A/B Test Sample Size and the Peeking Problem: Interview Guide
Size an A/B test from baseline, MDE, alpha and power, turn it into days, and see by simulation why stopping at the first p below 0.05 fails.
~8 min readread →
Developer interview notes & guides — Page 1
Focused explanations and longer guides, with worked scenarios, common mistakes and interview exercises across every chapter.
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Showing 1–24 of 825 articles
Bayes' Theorem and the Base Rate Fallacy: Interview Guide
Solve the classic 1% disease test question three ways, see why rare events wreck precision, and avoid the base rate traps interviewers set.
~8 min readread →
Central Limit Theorem Explained: Data Science Interview Guide
What the central limit theorem really promises, shown by simulation on skewed revenue data, and where it quietly breaks in A/B tests.
~9 min readread →
Confidence Intervals Explained: Data Science Interview Guide
What 95% confidence really means, how to build t, Wilson and bootstrap intervals in Python, and why the textbook proportion interval fails.
~8 min readread →
Linear Regression Assumptions: What Breaks and How to Check
The OLS assumptions interviewers expect, which violations bias coefficients and which only break standard errors, with Python checks for each.
~10 min readread →
P-Values and Hypothesis Testing: Data Science Interview Guide
What a p-value actually measures, how to run a two-proportion test by hand in Python, and the misreadings that sink data science candidates.
~8 min readread →
Simpson's Paradox and Correlation vs Causation: Interview Guide
Why a variant can win in every segment yet lose overall, how to standardise for mix, and how to tell confounders from causes in product data.
~8 min readread →
T-Test vs Z-Test vs Chi-Square: Which Test to Use in Interviews
Pick the right test from the data type and design: two-proportion z-tests, Welch and paired t-tests, and chi-square, each run in Python.
~9 min readread →
SGD, Momentum, Adam and AdamW: Optimizer Interview Guide
How SGD, momentum, Adam and AdamW update weights, why AdamW decouples weight decay, and how warmup and cosine decay keep training stable.
~9 min readread →
Backpropagation Explained: The Chain Rule, Step by Step
How backpropagation applies the chain rule layer by layer, worked by hand on a tiny network with softmax cross-entropy and a gradient check.
~9 min readread →
Batch Norm vs Layer Norm: Deep Learning Interview Question
How batch norm and layer norm compute their statistics, why model.eval() matters, why small batches hurt, and why transformers use LayerNorm.
~9 min readread →
CNN Interview Guide: Convolution, Pooling and Receptive Field
How a conv layer slides a kernel, the output size and parameter formulas, pooling, and how receptive field grows, with runnable Python.
~10 min readread →
Dropout and Overfitting in Deep Neural Networks Explained
How to spot overfitting in loss curves, why inverted dropout scales by 1/(1-p), and how weight decay, augmentation and early stopping help.
~9 min readread →
RNN vs LSTM vs GRU: How Gated Recurrent Networks Work
How RNNs, LSTMs and GRUs process sequences, why gradients vanish through time, how gates fix it, and how to count each model's parameters.
~9 min readread →
Self-Attention and Transformers Explained Step by Step
Compute softmax(QK^T/sqrt(d_k))V by hand, see why the scaling matters, then add causal masks, multiple heads and a KV cache memory budget.
~10 min readread →
Vanishing and Exploding Gradients: Causes, Init and Fixes
Why gradients vanish or explode in deep networks, measured layer by layer, and how He and Xavier init, residuals and clipping fix it.
~9 min readread →
Go Buffered vs Unbuffered Channels: Interview Guide
When a Go channel send blocks, what buffering changes, who closes a channel, and how select, nil channels and timeouts fit together.
~8 min readread →
Go Context: Cancellation, Timeouts and Deadlines
How context cancellation propagates through a Go call tree, why cancel must always be called, and how to stop goroutines on a deadline.
~7 min readread →
Go Error Wrapping: %w, errors.Is and errors.As
How fmt.Errorf with %w builds an error chain in Go, when to use errors.Is versus errors.As, and how to design sentinel and typed errors.
~7 min readread →
Goroutines vs OS Threads: How the Go Scheduler Works
How goroutines differ from OS threads, how the GMP scheduler maps them onto CPUs, and what GOMAXPROCS, preemption and syscalls change.
~8 min readread →
Go Data Races: Mutex, WaitGroup and the Race Detector
What counts as a data race in Go, how sync.Mutex and WaitGroup fix it, and how go test -race finds the race before production does.
~7 min readread →
Go Interfaces and the Nil Error That Is Not Nil
Why a nil pointer returned as an error makes err != nil true in Go, how interface values store type and value, and how to avoid it.
~7 min readread →
Go Slices: len, cap, append and Shared Backing Arrays
How a Go slice header works, when append reallocates, why two slices can overwrite each other, and how to copy safely with Clone.
~7 min readread →
Go Worker Pools and Goroutine Leaks Explained
How to bound concurrency in Go with a worker pool or errgroup, why early returns leak goroutines, and how to find leaks with pprof.
~7 min readread →
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