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  • Arduino
  • Machine Learning & AI
  • Robotic Process Automation
  • Internet of thing
  • Rubic cube solving
  • Electrical Engineering
  • Content  Creating

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Pcb Fault Detection(Deep Learning Technique)

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  Hello,  that's how I applied my deep Learning skills to my electronics industry  application Technical except After iterating through many models we find this model to be giving good accuracy and least loss. we used  #yolo  v5 with 4000 epoch. here us the latest update to the dataset. I have gathered data related to pcb fault detection. I have gathered a huge amount of image data. and I have open sourced it via-Kaggle. feel free to explore it.
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environment creation on python

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  without conda : D:\Anney\environment>python -m venv magic_venv D:\Anney\environment>.\magic_venv\Scripts\activate (magic_venv) D:\Anney\environment> environment  conda: $ conda create --name [Virtual Environment Name] python=[Version you want to install] Example, $ conda create --name sample_venv python= 3.8  
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rock paper scisor game

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for the exact code: https://github.com/animeesh/game-rock-paper-scissor code  random lib is used to get the random value  import random #input_list = ["Rock", "Paper", "Scissor"] while True:     input_user = input("Select the input: ")     print (input_user)      possible_action=["rock","paper","scissor"]     computer_action=random.choice(possible_action)          print(f"\nyou chose :{input_user},computer chose :{computer_action}.\n")     #hear starts the real technique     if input_user ==computer_action:         print(f"both player selected {input_user}.hence its a tie!")     elif input_user== "rock":         if computer_action=="scissor":             print("rock smashes scissor! you win")         else:             print("paper covers the rock! you lose") ...
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