unit02-pytorch-tensors/2.4-linalg/2.4-linalg-part4.ipynb

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   "source": [
    "# 2.4 Improving Code Efficiency with Linear Algebra"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Part 4: Broadcasting — Computations with Unequal Tensor Shapes"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "id": "b1584b82-1800-48e5-9966-c9abe98a3388",
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   "outputs": [],
   "source": [
    "import torch"
   ]
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   "cell_type": "code",
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    {
     "data": {
      "text/plain": [
       "tensor([6.5000, 7.6000, 8.7000, 9.8000])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = torch.tensor([1.1, 2.1, 3.1, 4.1])\n",
    "\n",
    "b = torch.tensor([5.4, 5.5, 5.6, 5.7])\n",
    "\n",
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e34b95b0-5310-4906-9980-eb4c6c1f803d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[6.5000, 7.6000, 8.7000, 9.8000],\n",
       "        [6.6000, 7.7000, 8.8000, 9.9000]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = torch.tensor([[1.1, 2.1, 3.1, 4.1],\n",
    "                  [1.2, 2.2, 3.2, 4.2]])\n",
    "\n",
    "b = torch.tensor([5.4, 5.5, 5.6, 5.7])\n",
    "\n",
    "A + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dd2e2aa-be40-4dd8-a70e-913bfce86e5a",
   "metadata": {},
   "outputs": [],
   "source": []
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