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Nail Your Data Science Interview: Day 5 — Neural Networks Fundamentals
- Neural networks consist of interconnected nodes organized in layers, including hidden layers, output layer, weights, biases, and activation functions.
- Activation functions like Sigmoid, Tanh, ReLU, Leaky ReLU, ELU, and Softmax introduce non-linearity in neural networks.
- Backpropagation is crucial for neural networks to learn efficiently by calculating gradients and updating parameters based on errors.
- The vanishing/exploding gradient problem in deep networks can be addressed through techniques like weight initialization, batch normalization, and LSTM/GRU.
- CNNs, RNNs, and Transformers are specialized architectures for different data types such as images, sequential data, and text data, each suited to specific tasks.
- Neural network optimizers like SGD, Adam, RMSprop, and AdaGrad adjust parameters to minimize loss, each with trade-offs in convergence and generalization.
- Proper weight initialization is essential to prevent vanishing/exploding gradients and optimize network training using strategies like Xavier, He, and LSUV.
- Batch normalization normalizes layer inputs, reducing internal covariate shift, improving training speed, and aiding convergence in deep networks.
- Combatting overfitting in neural networks involves data augmentation, dropout, early stopping, and regularization techniques to improve generalization.
- Embeddings in neural networks are low-dimensional vector representations of categorical variables that capture semantic relationships and facilitate transferable knowledge.
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