What is a Neural Network?

This cover signals a deliberate gear change: the previous post explained the machinery, this one puts it under the reader's fingers. The promise is a complete, copy-paste-able PyTorch pipeline — define, train, evaluate — which is exactly what the 'code example' angle demands.

Using a simple tabular classification setup with 20 features and 2 classes is a conscious choice. It keeps the network small enough to read in full, lets the training loop run fast, and focuses attention on the mechanics rather than a complex dataset. The same pattern scales directly to images or text by swapping the layers, which the post notes so readers see it as a template.

The install-and-import slide front-loads every dependency so the rest of the post runs without surprises. It also previews the post's structure through its imports: nn for layers, optim for the optimizer, and DataLoader/TensorDataset for feeding batches. Listing them together mirrors how a real PyTorch script is organized.

Noting the pip install torch line is deliberate — PyTorch is a sizable separate package and beginners often stumble at installation, sometimes needing a specific CUDA build. Putting it up front saves a reader a stumble before they have written any modeling code, matching the disciplined setup the series models throughout.

This slide is the structural heart of the build: defining the network as a subclass of nn.Module with a Sequential stack of Linear and ReLU layers. The architecture — 20 inputs to 64 to 32 to 2 outputs — directly embodies post 1's definitions: each Linear is a layer of weighted-sum neurons, each ReLU is the nonlinear activation between them.

Writing it as a class with a forward method models real PyTorch idiom rather than the shortest possible code. The forward method is literally the forward pass from post 3, now expressed in framework terms, so the reader sees the theory and the API line up. The two output units correspond to the two classes the CrossEntropyLoss will score.

The loss-and-optimizer slide assembles the two ingredients that turn a static network into a trainable one. CrossEntropyLoss is the standard classification loss from post 3, and Adam is the optimizer that will perform the gradient-descent updates. The lr=1e-3 argument is the learning rate, the hyperparameter post 3 flagged as most important.

The comment noting that Adam adapts the step size per weight is included because beginners often wonder why Adam, not plain SGD. Adam keeps a running estimate of each weight's gradient behavior and scales updates accordingly, which makes training more robust to a poorly chosen learning rate — a practical default that explains why 1e-3 with Adam is the near-universal starting point.

This slide is the payoff: the actual training loop, where every concept from post 3 becomes four lines of code inside two nested loops. The outer loop counts epochs, the inner loop iterates batches, and inside each batch the canonical sequence runs — forward (model(xb)), measure (loss_fn), reset (zero_grad), backprop (backward), update (step).

Printing the loss each epoch is deliberate: it lets the reader watch the number fall, making convergence observable rather than asserted. The ordering of zero_grad before backward before step is exactly the pattern post 5 will warn about getting wrong, so showing the correct order here builds the right habit before the cautionary post arrives.

This explanatory slide defines the epoch, a term the loop above uses but does not explain. The key idea is that one epoch is one full sweep through the training data, broken into small batches, and that training runs many epochs so the network sees the data repeatedly and refines its weights a little more each time.

The note that batches add helpful noise to the gradient is worth including because it corrects the intuition that batching is purely a memory convenience. Stochastic, batch-wise updates actually help the model escape poor minima and generalize, which is why mini-batch training is the default rather than computing the gradient over the entire dataset at once.

The evaluation slide enforces the discipline that separates a toy from a real model: judge on held-out data. It introduces three production-critical habits at once — model.eval() to switch layers like dropout into inference mode, torch.no_grad() to skip gradient tracking for speed and memory, and argmax to turn raw logits into class predictions before computing accuracy.

The no_grad context and eval() call are emphasized because forgetting them is a common, silent bug: dropout would stay active and batchnorm would misbehave at test time, quietly degrading results. Modeling the correct evaluation pattern here directly preempts the 'trusting training accuracy' mistake in post 5, where held-out evaluation is the central fix.

The pipeline diagram distills the whole build into four stages — define, compile, train, evaluate — giving readers a mental map of the workflow independent of the specific code. It reinforces that building a neural network is a sequence of disciplined steps, not a single fit call.

Using the word 'compile' for the loss-and-optimizer stage deliberately bridges to the Keras code that follows, where compile is the literal method name. The portable four-stage template is the thing a reader can carry to any neural-network problem, swapping the layer definitions while keeping the train and evaluate stages essentially unchanged.

The Keras slide shows the same architecture in a higher-level framework so readers see that the concepts, not the library, are what matter. The Dense layers with relu activations mirror the PyTorch Linear-plus-ReLU stack exactly, and compile bundles the optimizer and loss that PyTorch set up separately, while fit collapses the entire training loop into one call.

Including both frameworks side by side is pedagogically deliberate. It demonstrates that the define-compile-train-evaluate pattern is universal: Keras hides the explicit loop for convenience, PyTorch exposes it for control, but both are doing the forward-loss-backprop-update cycle from post 3. A reader who understands one can read the other, which is a durable, transferable skill.

The recap orders the six pipeline steps so a reader can reproduce the entire workflow from memory: define layers in a Module, choose a loss and Adam, loop through forward/backward/step, zero_grad before every backward, evaluate with eval() and no_grad, and recognize the same shape in Keras. It is a portable template, not just code for one dataset.

Each bullet maps to a slide, so the recap doubles as an index. The emphasis on zero_grad and on eval()/no_grad deliberately bakes in the two habits whose omission causes the most common and most silent bugs — exactly the failures the final post is built around.

The CTA pivots from the happy path to the cautionary one. Having built a working network, the reader is ready to learn how the same code goes wrong — forgetting zero_grad, a bad learning rate, unscaled inputs, overfitting, and trusting training accuracy — which is exactly post 5.

Naming the specific traps in the teaser creates anticipation and signals that the series does not stop at 'it works in a notebook.' Real competence is knowing the failure modes, and that is the promise the final post delivers on.