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)

iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

Anomaly detection implemented in Keras

AI deep learning neural network for anomaly detection using Python, Keras and TensorFlow

Unsupervised anomaly detection with generative model, keras implementation

Anomaly detection using Autoencoder implemented with Keras 2.

Keras implementation of LSTM-VAE model for anomaly detection

PyTorch and Keras implementation of CompactCNN for Anomaly Detection in textured surfaces.

CNN based autoencoder combined with kernel density estimation for colour image anomaly detection / novelty detection. Built using Tensforflow 2.0 and Keras

AutoEncoders for Event Detection (AEED): a Keras-based class for anomaly detection in water sensor networks.

anomaly detection using tensorflow, Keras, and Open CV

Lets do anomaly detection with keras!

CNN autoencoder is trained on the MNIST numbers dataset for image reconstruction. Anomaly detection is carried out by calculating the Z-score. The framework used is Keras.

Anomaly Detection using SVM one class and Auto encoders in Keras

Satellite telemetry anomaly detection using Keras

This project demonstrates how to build a Convolutional Neural Network (CNN) model for anomaly detection in time series data using Keras. It is implemented in Google Colab and uses a CSV dataset containing time series values. The model detects anomalies based on reconstruction errors by setting a dynamic threshold.

Keras-1D-VAE-Anomaly-Detection

Anomaly detection in multivariate time series with Keras and LSTM layers

in this repository i will show how to build an Anomaly Detection model for Time Series data.I use LSTMs and Autoencoders in Keras and TensorFlow 2. I’ll use the model to find anomalies in S&P 500 daily closing prices.

Anomaly detection Example LSTM example with Quantile Regression Keras

•An Autoencoder Neural Network (implemented in Keras) was used in unsupervised (or semi-supervised) fashion for Anomaly Detection in credit card transaction data. •The trained model was evaluated on pre-labeled and anonymized dataset.

Real-time biological signal processing and classification framework using TensorFlow/Keras. Supports ECG, EEG, and respiratory waveform analysis with adaptive 1D CNN architecture for anomaly detection. Features signal preprocessing, feature extraction, and continuous monitoring capabilities.

Anomaly Detection with Time-Series Data in Keras with Tensorflow Backend

GAN model for anomaly detection using keras

Anomaly Detection using Natural Language Processing technique. Recurrent Neural Network #Keras #NLP #LSTM

TF 2.0 - Keras implementation of MemAE for anomaly detection. -- Gong, Dong, et al. "Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection". ICCV 2019.

Anomaly Detection in Time Series Data with Keras.

Anomaly detection for ball screw driver with keras model

Beginning Anomaly Detection Using Python-Based Deep Learning With Keras

LSTM autoencoder based anomaly detection using Keras and Tensorflow backend