Beginning Anomaly Detection Using Python-Based Deep Learning

Chapter 1:  What is Anomaly Detection?Chapter Goal: Introduce reader to the task of anomaly detection, where it's used, why it's important, as well as the different types of "anomaly detection" there are. No of pages: 30Sub -Topics1. What is an anomaly2. Use cases today3. Different types of anomalies

Chapter 2:  Traditional Methods of Anomaly DetectionChapter Goal: Introduce reader to a couple high performing traditional methods of anomaly detection in Scikit-Learn. Evaluation metrics are performed on both (will be set as the benchmark of comparison for the deep learning models later on)No of pages: 50Sub - Topics1.   Isolation Forest2.   One class support vector machine3.   Mahalanobis distance based anomaly detection  
Chapter 3: Intro to Keras and PyTorchChapter Goal: Introduce reader to deep learning and how to build, train a basic model in both Keras and in PyTorch. Additionally, perform evaluation metrics on both. Also, discuss the various deep learning models that can be applied to semi-supervised and unsupervised anomaly detection.No of pages : 40Sub - Topics:  1.What is deep learning?2. Intro to Keras: simple classifier model3. Intro to PyTorch: simple classifier model4. How can we apply deep learning to anomaly detection? 
Chapter 4: AutoencodersChapter Goal: Introduce reader to several autoencoders and how they can perform anomaly detection in both semi-supervised and unsupervised anomaly detection.No of pages: 40Sub - Topics: 1. What are autoencoders?2. Basic autoencoder3. Denoising autoencoder4. Variational autoencoder5. Summary of autoencoders as a model
Chapter 5: Boltzmann MachinesChapter Goal: Introduce reader to a restricted Boltzmann machine, deep Boltzmann machine, and a deep belief network.No of pages: 30Sub - Topics: 1. What is a Boltzmann machine?2. RBM3. DBM4. DBN5. Summary of the models
Chapter 6: Time-Series Anomaly DetectionChapter Goal: Introduce reader

Sridhar Alla is the co-founder and CTO of Bluewhale, which helps organizations big and small in building AI-driven big data solutions and analytics. He is a published author of books and an avid presenter at numerous Strata, Hadoop World, Spark Summit, and other conferences. He also has several patents filed with the US PTO on large-scale computing and distributed systems. He has extensive hands-on experience in several technologies, including Spark, Flink, Hadoop, AWS, Azure, Tensorflow, Cassandra, and others. He spoke on Anomaly Detection Using Deep Learning at Strata SFO in March 2019 and will also present at Strata London in October 2019. He was born in Hyderabad, India and now lives in New Jersey, USA with his wife Rosie and daughter Evelyn. When he is not busy writing code, he loves to spend time with his family and also training, coaching, and organizing meetups.

Suman Kalyan Adari is an undergraduate student pursuing a BS degree in Computer Science at the University of Florida. He has been conducting deep learning research in the field of cybersecurity since his freshman year, and has presented at the IEEE Dependable Systems and Networks workshop on Dependable and Secure Machine Learning held in Portland, Oregon, USA in June 2019. He is quite passionate about deep learning, and specializes in its practical uses in various fields such as video processing, image recognition, anomaly detection, targeted adversarial attacks, and more.