Plan of Study (Fall 2019) @ Virginia Tech DLRL

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Plan of Study (Fall 2019) @ Virginia Tech DLRL

PublishedSeptember 14, 2019

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The perfect storm of AI/ML, Data Science, DevOps, Infrastructure, Agile/Lean Product Management all for the purpose of delivering enterprise intelligence solutions and operational value to clients. Today, I'm doing this for Defense and Intelligence clients through Harness.

When I'm not working or studying I'm likely doing design projects, spending time at the gym, reading/discussing anthropology, or mentoring others who are pursuing non-traditional entries into a technology-driven career.

Opinions expressed here are my own.

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September Paper Reading:Get Familiar With:October Goals & Implementation:Goals & Implementation November Paper Reading December

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N

Nic Acton

15 posts

Guru of Grok; Lover of Lifts; Peloton Pedaler; Constantly Consuming Coffee; Crafter of Code, Containers, and Clouds! Absolutely adore alliteration

September

Paper Reading:

Q-Learning
Recurrent Neural Networks

Get Familiar With:

Markov Models (as well as Hidden Variants)
Neural Networks
Cloud Machine Learning Pipelines (likely AWS)

October

Paper Reading:

Additional papers on Recurrent Neural Networks as advised by Dr. Xuan.

Goals & Implementation:

Jupyter/Colaboratory Notebooks
- Basics of Tensorflow
- Introduction to Markov Models

Goals & Implementation

Whitepaper: Machine Learning Pipelines on AWS
- Nominal demonstration of a Deep Learning Pipeline from Data Collection to usable Intelligence.

November

Paper Reading

Additional papers on Recurrent Neural Networks as advised by Dr. Xuan, with more of a frame on applications

Goals & Implementation

Whitepaper: Machine Learning Data Engineering for Biology
- A Systems-level approach on identifying and tackling a Biology problem with Machine Learning
- Discussion points on HIPAA compliance for Bio-companies and universities when operating in this space.

December

Goals & Implementation

Nominal Implementation of the Biology Machine Learning Framework
- Approaches and considerations to gathering Data
- Identifying the problem and how to determine what ML methodologies to tackle them
- How to make your models scalable, available, and secure to stakeholders

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My opinions are my own.

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