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Deep Learning

70 questions / प्रश्न · 8 sections

Deep Learning Fundamentals Q1–12

  1. What is Deep Learning?Deep Learning क्या है?
  2. What is the difference between Machine Learning and Deep Learning?Machine Learning और Deep Learning में क्या अंतर है?
  3. What is Neural Network?Neural Network क्या होता है?
  4. What is Artificial Neural Network (ANN)?Artificial Neural Network (ANN) क्या है?
  5. What are the main components of Neural Network?Neural Network के basic components क्या हैं?
  6. What is Neuron?Neuron क्या होता है?
  7. What are weights and biases?Weights और Bias क्या होते हैं?
  8. What are the input, hidden, and output layers?Input layer, Hidden layer और Output layer क्या हैं?
  9. Why is Deep Neural Network "Deep" kehte important?Deep Neural Network को "Deep" क्यों kehte हैं?
  10. What is Forward Propagation?Forward Propagation क्या है?
  11. What is the difference between Training and Inference?Training और Inference में क्या अंतर है?
  12. Describe Neural Network training ka complete workflow.Neural Network training का complete workflow समझाएँ.

Activation Functions Q13–20

  1. What is Activation Function?Activation Function क्या है?
  2. Why is Activation function needed?Activation function की ज़रूरत क्यों होती है?
  3. What is Sigmoid function?Sigmoid function क्या है?
  4. Explain Sigmoid advantages disadvantages.Sigmoid के advantages और disadvantages?
  5. What is Tanh?Tanh क्या है?
  6. What is ReLU?ReLU क्या है?
  7. How do ReLU and Sigmoid vs Tanh differ?ReLU vs Sigmoid vs Tanh?
  8. Explain Leaky ReLU kab karenge.Leaky ReLU क्या है और कब उपयोग karenge?

Loss Functions Q21–27

  1. What is Loss Function?Loss Function क्या होता है?
  2. What is the difference between Loss and Cost Function?Loss और Cost Function में क्या अंतर है?
  3. What is MSE?MSE क्या है?
  4. What is Binary Cross Entropy?Binary Cross Entropy क्या है?
  5. What is Categorical Cross Entropy?Categorical Cross Entropy क्या है?
  6. How would you approach Regression Classification loss function choose?Regression और Classification में loss function कैसे choose करेंगे?
  7. Explain Wrong loss function choose problem sakti.Wrong loss function choose करने से क्या problem हो सकती है?

Backpropagation & Gradient Descent Q28–38

  1. What is Backpropagation?Backpropagation क्या है?
  2. Describe Backpropagation ka complete process.Backpropagation का complete process समझाएँ.
  3. What is Gradient?Gradient क्या होता है?
  4. What is Gradient Descent?Gradient Descent क्या है?
  5. What is Learning Rate?Learning Rate क्या है?
  6. Explain Learning rate bahut high.Learning rate बहुत high हो तो क्या होगा?
  7. Explain Learning rate bahut low.Learning rate बहुत low हो तो क्या होगा?
  8. What is Vanishing Gradient Problem?Vanishing Gradient Problem क्या है?
  9. What is Exploding Gradient Problem?Exploding Gradient Problem क्या है?
  10. How would you approach Neural network weights update?Neural network weights update कैसे होते हैं?
  11. What is the difference between Backpropagation and Gradient Descent?Backpropagation और Gradient Descent में क्या अंतर है?

Optimizers Q39–45

  1. What is Optimizer?Optimizer क्या है?
  2. What is SGD?SGD क्या है?
  3. What is the difference between SGD and Batch Gradient Descent?SGD और Batch Gradient Descent में क्या अंतर है?
  4. What is Momentum?Momentum क्या है?
  5. What is Adam Optimizer?Adam Optimizer क्या है?
  6. How do Adam and SGD differ?Adam vs SGD?
  7. What is Learning-rate scheduling?Learning-rate scheduling क्या है?

Training Concepts Q46–53

  1. What is Epoch?Epoch क्या है?
  2. What is Batch?Batch क्या है?
  3. What is Iteration?Iteration क्या है?
  4. What is Batch Size ka effect?Batch Size का effect क्या होता है?
  5. What is the difference between Epoch, Batch and Iteration?Epoch, Batch और Iteration में क्या अंतर है?
  6. How would you approach Training/Validation loss interpret?Training/Validation loss को कैसे interpret करेंगे?
  7. What is Early Stopping?Early Stopping क्या है?
  8. Explain Neural Network training unstable investigate.Neural Network training unstable हो तो क्या investigate करेंगे?

Overfitting & Regularization Q54–61

  1. What is Neural Network mein overfitting?Neural Network में overfitting क्या है?
  2. What is Underfitting?Underfitting क्या है?
  3. Why is Deep Learning model overfit important?Deep Learning model overfit क्यों करता है?
  4. What is Dropout?Dropout क्या है?
  5. What is Batch Normalization?Batch Normalization क्या है?
  6. What is L1/L2 Regularization?L1/L2 Regularization क्या है?
  7. What is Data Augmentation?Data Augmentation क्या है?
  8. How would you approach Deep Learning model overfitting bachayenge?Deep Learning model को overfitting से कैसे bachayenge?

CNN Q62–70

  1. What is CNN?CNN क्या है?
  2. Why is CNN images effective important?CNN images के लिए effective क्यों है?
  3. What is Convolution operation?Convolution operation क्या है?
  4. What is Kernel/Filter?Kernel/Filter क्या होता है?
  5. What is Stride?Stride क्या है?
  6. What is Padding?Padding क्या है?
  7. What is Pooling?Pooling क्या है?
  8. How do Max Pooling and Average Pooling differ?Max Pooling vs Average Pooling?
  9. Describe Complete CNN architecture.Complete CNN architecture समझाएँ.