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Replace handson-ml2 with handson-ml3, and fix figure chapter numbers

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Aurélien Geron
2021-11-23 15:42:16 +13:00
parent e38983d595
commit 5bb0366125
23 changed files with 137 additions and 137 deletions
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8
math_differential_calculus.ipynb
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@@ -19,10 +19,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/math_differential_calculus.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml3/blob/main/math_differential_calculus.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/math_differential_calculus.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml3/blob/main/math_differential_calculus.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@@ -476,7 +476,7 @@
"id": "ebb31wJp72Zn"
},
"source": [
"**Important note:** in Deep Learning, differentiation is almost always performed automatically by the framework you are using (such as TensorFlow or PyTorch). This is called auto-diff, and I did [another notebook](https://github.com/ageron/handson-ml2/blob/master/extra_autodiff.ipynb) on that topic. However, you should still make sure you have a good understanding of derivatives, or else they will come and bite you one day, for example when you use a square root in your cost function without realizing that its derivative approaches infinity when $x$ approaches 0 (tip: you should use $\\sqrt{x+\\epsilon}$ instead, where $\\epsilon$ is some small constant, such as $10^{-4}$)."
"**Important note:** in Deep Learning, differentiation is almost always performed automatically by the framework you are using (such as TensorFlow or PyTorch). This is called auto-diff, and I did [another notebook](https://github.com/ageron/handson-ml3/blob/main/extra_autodiff.ipynb) on that topic. However, you should still make sure you have a good understanding of derivatives, or else they will come and bite you one day, for example when you use a square root in your cost function without realizing that its derivative approaches infinity when $x$ approaches 0 (tip: you should use $\\sqrt{x+\\epsilon}$ instead, where $\\epsilon$ is some small constant, such as $10^{-4}$)."
]
},
{
@@ -1064,7 +1064,7 @@
" zs = f(xs, ys)\n",
"\n",
" surface = ax.plot_surface(xs, ys, zs,\n",
" cmap=mpl.cm.coolwarm,\n",
" cmap=\"coolwarm\",\n",
" linewidth=0.3, edgecolor='k')\n",
"\n",
" ax.set_xlabel(\"$x$\", fontsize=14)\n",