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2 Core
2 Core
- Data Structures & Algorithms
  Data Structures & Algorithms
  - 01 Algorithms
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    00 Intro
    
    01
    
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    09
- Digital Design
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  - Practicals
    
    Practicals
    
    01
    
    02
    
    03
    
    04
    
    05
    
    06
    
    07
    
    08
- Object Oriented Programming
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3 Core
3 Core
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    00 Setup
    
    01
    
    02
    
    07 Procedures
    
    08 Procedures 2
    
    09 Floating Point
- Computer Networks
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CS Electives
CS Electives
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- Machine Learning
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- Sports Analytics
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  - 01 Foundations
    01 Foundations
    
    Course Information
    
    Pythagorean Expectation
    
    Why Python?
    
    03
    
    Linear Regression
    
    2 Types of regression analyses
    
    Hot Hand
    
    Nb
    Nb
    
    English Premier League (EPL) Pythagorean Predictor
    
    02.01
    
    02.02
    
    02.03
    
    Visualizing sports data: basketball
    
    Visualizing Sports Data
    
    Visualizing sports data: baseball
    
    04.01
    
    Week 4.1 - Introduction to Regression Analysis
    
    Regression Analyses with Cricket Data
    
    The Salary-Performance Relationship in the English Premier League
    
    Salaries and Performance in the National Hockey League (NHL)
    
    Salaries and Performance in Major League Baseball
    
    Explaining Relationships Using Regression Analysis
    
    06.01
    
    Using Summary Statistics to Examine the "Hot Hand"
    
    06.03
    
    06.04
- Tiny ML
  Tiny ML
Finance Electives
Finance Electives
- Accounts
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  - 01 Introduction
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    Current Maturity
    
    Insolvency
- Behavioral Economics
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  - 01 Introduction
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HSS Electives
HSS Electives
- IDS
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- International Relations
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  - 01 Intro
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  - 04 Theories of IR
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  - 06 Softpower
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  - 09 Class Presentations
  - Project
    Project
    
    Report on 'The Uncontrolled Rise of China'
    
    Presentation
Math Electives
Math Electives
- Numerical Analysis
  Numerical Analysis
- Optimization
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Mech
Mech
- FEA
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Misc
Misc
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School
School
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- Linux Fundamentals
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- Math
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  - 01 Trignometric
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Tools
Tools
- AI & Data
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    Facebook Prophet
  - IBIS
    
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  - Numpy
    
    Numpy
    
    01
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    Pandas
    
    01
    
    02
  - PyTorch
    
    PyTorch
    
    PyTorch
    
    Basic Model
    
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    Advanced
    
    Performance Optimization
  - Scipy
    
    Scipy
    
    01
    
    Regression
  - Sklearn
    
    Sklearn
    
    01 Performance Optimization
  - Streamlit
    
    Streamlit
  - Sympy
    
    Sympy
  - Tensorflow
    
    Tensorflow
    
    01
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    Httpx
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    Pywav
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    FastAPI
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    Flask
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    Robyn
- Apps
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- Data Visualization
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    Matplotlib
    
    01
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    Networkx
    
    Networkx
    
    PyVis
    
    PyVis
  - Plotly
    
    Plotly
    
    01
- Databases
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    DuckDB
- DevOps
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  - CML
    
    CML
  - Git
    
    Git
    
    Basics
    
    CI/CD
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    Unit Testing
- Documentation
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    Latex
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    Mermaidjs
- FEA
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    Abaqus
    
    Introduction
    
    Python Scripting
    
    03 IDK
- Google Sheets
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- IoT
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    Arduino
    
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- NLP
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- PowerPlatform
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    PowerApps
    
    Basics
    
    01 Getting Data
    
    02 Displaying Data
    
    Forms
    
    Patching
    
    Performance
    
    Miscellaneous
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    PowerAutomate
    
    01
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    01
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    Creating Packages
- Video
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    OBS Studio
    
    01
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    Ffmpeg
- Web Dev
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    HTML
  - MERN
    
    MERN
    
    MongoDB
    
    MongoDB
    
    Node
    
    Node
    
    Node Express
    
    Node Express
    
    React
    
    React
    
    Million

Table of contents

References

Tensorflow¶

References¶

Learn TensorFlow and Deep Learning (beginner friendly code) | Daniel Bourke

Last Updated: 2024-05-12 ; Contributors: AhmedThahir

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