“Valerie has always made insightful comments on different programming projects.”
San Diego, California, United States
Contact Info
2K followers
500+ connections
About
Experience & Education
Licenses & Certifications
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Intuit QuickBooks
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Issued -
Volunteer Experience
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Volunteer
League of Women Voters of san diego
- 2 years 4 months
Politics
Secretary, helped with Voter Registration
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Volunteer
Amercian Cancer Society's Discovery Shop
- 15 years 4 months
Health
Helped take in donations, sort them, steam and hang up clothes.
Projects
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https://github.com/valchau/
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https://github.com/valchau/WeatherPY using Jypter Notebooks and Python
https://github.com/valchau/Election_Analysis Python
https://github.com/valchau/Cryptocurrencies Python and machine learning -
https://github.com/valchau/kickstarter-analysis
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This project provides Louise with information about how different campaigns fared in relation to their launch dates and their funding goals. Using the Kickstarter dataset that Louise and others provided, this project helps Louise visualize Kickstarter campaign outcomes based on their launch dates and their funding goals. This report can be provided to Louise to help her decide when to lauch a funding campaign for her new play and what an appropriate fundraising goal should be.
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https://github.com/valchau/Neural_Network_Charity_Analysis
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The purpose of this project is to learn about and use deep learning neural networks using TensorFlow in Python to analyze and classify the success of charitable donations. The first two parts of this task used specific steps and defined model. The third part of the task required thought to improve the given neural network model to optimze it. I wasn't able to get 75% accuracy but I did try several attempts to improve the model's accuracy by adding hidden layers, changing their units and…
The purpose of this project is to learn about and use deep learning neural networks using TensorFlow in Python to analyze and classify the success of charitable donations. The first two parts of this task used specific steps and defined model. The third part of the task required thought to improve the given neural network model to optimze it. I wasn't able to get 75% accuracy but I did try several attempts to improve the model's accuracy by adding hidden layers, changing their units and activation functions. Some of my attempts actually made the accuracy WORSE so after that, I just did my best to get as close to 75% accuracy as I could in the optimization task.
I learned the background and history of computational neurons as well as current implementations of neural networks as they apply to deep learning. I also learned the major costs and benefits of different neural networks and learned how to compare these costs to traditional machine learning classification and regression models. Additionally, I learned about implementing neural networks and deep neural networks across a number of different datasets, including image, natural language, and numerical datasets. Finally, I learned how to store and retrieve trained neural network models to optimize the results. This was all new to me.
Steps
get colab to find input data file
preprocessing the data for the neural network model,
compile, train and evaluate the model,
optimize the model.
display results visually and tables
get colab to write output files to my PC
Honors & Awards
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Microsoft Office Word 2013 certification
Microsoft
Earned Word Certification
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Microsoft Office Excel 2013 Certification
Microsoft
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QuickBooks Pro/Premier 2014 Certified User
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IT Fundamentals certified
CompTIA
Languages
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C, C++, Python, Java, HTML 5, CSS, JavaScript,
Professional working proficiency
Organizations
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Mensa, IEEE, ACM
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Recommendations received
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