H13-311_V3.5: HCIA-AI V3.5

The order of a tensor refers to its rank, which is the number of dimensions it has. For the tensor [[0,1],[2,3]], the rank is 2 because it is a 2x2 matrix, meaning it has 2 dimensions.

In machine learning, hyperparameters are the parameters that govern the learning process and are not learned from the data. Hyperparameter optimization or hyperparameter tuning is a critical part of improving a model's performance. The goal of a hyperparameter-based search is to find the set of hyperparameters that maximizes the model's performance on a given dataset.

There are different techniques for hyperparameter tuning, such as grid search, random search, and more advanced methods like Bayesian optimization. The performance of the model is assessed based on evaluation metrics (like accuracy, precision, recall, etc.), and the hyperparameters are adjusted accordingly to achieve the best performance.

In Huawei's HCIA AI curriculum, hyperparameter optimization is discussed in relation to both traditional machine learning models and deep learning frameworks. The course emphasizes the importance of selecting appropriate hyperparameters and demonstrates how frameworks such as TensorFlow and Huawei's ModelArts platform can facilitate hyperparameter searches to optimize models efficiently.

HCIA AI

AI Overview and Machine Learning Overview: Emphasize the importance of hyperparameters in model training.

Deep Learning Overview: Highlights the role of hyperparameter tuning in neural network architectures, including tuning learning rates, batch sizes, and other key parameters.

AI Development Frameworks: Discusses the use of hyperparameter search tools in platforms like TensorFlow and Huawei ModelArts.

AI inference chips are generally simpler than training chips because inference involves running a trained model on new data, which requires fewer computations compared to the training phase. Training chips need to perform more complex tasks like backpropagation, gradient calculations, and frequent parameter updates. Inference, on the other hand, mostly involves forward pass computations, making inference chips optimized for speed and efficiency but not necessarily more complex than training chips.

Thus, the statement is false because inference chips are optimized for simpler tasks compared to training chips.

HCIA AI

Cutting-edge AI Applications: Describes the difference between AI inference and training chips, focusing on their respective optimizations.

Deep Learning Overview: Explains the distinction between the processes of training and inference, and how hardware is optimized accordingly.

The gradient descent method is a core optimization technique in machine learning, particularly for neural networks and deep learning models. The common gradient descent methods include:

Batch Gradient Descent (BGD): Updates the model parameters after computing the gradients from the entire dataset.

Mini-batch Gradient Descent (MBGD): Updates the model parameters using a small batch of data, combining the benefits of both batch and stochastic gradient descent.

Stochastic Gradient Descent (SGD): Updates the model parameters for each individual data point, leading to faster but noisier updates.

Multi-dimensional gradient descent is not a recognized method in AI or machine learning.

In MindSpore, callbacks can be used in various scenarios such as:

Early stopping: To stop training when the performance plateaus or certain criteria are met.

Saving model parameters: To save checkpoints during or after training using the ModelCheckpoint callback.

Monitoring loss values: To keep track of loss values during training using LossMonitor, allowing interventions if necessary.

Adjusting the activation function is not a typical use case for callbacks, as activation functions are usually set during model definition.

In a fully-connected layer, the number of parameters is calculated by multiplying the number of input features by the number of neurons in the hidden layer. For an image of resolution 100100=10,000100 \times 100 = 10,000100100=10,000 pixels and a hidden layer of 1,000 neurons, the total number of parameters is 10,0001,000=1,000,00010,000 \times 1,000 = 1,000,00010,0001,000=1,000,000.

A . TRUE. The common decision tree algorithms include ID3, C4.5, and CART. These are the most widely used algorithms for decision tree generation.

B . FALSE. Purity in decision trees can be measured using multiple metrics, such as information gain, Gini index, and others, not just information entropy.

C . TRUE. Building a decision tree involves selecting the best features and determining their order in the tree structure to split the data effectively.

D . TRUE. One key step in decision tree generation is evaluating the purity of different splits (e.g., how well the split segregates the target variable) by comparing metrics like information gain or Gini index.

HCIA AI

Machine Learning Overview: Covers decision tree algorithms and their use cases.

Deep Learning Overview: While this focuses on neural networks, it touches on how decision-making algorithms are used in structured data models.

In machine learning:

The testing set is a dataset used after training to evaluate the model's performance and generalization ability. Each sample in this set is called a test sample.

A dataset generally has multiple dimensions, with each dimension representing a feature or attribute of the data.

A typical machine learning process divides the data into a training set (to train the model), a validation set (to tune hyperparameters and avoid overfitting), and a test set (to evaluate the model's final performance).

The statement that the validation set and test set are the same is false because they serve different purposes: validation is for hyperparameter tuning, while testing is for final model evaluation.

In MindSpore, when you define a tensor using Tensor(np.array([[1, 2], [3, 4]]), dtype.int32), the dtype attribute returns the data type of the tensor, which in this case is mindspore.int32. This specifies the type of elements in the tensor.