Pinned Repositories
086761
Vision Aided Navigation coursework
100DaysOfMlCode
Fork this template for the 100 days journal - to keep yourself accountable #100DaysOfMlCode
100Mldays
3dv_tutorial
An Invitation to 3D Vision: A Tutorial for Everyone
ad_examples
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
anomalib
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
bayesian-neural-network-blogpost
Building a Bayesian deep learning classifier
bookdown
Authoring Books and Technical Documents with R Markdown
Machine-Learning-for-Cyber-Security
Curated list of tools and resources related to the use of machine learning for cyber security
ortalby's Repositories
ortalby/Machine-Learning-for-Cyber-Security
Curated list of tools and resources related to the use of machine learning for cyber security
ortalby/086761
Vision Aided Navigation coursework
ortalby/100Mldays
ortalby/ad_examples
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
ortalby/bookdown
Authoring Books and Technical Documents with R Markdown
ortalby/Cartoonizer-with-TFLite
How to create a Cartoonizer Android app with TensorFlow Lite models.
ortalby/Data-science-best-resources
Carefully curated resource links for data science in one place
ortalby/deep-learning-drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
ortalby/deep_learning_object_detection
A paper list of object detection using deep learning.
ortalby/deep_sort_yolov3
Real-time Multi-person tracker using YOLO v3 and deep_sort with tensorflow
ortalby/deepbayes-2019
Practical assignments of the Deep|Bayes summer school 2019
ortalby/elistimator
Alternative implementation of TensorFlow estimator for ease-of-use
ortalby/Google-Machine-Learning-Course-Notes
Notes taken from Google Machine Learning Course provided to public for practice & correction.
ortalby/HardNet-Tensorflow
ortalby/jasper
Official Repository for the JasPer Image Coding Toolkit
ortalby/Lora
Lora signal decoding
ortalby/LSTM-GRU-BiLSTM-in-TensorFlow-for-predictive-analytics
ortalby/MAFAT_4net
A CNN for classifying radar spectrogram subjects as human or non-human for Israel's Ministry of Defense MAFAT challenge.
ortalby/Multivariate-Time-Series-Using-LSTM
ortalby/patch2image
Training FCNNs from patches to full-sized images. A framework to train arbitrarily designed networks for medical image segmentation.
ortalby/practicalAI
A practical approach to learning machine learning.
ortalby/pyod
A Python Toolkit for Scalable Outlier Detection (Anomaly Detection)
ortalby/pytorch-handbook
pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行
ortalby/pytorch-pretrained-BigGAN
🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.
ortalby/resources
PyMC3 educational resources
ortalby/rethinking
Statistical Rethinking course and book package
ortalby/retina-unet
Retina blood vessel segmentation with a convolutional neural network
ortalby/Stanford-Project-Predicting-stock-prices-using-a-LSTM-Network
Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).
ortalby/statrethinking_winter2019
Statistical Rethinking course at MPI-EVA from Dec 2018 through Feb 2019
ortalby/telemanom
A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.