DeepLearningML/2_activation_functions/2_activation_functions.ipynb

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    "<h3 style='color:blue' align='center'>Implementation of activation functions in python</h3>"
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    "<h3 style='color:purple'>Sigmoid</h3>"
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    "import math\n",
    "\n",
    "def sigmoid(x):\n",
    "  return 1 / (1 + math.exp(-x))"
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   "source": [
    "<h3 style='color:purple'>tanh</h3>"
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   "source": [
    "def tanh(x):\n",
    "  return (math.exp(x) - math.exp(-x)) / (math.exp(x) + math.exp(-x))"
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    "tanh(-56)"
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    "tanh(50)"
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    "tanh(1)"
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   "source": [
    "<h3 style='color:purple'>ReLU</h3>"
   ]
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relu(x):\n",
    "    return max(0,x)"
   ]
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     "execution_count": 15,
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     "execution_count": 14,
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    "relu(8)"
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   "cell_type": "markdown",
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   "source": [
    "<h3 style='color:purple'>Leaky ReLU</h3>"
   ]
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   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def leaky_relu(x):\n",
    "    return max(0.1*x,x)"
   ]
  },
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     "execution_count": 18,
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