update

deep_learning_from_scratch.ipynb

@@ -18,6 +18,63 @@
     "## Perceptron"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is a perceptron\n",
+    "\n",
+    "![A perceptron](./figures/dlscratch_perceptron.png)    \n",
+    "$\n",
+    "y = \n",
+    "\\begin{cases} \n",
+    "0 & \\text{if } w_1 x_1 + w_2 x_2 \\leq \\theta, \\\\\n",
+    "1 & \\text{if } w_1 x_1 + w_2 x_2 > \\theta.\n",
+    "\\end{cases}\n",
+    "$\n",
+    "$x$ is input signal, $y$ is output signal, $w$ is weight, $\\theta$ is threshold.\n",
+    "\n",
+    "### Simple logic circuit\n",
+    "\n",
+    "![AND gate](./figures/dlscratch_andgate.png) ![NOTAND gate](./figures/dlscratch_notandgate.png) ![OR gate](./figures/dlscratch_orgate.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0, 0) -> 0\n",
+      "(1, 0) -> 0\n",
+      "(0, 1) -> 0\n",
+      "(1, 1) -> 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def AND(x1, x2):\n",
+    "    x = np.array([x1, x2])\n",
+    "    w = np.array([0.5, 0.5])\n",
+    "    b = -0.7\n",
+    "    tmp = np.sum(w*x) + b\n",
+    "    if tmp <= 0:\n",
+    "        return 0\n",
+    "    else:\n",
+    "        return 1\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "    for xs in [(0, 0), (1, 0), (0, 1), (1, 1)]:\n",
+    "        y = AND(xs[0], xs[1])\n",
+    "        print(str(xs) + \" -> \" + str(y))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -69,8 +126,27 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.11.6 ('base')",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "d0af3871f74fa798a0a222812f3db7ca3ddb43c51379edde28baf2323a73ac20"
+   }
   }
  },
  "nbformat": 4,