tasks/EN_Activation_Functions.ipynb

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    "# Activation Functions\n",
    "\n",
    "Explanation: https://en.wikipedia.org/wiki/Activation_function\n",
    "\n",
    "        "
   ]
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    "Create function for for each given activation function below. \n",
    "- **(Do not rename the function names!)**:\n",
    "- **Imports are forbidden**\n"
   ]
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    "def sigmoid(x: float) -> float:\n",
    "    \"\"\"\n",
    "    Compute the sigmoid function for the given input.\n",
    "\n",
    "    Args:\n",
    "        x (float): The input value.\n",
    "\n",
    "    Returns:\n",
    "        float: The sigmoid value of the input.\n",
    "    \"\"\"\n",
    "    return\n",
    "\n",
    "def relu(x: float) -> float:\n",
    "    \"\"\"\n",
    "    Computes the Rectified Linear Unit (ReLU) activation function.\n",
    "\n",
    "    The ReLU function returns the input value if it is positive, and 0 otherwise.\n",
    "\n",
    "    Parameters:\n",
    "    x (float): The input value.\n",
    "\n",
    "    Returns:\n",
    "    float: The output value after applying the ReLU function.\n",
    "    \"\"\"\n",
    "    return \n",
    "\n",
    "def leaky_relu(x: float, alpha=0.01) -> float:\n",
    "    \"\"\"\n",
    "    Applies the leaky ReLU activation function to the input.\n",
    "\n",
    "    Parameters:\n",
    "    - x (float): The input value.\n",
    "    - alpha (float): The slope of the negative part of the function (default: 0.01).\n",
    "\n",
    "    Returns:\n",
    "    - float: The output value after applying the leaky ReLU function.\n",
    "    \"\"\"\n",
    "    return \n",
    "\n",
    "def tanh(x: float) -> float:\n",
    "    \"\"\"\n",
    "    Compute the hyperbolic tangent of a given input.\n",
    "\n",
    "    Parameters:\n",
    "    x (float): The input value.\n",
    "\n",
    "    Returns:\n",
    "    float: The hyperbolic tangent of the input value.\n",
    "    \"\"\"\n",
    "    return \n",
    "\n",
    "def softmax(x: list[float]) -> list[float]:\n",
    "    \"\"\"\n",
    "    Compute the softmax function for a list of values.\n",
    "\n",
    "    Parameters:\n",
    "    x (list[float]): The input list of values.\n",
    "\n",
    "    Returns:\n",
    "    list[float]: The softmax values for the input list.\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    return \n"
   ]
  }
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