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name: deep-learning description: "Assemble and debug ML-From-Scratch NeuralNetwork, layers, activations, losses, optimizers, MLP/CNN/RNN, and generative model-building workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

deep-learning

Use this sub-skill when a task involves ML-From-Scratch's neural-network framework: NeuralNetwork, deep-learning layers, activation/loss/optimizer selection, MLP/CNN/RNN model assembly, or advanced generative model builders.

Route here for

• Building or debugging NeuralNetwork(optimizer, loss, validation_data=None) workflows.
• Selecting and ordering layers such as Dense, Activation, Dropout, Flatten, Conv2D, pooling, BatchNormalization, RNN, Reshape, and UpSampling2D.
• Choosing optimizers, losses, and activations for small CPU examples.
• Diagnosing shape errors in MLP, CNN, RNN, autoencoder, GAN, or DCGAN-style builders.
• Creating a tiny CPU smoke check that imports the installed package and performs one deterministic epoch.

Route away

• Classical supervised estimators such as tree, regression, SVM, Naive Bayes, perceptron, or GMM classifier workflows: use supervised-learning.
• Clustering, dimensionality reduction, association mining, and non-neural unsupervised estimators: use unsupervised-learning.
• Gym environment loops, replay memory, epsilon decay, CartPole training/play loops, and DQN compatibility: use reinforcement-learning. This sub-skill only covers the neural model-builder portion.

Operating procedure

1. Treat the installed mlfromscratch package as the runtime source of truth. Do not require source-checkout reads or original demo scripts during user work.
2. Start with the framework contracts in references/api-reference.md, especially input shape conventions and exact constructor names.
3. Use references/workflows.md for MLP, CNN, RNN, autoencoder, GAN/DCGAN, and fast validation recipes.
4. If a user reports an exception or unexpected output, diagnose from references/troubleshooting.md before changing model code.
5. For quick environment validation, run one of the bundled scripts:
   • python scripts/run_mlp_smoke.py
   • python scripts/run_cnn_smoke.py

High-signal rules

• The first shape-bearing layer must receive input_shape without the batch dimension: e.g. Dense(..., input_shape=(n_features,)), Conv2D(..., input_shape=(channels, height, width)), or RNN(..., input_shape=(timesteps, input_dim)).
• CrossEntropy expects one-hot encoded targets with the same final class dimension as the network output. For multiclass classification, finish with Dense(n_classes) then Activation('softmax').
• The framework expects channels-first image batches for convolution: (n_samples, channels, height, width), not channels-last.
• Add transform layers in data-flow order: shape-bearing first layer, activation after affine/convolution/recurrent output, Flatten() before dense layers after image-like tensors.
• Full demos can be slow and plot by default. Prefer the bundled smokes or reduce epochs/sample counts and set a headless Matplotlib backend before importing plotting libraries.