TensorFlow 101: Introduction to Deep Learning
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Updated
Dec 29, 2023 - Jupyter Notebook
TensorFlow 101: Introduction to Deep Learning
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)
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data
This repository explores the variety of techniques and algorithms commonly used in deep learning and the implementation in MATLAB and PYTHON
Implementation of simple autoencoders networks with Keras
PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models
Autoencoders for Link Prediction and Semi-Supervised Node Classification (DSAA 2018)
Place recognition with WiFi fingerprints using Autoencoders and Neural Networks
A tensorflow.keras generative neural network for de novo drug design, first-authored in Nature Machine Intelligence while working at AstraZeneca.
Machine Learning Course at IIT Bhilai
Hiding Images within other images using Deep Learning
Codes and Templates from the SuperDataScience Course
Automatic feature engineering using deep learning and Bayesian inference using TensorFlow.
Deep Learning-based Clustering Approaches for Bioinformatics
Compressive Autoencoder.
Intro to Deep Learning by National Research University Higher School of Economics
Data and code related to the paper "ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa..." Jie Tan, et al · mSystems · 2016
implementation of WSAE-LSTM model as defined by Bao, Yue, Rao (2017)
Collection of operational time series ML models and tools
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