Huawei HCIA-AI study plan

Define neuron, activation function, loss function, and backpropagation in your own words and write each definition down without consulting notes.

Derive the gradient update for a single fully connected layer by hand on paper for a two-input, one-output example.

Build a minimal neural network from scratch with NumPy that learns the XOR function and reaches zero classification error.

Explain why nonlinear activation functions are required, using a concrete counterexample.

Distinguish CNN, RNN, and Transformer architectures by describing their core operation and typical use case in one sentence each.

Compute output dimensions of a convolution and pooling stack given kernel size, stride, and padding.

Train a small CNN on a public image dataset and report accuracy after fixing one overfitting issue with regularization or dropout.

Identify vanishing gradient symptoms in a training log and state one mitigation.

Describe the roles of Ascend NPU, Atlas hardware, and CANN in the Huawei AI stack and place them on a layered diagram.

List the main CANN components and explain what each layer abstracts for the developer.

Compare Ascend and GPU execution models on key concepts such as compute units and memory hierarchy.

Map a training job onto Ascend hardware by naming the software layers it passes through.

Install or access a MindSpore environment and run an official-style minimal training example end to end.

Convert a simple TensorFlow or NumPy model definition into equivalent MindSpore code.

Explain how MindSpore's automatic differentiation and graph execution modes differ from eager execution.

Describe the role of MindSpore Lite and MindSpore Serving in deployment.

Differentiate supervised, unsupervised, and reinforcement learning with one concrete task example each.

Explain bias-variance tradeoff and diagnose underfitting versus overfitting from train and validation curves.

Compute precision, recall, F1, and confusion matrix values for a small binary classification result by hand.

Apply k-fold cross-validation to a dataset and justify the chosen k.

Build and train a TensorFlow model using the Keras sequential API and save it in a reloadable format.

Explain the roles of tensors, variables, and gradient tapes in TensorFlow execution.

Compare TensorFlow and MindSpore on model definition, training loop, and deployment tooling in a written summary.

Describe how a trained model is exported and served in each framework.

Summarize Huawei's AI strategy pillars and connect each to a specific product or platform.

Describe the full-stack, all-scenario AI positioning and give one example product per scenario layer.

Explain how Ascend, CANN, MindSpore, and ModelArts fit together in an end-to-end workflow.

Identify common industry application patterns such as vision inspection and forecasting, and match each to suitable model types.

Review all five domains by writing a one-page summary of key terms and definitions per domain.

Complete a timed practice set covering every domain and log wrong answers by domain.

Re-explain the three weakest concepts aloud or in writing until each explanation is accurate and complete.

Verify readiness by scoring above your target on the second practice attempt.