Hobisist need to pay attention!

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Hobisist out there. move whatevere you feel bliss at!
Location: Mumbai, Maharashtra, India

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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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k nearest neighbors with well defined k value

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 so let us understand how we can chooose the perfect k for our model  from the last model i had prepared a function def regression(model):     x_train,x_test,y_train,y_test= train_test_split(x,y,test_size=0.2)     reg_all=model     reg_all.fit(x_train,y_train)     y_predict=reg_all.predict(x_test)     rmse_value=np.sqrt(mean_squared_error(y_test,y_predict))     print("rms error={}".format(rmse_value)) i have prepared cross value squared to get the mean of rmse where k=3 denote the mean of three iterated value of rmse Lasso is a way to conterect over fitting (we can also use ridge) to check. from sklearn.model_selection import cross_val_score from sklearn.linear_model import Lasso def regression_cv(model,k=3):     scores=cross_val_score(model,x,y,scoring='neg_mean_squared_error',cv=k)     rmse=np.sqrt(-scores)     print('reg rmse:',rmse)     print('reg mean:',rmse.mean()) importi...
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using your age and duration of workout finding how much calories you burned!

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