{
  "schemaVersion": 1,
  "lessonId": "optimization-06",
  "contentHash": "29d8442cd8365bb5d24c7021a429284ada0b407c960fc4a572409a98d41782c2",
  "executedAt": "2026-10-20T10:45:37.409448+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "weights / rank / MSE: [0.6666669 1.6666665] 2 0.11111113\nweights / rank / MSE: [0.6666669 1.6666665] 2 0.11111113\ncondition / target change / weight change: 200.00504 0.0010000002 0.14142144\nPASS: optimization-06\n",
  "notebookCells": [
    {
      "sourceHash": "ad0bfb277b838293b083772f5339456a0f5548fee553038e56cce150cb95e991",
      "outputs": []
    },
    {
      "sourceHash": "70a387a472563cb47f5cf689076937babeec7e716417064163467adf5a4c3bb6",
      "outputs": []
    },
    {
      "sourceHash": "588c512cde18586137e3160398865f30c4a54a111af0c936f876d20dc1b575c0",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "weights / rank / MSE: [0.6666669 1.6666665] 2 0.11111113\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "7fb119eb663b51db3de1032107fe8ec9484b34938e75a577ac4c02b4d8373e32",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "weights / rank / MSE: [0.6666669 1.6666665] 2 0.11111113\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "133a838bda9b553a5c96d07a2221ca8e7eb77aa14d878a0a166dc3f0525ec4b4",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "b0efd3a525936f10bd066369a947db3dd1e3aea163a6abeda946217bcfc679c3",
      "outputs": []
    },
    {
      "sourceHash": "8da90601b13cf4bb2f11b1047759dd2d731a75802adfa316c9439058e744ee90",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "condition / target change / weight change: 200.00504 0.0010000002 0.14142144\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "cf7d777e4840739a7184720dfc84ae636596663d49590bc38e5e1abd780da3ae",
      "outputs": []
    },
    {
      "sourceHash": "63f1877927bfade585a6fd3689759ba88bb7fe21385aba2600eb6f4dad4ba884",
      "outputs": []
    },
    {
      "sourceHash": "6211ffd1c482ad7b10a3be5708555578157783597750b533bf64e0e5b40f4faa",
      "outputs": []
    },
    {
      "sourceHash": "7aab28cafa41e35418a13a86185a4f42a562b9fd1351545f8b982505f279fe33",
      "outputs": []
    },
    {
      "sourceHash": "18215faa5dd7eada1dd3c6eb62e3486540ca466a5e6fe00cd2f4cb144e2bd2f3",
      "outputs": []
    },
    {
      "sourceHash": "d1e817ea8698ab50b5b5aeaf21099bfdebd8cb858d84b5b2110ece5ec72b58a3",
      "outputs": []
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}