Experiments with different ML techniques using TensorFlow and Sci-kit-learn in Python
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Updated
Mar 24, 2017 - Jupyter Notebook
Experiments with different ML techniques using TensorFlow and Sci-kit-learn in Python
Deep unsupervised learning methods for the identification and characterization of TCR specificity to Sars-Cov-2
Repository containing TensorFlow tutorial Notebooks
Notebooks containing examples for different DNN components
A series of 12 assignments/labs regarding Stochastic Processes and Machine Learning including a plethora of models and techniques implemented in Google Colab notebooks
This repository contains all the Google Colab Notebooks where I have implemented different Neural Networks like ANN, CNN, RNN, and also other Deep Learning models such as Self-organizing Maps, Autoencoders, Boltzmann Machines, etc.
Repo with my most popular kaggle notebooks. I've put a lot of effort into them back in the day, so they are highly curated and well documented.
A Jupyter Notebook encasing a Retrieval system for similar images based on the Autoencoder networks and Euclidean Distance and Clustering based approach.
Repository of all notebooks used in workshop at NII.
This repository consists a set of Jupyter Notebooks with a different Deep Learning methods applied. Each notebook gives walkthrough from scratch to the end results visualization hierarchically. The Deep Learning methods include Multiperceptron layers, CNN, GAN, Autoencoders, Sequential and Non-Sequential deep learning models. The fields applied …
Nvidia DLI workshop on AI-based anomaly detection techniques using GPU-accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) and then implement and compare supervised and unsupervised learning techniques.
Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data
Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)
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