{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neuron with 3 inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output: 2.3\n"
     ]
    }
   ],
   "source": [
    "inputs = [1, 2, 3]\n",
    "weights = [0.2, 0.8, -0.5]\n",
    "bias = 2\n",
    "\n",
    "output = (inputs[0]*weights[0] + inputs[1]*weights[1] + inputs[2]*weights[2] + bias)\n",
    "\n",
    "print(f\"Output: {output}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neuron with 4 inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output: 4.8\n"
     ]
    }
   ],
   "source": [
    "inputs = [1.0, 2.0, 3.0, 2.5]\n",
    "weights = [0.2, 0.8, -0.5, 1.0]\n",
    "bias = 2\n",
    "\n",
    "output = (inputs[0]*weights[0] + inputs[1]*weights[1] + inputs[2]*weights[2] + inputs[3]*weights[3] + bias)\n",
    "\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer of 3 neurons with 4 inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.8, 1.21, 2.385]\n"
     ]
    }
   ],
   "source": [
    "num_neurons = 3\n",
    "num_inputs = 4\n",
    "inputs = [1.0, 2.0, 3.0, 2.5]\n",
    "weights = [[0.2, 0.8, -0.5, 1.0],\n",
    "           [0.5, -0.91, 0.26, -0.5],\n",
    "           [-0.26, -0.27, 0.17, 0.87]]\n",
    "biases = [2, 3, 0.5]\n",
    "\n",
    "outputs = []\n",
    "for i in range(num_neurons):\n",
    "    output = 0\n",
    "    for j in range(num_inputs):\n",
    "        output += inputs[j]*weights[i][j]\n",
    "    output += biases[i]\n",
    "    outputs.append(output)\n",
    "\n",
    "print(outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer of 3 neurons with 4 inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.8, 1.21, 2.385]\n"
     ]
    }
   ],
   "source": [
    "num_neurons = 3\n",
    "num_inputs = 4\n",
    "inputs = [1.0, 2.0, 3.0, 2.5]\n",
    "weights = [[0.2, 0.8, -0.5, 1.0],\n",
    "           [0.5, -0.91, 0.26, -0.5],\n",
    "           [-0.26, -0.27, 0.17, 0.87]]\n",
    "biases = [2, 3, 0.5]\n",
    "\n",
    "outputs = []\n",
    "for neuron_weights, neuron_bias in zip(weights, biases):\n",
    "    neuron_output = 0\n",
    "    for input, weight in zip(inputs, neuron_weights):\n",
    "        neuron_output += input*weight\n",
    "    neuron_output += neuron_bias\n",
    "    outputs.append(neuron_output)\n",
    "\n",
    "print(outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single Neuron using Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.8\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "inputs = [1.0, 2.0, 3.0, 2.5]\n",
    "weights = [0.2, 0.8, -0.5, 1.0]\n",
    "bias = 2\n",
    "\n",
    "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)"
   ]
  }
 ],
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