Once data preprocessing is complete and all features and labels are strictly numerical, the final data preparation step is converting these values into the native tensor format of a deep learning framework. This allows the dataset to be utilized in mathematical operations and model training.

Conversion to the Tensor Format

For the JAX framework, the `jnp.array()` function (from the `jax.numpy` module) is used to cast standard arrays into JAX's specific array format. Notably, when converting standard floating-point NumPy arrays, JAX often defaults to creating a `float32` array rather than `float64` depending on the system configuration.

jax.numpy.array()

The to_numpy() method in pandas is used to convert a DataFrame into a NumPy array. By passing the dtype argument, such as dtype=float, you can ensure that all extracted numerical data is uniformly formatted as floating-point numbers. This is a common preprocessing step before loading tabular data into deep learning tensors.

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In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis. It is primarily used for data cleaning and preparation through the use of dataframes and series. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is a free software anyone can download.

Pandas

Dive into Deep Learning

All references relating to the pandas library of Python.

Pandas References

Pandas includes two main data structures: 
- Series
- DataFrame

Pandas Data Structures

Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns.

Pandas DataFrame

pandas.DataFrame.to_numpy()

In data science, missing values (often represented programmatically as `NaN`) occur when entries in a dataset are empty or undefined, resulting from missing data collection or parsing errors. These incomplete records are a persistent challenge in data preprocessing and must be deliberately handled before training a model, typically through either imputation (estimating replacement values) or deletion (discarding the incomplete rows or columns).

Missing Values

The torch.tensor() function in PyTorch constructs a tensor from data such as a Python list or a NumPy array. When preparing datasets for neural networks, this function is used to load numerical features and target labels into PyTorch's native multidimensional array format.

torch.tensor()

In TensorFlow, the tf.constant() function creates a constant tensor from an array-like object, such as a NumPy array. During data preprocessing, this operation is commonly used to load numerical features and target values into TensorFlow's native format for model training.

tf.constant()

The np.array() function within the mxnet.np module is used to convert standard Python data structures or NumPy arrays into MXNet's native tensor format. This enables datasets to be processed by MXNet's deep learning algorithms.

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