perceptrons-in-colab.ipynb

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   "source": [
    "[![Open In Colab!](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/trsvchn/interactive-perceptrons/blob/master/perceptrons-in-colab.ipynb)"
   ]
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   "source": [
    "# Interactive Logical Perceptrons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!git clone https://github.com/trsvchn/interactive-perceptrons.git\n",
    "%cd interactive-perceptrons\n",
    "from perceptrons import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Move weights and biases sliders to help perceptron to separate given set of points.\n",
    "\n",
    "Visit [Intro to Deep Learning with PyTorch](https://www.udacity.com/course/deep-learning-pytorch--ud188) Lesson 2.8 \"Perceptrons as Logical Operators\" for detailed explanations."
   ]
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  {
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   "source": [
    "## AND Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "and_perceptron()"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OR Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "or_perceptron()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NOT Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "not_perceptron()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XOR Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xor_perceptron()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-Layer XOR Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xor_mlp()"
   ]
  }
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