🔅 OpenCV for Python Developers

🌐 Author: Patrick W. Crawford
🔰 Level: Intermediate

⏰ Duration: 3h 5m

🌀 Learn how to harness the image-processing power of OpenCV to develop Python scripts that manipulate photos, create custom video streams, and even perform object and face tracking.

📗 Topics: #ComputerVision, #Python, #OpenCV

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🔅 001 Plan of Attack

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🔅 002 What are convolutional neural networks

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🔅 003 Step 1 - Convolution Operation

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🔅 004 Step 1(b) - ReLU Layer

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🔅 005 Step 2 - Pooling

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🔅 006 Step 3 - Flattening

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🔅 007 Step 4 - Full Connection

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🔅 008 Summary

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🔅 009 Softmax & Cross-Entropy

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📦 Subtitles

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What is Convolutional Neural Network?

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⌨️ ⌨️ VS Code for Python Developers

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People choose VS Code because it's clean and simple. But that simplicity belies a massive amount of functionality that is out there to discover. That's the goal of this course, to take you from simply using VS Code to really leveraging its true potential.

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Intro to customize the #Django admin dashboard

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The Best Python Cheat Sheet

Unlock Python mastery with *The Best Python Cheat Sheet* (2024 Edition)! Perfect for coders and data scientists, this comprehensive guide covers Python 3.8+ syntax, built-in functions, flow control, lists, dictionaries, generators, decorators, regex, OOP, error handling, and more. Includes ready-to-use code snippets, operator precedence rules, context managers, match-case patterns, and advanced topics like scope management and execution environments. Ideal for quick reference, interviews, or daily coding tasks. Download now to boost your Python skills!

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#PythonCheatSheet #PythonProgramming #DataScience #CodingTips #Python3 #LearnPython #ProgrammingGuide #PythonSyntax #CodeSnippets #DataStructures #OOP #Regex #ErrorHandling #PythonLibraries #CodingReference #PythonTricks #TechResources #DeveloperTools #PythonForBeginners #AdvancedPython

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📶 Deep Learning: Model Optimization and Tuning

🌐 Author: Kumaran Ponnambalam
🔰 Level: Advanced

⏰ Duration: 54m

🌀 Learn about various optimization and tuning options available for deep learning models and use them to improve models.

📗 Topics: Deep Learning, Machine Learning

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