Lecture 2

lecture01/notes_01.ipynb

@@ -170,6 +170,91 @@
     "output = np.dot(inputs, weights) + bias\n",
     "print(output)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Layer of Neurons Using Numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[4.8   1.21  2.385]\n",
+      "[4.8   1.21  2.385]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "inputs = [1.0, 2.0, 3.0, 2.5]\n",
+    "weights = np.array([\n",
+    "    [0.2, 0.8, -0.5, 1],\n",
+    "    [0.5, -0.91, 0.26, -0.5],\n",
+    "    [-0.26, -0.27, 0.17, 0.87]\n",
+    "])\n",
+    "biases = [2.0, 3.0, 0.5]\n",
+    "\n",
+    "layer_outputs = np.dot(weights, inputs) + biases\n",
+    "# must be dot(weights, inputs), not dot(inputs, weights)\n",
+    "# this takes the dot each row of the weights by the column of inputs (remember the second term is transposed)\n",
+    "print(layer_outputs)\n",
+    "\n",
+    "layer_outputs_2 = np.dot(inputs, weights.T) + biases\n",
+    "# this takes each input and multiplies by the weight. also correct.\n",
+    "print(layer_outputs_2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Layer of Neurons and Batch of Data Using Numpy\n",
+    "Batch of data is simply a set of inputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 4.8    1.21   2.385]\n",
+      " [ 8.9   -1.81   0.2  ]\n",
+      " [ 1.41   1.051  0.026]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "inputs = [ # Batch of inputs\n",
+    "    [1.0, 2.0, 3.0, 2.5], \n",
+    "    [2.0, 5.0, -1.0, 2.0], \n",
+    "    [-1.5, 2.7, 3.3, -0.8]\n",
+    "]\n",
+    "weights = np.array([\n",
+    "    [0.2, 0.8, -0.5, 1],\n",
+    "    [0.5, -0.91, 0.26, -0.5],\n",
+    "    [-0.26, -0.27, 0.17, 0.87]\n",
+    "])\n",
+    "biases = [2.0, 3.0, 0.5]\n",
+    "\n",
+    "outputs = np.dot(inputs, weights.T) + biases\n",
+    "# For every row of inputs, compute the dot of input set and weights\n",
+    "print(outputs)"
+   ]
   }
  ],
  "metadata": {