9. HR management (Curriculum)

Human Resource Management - the activities associated with recruiting,development, and compensation of employees.

9.1. .NET Curriculum

9.1.1. Documentation

Todo

  1. create a public repo on github presenting a template for sphinx projects

9.1.2. Testing

9.1.4. Software development .NET

9.1.5. DATA MODELING .NET and Azure

9.1.6. GENERATE OFFICE FILES

9.1.7. DEPLOYMENT

9.1.8. Artificial intelligence / Machine learning / Big data analysis

9.1.9. Reproducible research

9.2. Excel Curriculum

9.2.1. Lookups

  • Introduction
  • VLOOKUP
  • VLOOKUP Exact Match
  • HLOOKUP
  • HLOOKUP Exact Match

9.2.2. Conditional Logic

  • Introduction
  • IF Statement
  • Nested IF
  • AND
  • OR
  • NOT
  • IFERROR
  • SUMIF
  • AVERAGEIF
  • COUNTIF & COUNTIF
  • SUMIFS
  • AVERAGEIFS

9.2.3. Data Tools

  • Introduction
  • Data Validation
  • Drop-Down Lists
  • Removing Duplicates
  • Text To Columns
  • Goal Seek
  • Scenario Manager

9.2.4. PivotTables

  • Introduction
  • Creating PivotTables
  • Choosing Fields
  • PivotTable Layout
  • Filtering PivotTables
  • Modifying PivotTable Data
  • PivotCharts

9.2.5. Collaboration

  • Introduction
  • Document Properties
  • Inserting Hyperlinks
  • Sharing a Workbook
  • Track Changes
  • Accept/Reject Changes
  • Mark as Final

9.2.6. Printing

  • Introduction
  • Page Orientation
  • Page Breaks
  • Print Area
  • Margins
  • Print Titles
  • Headers and Footers
  • Scaling
  • Sheet Options

9.2.7. Macros

  • Introduction and Macro Security
  • Recording a Macro
  • Assign a Macro to a Button or Shape
  • Run a Macro upon Opening a Workbook
  • How to Inspect and Modify a Macro

9.3. Secure and Private Artificial Intelligence

9.3.1. Deep learning with PyTorch

9.3.1.1. Install Python3

  • create a python3.7.X environment : conda create -n py37 python=3.7 anaconda
  • activate the environment conda activate py37
  • deactivate the environment conda deactivate
  • determining my environment : conda info –envs

9.3.1.2. Install PyTorch an Conda

9.3.1.3. Launching Jupyter Notebook App

9.3.2. Udacity course : Deep Learning with PyTorch

This repo contains notebooks and related code for Udacity’s Deep Learning with PyTorch lesson. This lesson appears in our [AI Programming with Python Nanodegree program](https://www.udacity.com/course/ai-programming-python-nanodegree–nd089).

  • Part 1: Introduction to PyTorch and using tensors
  • Part 2: Building fully-connected neural networks with PyTorch
  • Part 3: How to train a fully-connected network with backpropagation on MNIST
  • Part 4: Exercise - train a neural network on Fashion-MNIST
  • Part 5: Using a trained network for making predictions and validating networks
  • Part 6: How to save and load trained models
  • Part 7: Load image data with torchvision, also data augmentation
  • Part 8: Use transfer learning to train a state-of-the-art image classifier for dogs and cats

9.3.3. Tools for Artificial Intelligence

  • Gym is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball. https://gym.openai.com
  • ONNX is an open format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners. https://onnx.ai
  • Machine learning cheatsheet : https://ml-cheatsheet.readthedocs.io