Frequently Asked Questions About Simplilearn AI & Deep Learning Builder Skill
22 answers covering everything from basics to advanced usage.
// Basics
What are the four data types I need to classify first?
Nominal (labels with no measurable value, treated as true/false flags), Ordinal (categorical with an order or scale, bucketed into ranges), Discrete (finite countable integers), and Continuous (any numerical value in a range, treated as float). This classification is step one and determines exactly how you feed each feature into your model.
What is the data economy and why does it drive AI?
The data economy refers to exponentially growing data volumes — roughly 44x growth since 2009 as a reference point — creating competitive pressure to extract value from data. This flood of data is what drove the emergence of AI. In practice, more data and more complexity push your solution toward deep learning rather than simpler methods.
What is a neural network made of?
A neural network consists of an input layer, one or more hidden layers, and an output layer, modeled on the human brain. Data passes between neurons over weighted channels. Each neuron computes a weighted sum of its inputs plus a bias, then applies an activation function that determines whether and how strongly the neuron fires.
What is a bias in a neuron, and how is it different from model bias?
A neuron bias is a unique constant added to the weighted sum, analogous to the y-intercept (+c) in y = mx + c, which shifts the activation threshold. Model bias (overfitting) is completely different — it means the model has become biased to its training data and fails to generalize. Don't confuse the two.
// How To
How do I check for class imbalance before training?
Run value_counts() on your label column before training. If 90% of labels are one class, a model predicting that class always will hit 90% accuracy while being useless. Checking label distribution reveals imbalance and null values early, so you can address them before wasting compute on a misleading baseline.
How do I set up training versus evaluation in the input function?
For training, set shuffle=True and num_epochs=None so the model sees varied data ordering across many passes. For evaluation and prediction, set shuffle=False and num_epochs=1 so each test record is seen exactly once in order. Using training settings during evaluation produces misleading accuracy numbers — a common and costly mistake.
How do I convert string labels into a usable target variable?
Convert label columns to binary 0/1 wherever possible, because machines handle 0/1 more cleanly than string labels. Watch for trailing punctuation — if test data has labels with a period at the end, failing to account for it causes label-matching failures and incorrect binary encoding of the target variable.
How do I generate and interpret individual predictions?
Call model.predict() with a get_input_function set to num_epochs=1, batch_size=128. For each record the output includes class_ids (predicted label), probabilities (confidence per class), and logits (raw scores). Match predicted labels against actual labels to verify the model produces meaningful individual-level outputs, not just aggregate accuracy that could mask problems.
// Troubleshooting
My test accuracy is far below my training accuracy — what do I do?
That gap is overfitting: the model memorized training examples and can't generalize. Stop training or reduce the number of epochs. Also verify you split train and test sets separately, check for data leakage, and consider whether you have enough data. The goal is a model that generalizes to unseen data, not one that fits the answers.
Both my training and test accuracy sit at the baseline — what's wrong?
If both accuracies hover near the baseline, your model isn't learning useful patterns — it needs better features. Run a correlation heatmap or sklearn correlation matrix to find features most predictive of the label, then engineer new ones: square features with non-linear relationships, bucket ordinal features, or add interaction terms between related variables.
Why do my neural network computations give unexpected results?
You may be confusing matrix multiplication (dot product, used for forward passes and solving equations) with element-wise multiplication (finding the product). They produce entirely different results in neural network computations. Confirm which operation each step requires — forward passes through weighted layers use the dot product, not element-wise multiplication.
My accuracy keeps changing between runs — is that improvement?
No. Running the model multiple times and picking the best result without changing anything is bad data science that produces an artificially optimistic performance estimate. Change one variable at a time, re-run steps 5 through 9, and compare accuracy against your recorded baseline. Only a change you made — not random variance — counts as real improvement.
// Comparisons
How does traditional programming differ from the machine learning approach?
In traditional programming you hard-code decision rules, evaluate, and iterate the rules manually. In the machine learning approach you never hard-code rules — you train a model on data to learn the input-output relationship, evaluate against test data, and retrain if unsatisfied. Hard-coding rules when you have sufficient labeled data is the trap ML is designed to avoid.
When should I use a LinearClassifier versus a DNNClassifier?
Use a LinearClassifier for structured data with relationships that are largely linear or where interpretability matters — it's a fast, strong baseline. Use a DNNClassifier when relationships are complex and non-linear, or when a linear model plateaus below your target accuracy. Following Build to Fail First, start with the linear model, record the baseline, then try a deeper network.
How does supervised learning compare to unsupervised learning here?
Supervised learning trains on labeled input-output pairs to learn a mapping — used for classification and regression when you have a known target variable. Unsupervised learning finds patterns, groupings, or structure without labeled outputs — used for clustering when you have no labels. Your target variable type determines which family of ML techniques you select in step three.
Is deep learning always better than machine learning?
No. Deep learning excels on unstructured data (images, speech, raw text) and very large datasets where feature engineering is hard. But for moderate, well-structured data, a LinearClassifier or small DNN can match or beat a deep network with far less compute and greater interpretability. Choose based on data structure, volume, and problem complexity — not hype.
// Advanced
How do I decide the number of hidden layers and neurons?
The number of hidden layers and neurons per layer is a hyperparameter you tune, not a fixed value. Start simple, establish a baseline, then increase depth or width if accuracy is insufficient and you aren't overfitting. Change one architectural variable at a time and compare against your recorded benchmark so you can attribute any gain to that change.
What is the right way to do feature engineering in this workflow?
Run a correlation heatmap or sklearn correlation matrix first to identify features most correlated with the label. Then tweak one variable at a time: square a feature with an up-then-down relationship (like age and income), bucket ordinal features into ranges, or add interaction terms. Re-run steps 5 through 9 each iteration and compare accuracy against your baseline.
What role does batch size play in training?
Batch size is the number of training samples processed together before one round of back propagation runs — 128 is a common default. Larger batches give smoother but slower gradient updates; smaller batches update more frequently with noisier gradients. It interacts with steps and epochs, so tune it as part of hyperparameter experiments, one change at a time.
What is a tensor and why does TensorFlow use them?
A tensor is a vector or matrix of n dimensions — 1D (vector), 2D (matrix), 3D, or higher — and it's the fundamental data structure in TensorFlow. All TensorFlow computations involve tensors. TensorFlow is Google's open-source deep learning platform, developed in C++ and implemented in Python, built to run these tensor computations efficiently at scale.
How many training steps should I start with?
Start with 1,000 steps. Steps control how many batches of back propagation run during training. After training, evaluate against your test set and record the accuracy as your comparison point. Increase steps only while monitoring the gap between training and test accuracy — if test accuracy starts diverging downward, you're overfitting and should stop.
Do I still need correlation analysis if I use deep learning?
It still helps. Deep learning can work by dumping in all features, but blindly doing so wastes computation and can introduce noise. Running a correlation or heatmap analysis first lets you understand which features matter, prune noise, and diagnose results faster. Skipping this step is a pitfall even when the model would technically still train.