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

[5 FPS - 150 FPS] Learning Deep Features for One-Class Classification (AnomalyDetection). Corresponds RaspberryPi3. Convert to Tensorflow, ONNX, Caffe, PyTorch. Implementation by Python + OpenVINO/Tensorflow Lite.

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

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Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.

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.

Detect Anomalies with Autoencoders in Time Series data

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

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•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