Replaced fake hints with <details>

Author: sofiiako

NumPy/Array Basics/Create an Empty Array/task.md

@@ -8,11 +8,12 @@ NumPy offers several functions to create arrays with initial placeholder content
 - the function [`empty`](https://numpy.org/doc/stable/reference/generated/numpy.empty.html#numpy.empty) creates an array where the initial content is random and 
 depends on the state of the memory.
 
-<div class="hint">
+
+<details>
 
 By default, the `dtype` of an array created in such a way is [`float64`](https://numpy.org/doc/stable/reference/arrays.scalars.html?highlight=float64#numpy.float64), 
 but it can be specified otherwise with the keyword argument `dtype`.
-</div>
+</details>
 
 ```python
 a = np.zeros((3, 4))

NumPy/Array Indexing and Slicing/Indexing Basics/task.md

@@ -37,15 +37,15 @@ Basic slicing occurs when obj is a slice object (constructed by `start:stop:step
 of brackets), an integer, or a tuple of slice objects and integers. All arrays generated by 
 basic slicing are always [views](https://numpy.org/doc/stable/glossary.html#term-view) of the original array.
 
-<div class="hint">
+<details>
 
-NumPy slicing creates a view instead of a copy as in the case of built-in Python 
-sequences such as string, tuple, and list. Care must be taken when extracting a 
-small portion from a large array that becomes useless after the extraction 
-because the small portion extracted contains a reference to the large original 
-array, whose memory will not be released until all arrays derived from it are 
+NumPy slicing creates a view instead of a copy as in the case of built-in Python
+sequences such as string, tuple, and list. Care must be taken when extracting a
+small portion from a large array that becomes useless after the extraction
+because the small portion extracted contains a reference to the large original
+array, whose memory will not be released until all arrays derived from it are
 garbage-collected. In such cases, an explicit `copy()` is recommended.
-</div>
+</details>
 
 1-D array:
 ```python

NumPy/Array Math/Linear Algebra/task.md

@@ -38,7 +38,8 @@ Output:
  [ 0 40]]
 ```
 
-<div class="hint">
+<details>
+<summary><code>numpy.matmul</code></summary>
 
 There's also the [`numpy.matmul(a, b)`](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html)
 function (shorthand: `@`). `matmul` differs from `dot` in two important ways:
@@ -57,7 +58,7 @@ Output:
 [[ 0 20]
  [ 0 40]]
 ```
-</div>
+</details>
 
 Matrix multiplication is arguably the most important and widely used operation in machine learning.
 It is used in linear regression, various kinds of neural networks, and other approaches.

NumPy/Transposing Sorting Concatenating/Sort/task.md

@@ -5,12 +5,12 @@ and [`numpy.ndarray.sort`](https://numpy.org/doc/stable/reference/generated/nump
 the only difference being that the former returns a sorted copy of an array, while the latter is used 
 to sort in-place.
 
-<div class="hint">
+<details>
 
 Various sorting algorithms can be used with these methods (see the documentation); they differ in 
 their average speed, worst-case performance, work space size, and stability.
 The default is [quicksort](https://en.wikipedia.org/wiki/Quicksort).
-</div> 
+</details>
 
 ```python
 a = np.array([[1, 4], [3, 1]])