Tclack88 / Lambda

a collection of Lambda school curriculum an related projects

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Lambda

a collection of Lambda school curriculum an related projects

DS-Unit-1-Sprint-1-Dealing-With-Data

Notebooks, assignments, and sprint challenge for Data Science Unit 1 Sprint 1

tmptmp_2cxg8pN8Ap/master =======

DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling

tmptmp_K1YYRhQK9m/master =======

DS-Sprint-03-Statistical-Tests-and-Experiments

Lecture and assignment notebooks for Data Science Unit 1 Sprint 3

tmptmp_WcqWVkzkGf/master =======

DS-Unit-2-Regression-Classification

tmptmp_WxRYVzaAGg/master =======

DS-Unit-2-Kaggle-Challenge

tmptmp_FXyKv2VVcV/master =======

DS-Unit-2-Applied-Modeling

tmptmp_q0GmFuIQye/master =======

DS-Unit-3-Sprint-1-Software-Engineering

Software Engineering and Reproducible Research for Data Science

The World Beyond Notebooks

Python Notebooks are great - they let us explore data and communicate and share results. But if you want to write more general-purpose reusable code, you should put it in a package - like numpy, pandas, and the other great tools we depend on.

A full production-grade library is a large undertaking, but this week we will build our own modest but still useful package with utility functions for common data science tasks. Behold, lambdata!

See each module for specific objectives and assignments. Note that you will be making the lambdata repo yourself - it will not be a fork, and you can have more independence and "creative control** in where you take it. You should still fork and open a PR to this repo, and edit this file to link to your lambdata.

My lambdata repository: you edit here

tmptmp_XQb53aLzaN/master =======

DS-Unit-3-Sprint-2-SQL-and-Databases

SQL and Databases for Data Science

tmptmp_XcItBrdX69/master =======

DS-Unit-3-Sprint-3-Productization-and-Cloud

Building a real deployed full-stack application, backed by Data Science

Note - assignments this week are all steps in a larger week-long project. They are to be worked on in a repo you make with your own account, as instructed in the first day. You should still fork this repo, and open a PR where you add a work_notes.md file that includes a link to your project repo. You should then update work_notes.md each day with the following:

• What went well (in the context of working on the assignment) today?
• What was particularly interesting or surprising about the topic(s) today?
• What was the most challenging part of the work today, and why?

tmptmp_emNcXi5vlQ/master =======

DS-Unit-4-Sprint-1-NLP

Hello World!!

tmptmp_wBWdWHTf28/master =======

Unit 4 Sprint 3: Major Neural Network Architectures

This week we will review several popular feed-forward neural network architectures that are common in commercial applications.

• Module 1: RNNs & LSTMs
  • Objectives:
    1. Describe recurrent neural network architecture
    2. Use an LSTM to generate text based on some input
• Module 2: CNNs
  • Objectives:
    1. Describe convolutions and convolutions within neural networks
    2. Apply pre-trained CNNs to object detection problems
• Module 3: Autoencoders
  • Objectives:
    1. Describe the componenets of an autoencoder
    2. Train an autoencoder
    3. Apply an autoencoder to a basic information retreval problem
• Module 4: Artificial General Intelligence & the Future
  • Objectives:
    1. Describe the history of artificial intelligence research
    2. Know the important research achievements in AI
    3. Delineate the ethnical challenges faces AI

tmptmp_oLdzZwJd5l/master =======

AB-Demo

Simple front-end A/B experiment - view it live!

tmptmp_GySmzlCpCG/master