context (neighboring words), and the label (masked word to be predicted). The word tokens are vectorized by replacing the actual words by their indices in our vocabulary. If a word doesn’t exist in the overfitting. We can now vectorize the train and test datasets. x_train_vectorized = vectorization_layer(x_train) x_test_vectorized = vectorization_layer(x_test) Step 3: Initialization of the Embedding model! bow_model_w2v_history = bow_model_w2v.fit( x_train_vectorized, y_train, batch_size=64, epochs=10, validation_data=(x_test_vectorized, y_test)) Epoch 1/10 313/313 [==============================]

statistical software suite also provides the data set corresponding to the pandas DataFrame. Also SAS vectorized operations, filtering, string processing operations, and more have similar functions in pandas Minimum Ver- sion Notes SciPy 1.7.1 Miscellaneous statistical functions numba 0.53.1 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.19.0 pandas-like API for N-dimensional other columns. In pandas, you’re able to do operations on whole columns directly. pandas provides vectorized operations by specifying the individual Series in the DataFrame. New columns can be assigned in

solution. It supports vector operations which operate on a vector (or a batch) of x variables (vectorized execution) instead of one variable at a time. Although it is possible to work without it, you would deep learning applications which frequently operate on batches of data. Using vectorized operations also speeds up the execution (and this book is about efficiency, after all!). We highly recommend learning

HTML parser for read_html (see note) matplotlib 2.2.2 Visualization numba 0.46.0 Alternative execution engine for rolling operations openpyxl 2.5.7 Reading / writing for xlsx files pandas-gbq 0.8.0 generally uses regular expressions by default (and in some cases always uses them). See more at Vectorized String Methods. In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 2000-01-10 0.030876 1.371900 0.609836 Applying elementwise functions Since not all functions can be vectorized (accept NumPy arrays and return another array or value), the methods applymap() on DataFrame and

HTML parser for read_html (see note) matplotlib 2.2.2 Visualization numba 0.46.0 Alternative execution engine for rolling operations openpyxl 2.5.7 Reading / writing for xlsx files pandas-gbq 0.8.0 generally uses regular expressions by default (and in some cases always uses them). See more at Vectorized String Methods. In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 2000-01-10 0.413806 2.489837 1.220589 Applying elementwise functions Since not all functions can be vectorized (accept NumPy arrays and return another array or value), the methods applymap() on DataFrame and

HTML parser for read_html (see note) matplotlib 2.2.2 Visualization numba 0.46.0 Alternative execution engine for rolling operations openpyxl 2.5.7 Reading / writing for xlsx files pandas-gbq 0.8.0 generally uses regular expressions by default (and in some cases always uses them). See more at Vectorized String Methods. In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 2000-01-10 0.030876 1.371900 0.609836 Applying elementwise functions Since not all functions can be vectorized (accept NumPy arrays and return another array or value), the methods applymap() on DataFrame and

HTML parser for read_html (see note) matplotlib 2.2.2 Visualization numba 0.46.0 Alternative execution engine for rolling operations openpyxl 2.5.7 Reading / writing for xlsx files pandas-gbq 0.8.0 generally uses regular expressions by default (and in some cases always uses them). See more at Vectorized String Methods. In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 2000-01-10 0.030876 1.371900 0.609836 Applying elementwise functions Since not all functions can be vectorized (accept NumPy arrays and return another array or value), the methods applymap() on DataFrame and

generally uses regular expressions by default (and in some cases always uses them). See more at Vectorized String Methods. In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', analysis toolkit, Release 0.25.0 Applying elementwise functions Since not all functions can be vectorized (accept NumPy arrays and return another array or value), the methods applymap() on DataFrame and over the rows is not needed and can be avoided with one of the following approaches: • Look for a vectorized solution: many operations can be performed using built-in methods or NumPy func- tions, (boolean)

generally uses regular expressions by default (and in some cases always uses them). See more at Vectorized String Methods. In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', analysis toolkit, Release 0.25.1 Applying elementwise functions Since not all functions can be vectorized (accept NumPy arrays and return another array or value), the methods applymap() on DataFrame and over the rows is not needed and can be avoided with one of the following approaches: • Look for a vectorized solution: many operations can be performed using built-in methods or NumPy func- tions, (boolean)

generally uses regular expressions by default (and in some cases always uses them). See more at Vectorized String Methods. In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 2000-01-10 0.413993 0.137720 NaN Applying Elementwise Functions Since not all functions can be vectorized (accept NumPy arrays and return another array or value), the methods applymap() on DataFrame and over the rows is not needed and can be avoided with one of the following approaches: • Look for a vectorized solution: many operations can be performed using built-in methods or NumPy func- tions, (boolean)