projects
Scrapes an instagram user's photos and videos
No description provided.
Ultimate Solidity, Blockchain, and Smart Contract - Beginner to Expert Full Course | Python Edition
We will build and train a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images of fashionable clothes.
We are going to improve the quality of discussions on Quora platform by detecting toxic content. Specifically, we want to build a predictive NLP model that labels questions asked on Quora as either sincere or insincere.
In this project, we will build, train and test a Convolutional Neural Networks with Residual Blocks to predict facial key point coordinates from facial images.