Started fine, input layer, hidden layers, output layer, talked about fully connected vs not.
Start by defining an artificial neural network as a composition of layers that transform input data into predictions. Then walk through the typical layers in order—input, hidden (with activation functions), and output—explaining the role of each and how they contribute to the network's ability to learn complex patterns. Emphasize that the choice of layers and their configurations involves trade-offs between model capacity, interpretability, and computational efficiency.
Pro tip: Relate each layer to LinkedIn's use cases (e.g., feed ranking, recommendation systems) to show practical understanding and business impact. Also, mention that while deeper networks can capture more abstract features, they require more data and compute, and may suffer from vanishing gradients—demonstrating awareness of trade-offs.
Explain that the input layer receives raw features (e.g., embeddings, numerical features) and passes them to the next layer without computation. Its size equals the feature dimension.
Describe hidden layers as where the magic happens: each neuron computes a weighted sum of inputs plus bias, then applies a non-linear activation function (e.g., ReLU, sigmoid). Multiple hidden layers enable hierarchical feature learning.
Highlight that activation functions introduce non-linearity, allowing the network to approximate complex functions. Common choices: ReLU (sparse, efficient), sigmoid/tanh (for probabilities or bounded outputs), and softmax (for multi-class output).
Explain that the output layer produces the final prediction. Its activation depends on the task: sigmoid for binary classification, softmax for multi-class, linear for regression.
Discuss how depth, width, activation functions, and regularization (dropout, batch norm) affect performance, training speed, and overfitting. Mention that architecture search and empirical validation are key.
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Start by defining each activation function mathematically and its key properties (e.g., range, smoothness, computational cost). Then compare them along axes like gradient flow, computational efficiency, and empirical performance in deep networks. Finally, discuss practical selection criteria based on architecture, task, and hardware constraints.
Pro tip: Mention that SwiGLU and SiLU are often used in transformer-based models (e.g., PaLM, LLaMA) due to their smoothness and gating mechanisms, while ReLU remains a strong baseline for CNNs and resource-constrained settings. Highlight that the choice can depend on whether you're optimizing for training stability, inference speed, or final accuracy.
Briefly state the formula and key properties (e.g., range, monotonicity, smoothness, computational complexity) for ReLU, sigmoid, SwiGLU, and SiLU.
Discuss vanishing gradients (sigmoid), dying ReLU problem, and how smooth activations like SiLU and SwiGLU mitigate these issues.
Compare FLOPs, memory access, and parallelization; note that SwiGLU involves extra parameters and computation due to gating.
Explain typical use cases: ReLU for CNNs, sigmoid for binary classification output, SiLU/SwiGLU for transformers and NLP tasks.
Summarize when to choose each: ReLU for simplicity and speed, sigmoid for probabilistic outputs, SiLU for smoothness, SwiGLU for state-of-the-art performance in large language models.
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This one felt like a gift after the activation function mess.
Start by framing loss functions as the bridge between model predictions and the task's objective, then systematically cover each task type (regression, binary classification, multi-class, ranking) by discussing common choices, their properties, and trade-offs. Emphasize that the choice depends on the data distribution, business metric, and optimization considerations, and mention how LinkedIn's specific use cases (e.g., feed ranking, job matching) might influence the decision.
Pro tip: Tie the loss function to the evaluation metric and business goal—e.g., for ranking, optimizing a pairwise or listwise loss often aligns better with NDCG than pointwise losses, but consider computational cost and data sparsity. Also, mention that sometimes a custom loss or a combination (e.g., multi-task learning) is needed, showing you think beyond textbook answers.
Clarify whether the task is regression (continuous output), binary classification (two classes), multi-class classification (multiple exclusive classes), or ranking (ordered list). This determines the basic form of the loss function.
For each task, name standard losses (e.g., MSE/MAE for regression, BCE for binary, CE for multi-class, pairwise/listwise for ranking) and discuss their sensitivity to outliers, probabilistic interpretation, and optimization behavior.
Explain how the loss should correlate with the final evaluation metric (e.g., RMSE for regression, AUC for binary, accuracy/F1 for multi-class, NDCG/MAP for ranking) and the business goal (e.g., minimizing false negatives in fraud detection).
Discuss factors like class imbalance (use weighted losses or focal loss), outliers (use Huber loss), computational efficiency, and whether the loss is differentiable and easy to optimize.
Conclude with a concise rule of thumb: start with the standard loss for the task, then adjust based on metric alignment, data issues, and business needs. Mention that sometimes custom losses or multi-task objectives are warranted.
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Knew the high-level story: Adam keeps a running average of gradients and squared gradients, uses those to adapt the learning rate per parameter.
Start by defining the problem Adam solves: efficient stochastic optimization with adaptive per-parameter learning rates. Then walk through the algorithm step-by-step: first moment (mean), second moment (uncentered variance), bias correction, and the final update rule. Emphasize the intuition behind each component and why they matter.
Pro tip: Mention that bias correction is crucial in early training when the moment estimates are initialized to zero, and that Adam can be seen as a combination of momentum and RMSProp. Also note that the default hyperparameters (β1=0.9, β2=0.999, ε=1e-8) work well across many tasks.
Explain that Adam is an adaptive learning rate optimization algorithm that computes individual learning rates for different parameters from estimates of first and second moments of the gradients.
Describe the first moment estimate (m_t) as the exponentially decaying average of past gradients, analogous to momentum. Update rule: m_t = β1 * m_{t-1} + (1 - β1) * g_t.
Describe the second moment estimate (v_t) as the exponentially decaying average of past squared gradients, analogous to RMSProp. Update rule: v_t = β2 * v_{t-1} + (1 - β2) * g_t^2.
Explain that because m_t and v_t are initialized to zero, they are biased towards zero, especially during initial steps. Apply bias correction: m_hat_t = m_t / (1 - β1^t) and v_hat_t = v_t / (1 - β2^t).
Present the final parameter update: θ_t = θ_{t-1} - α * m_hat_t / (sqrt(v_hat_t) + ε), where α is the step size and ε is a small constant for numerical stability.
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