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Add notebook outputs

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Aurélien Geron 2022-02-19 22:24:54 +13:00
parent bbc1113951
commit 0a28fab583
22 changed files with 42822 additions and 1603 deletions
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1023
01_the_machine_learning_landscape.ipynb
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02_end_to_end_machine_learning_project.ipynb
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03_classification.ipynb
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04_training_linear_models.ipynb
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05_support_vector_machines.ipynb
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06_decision_trees.ipynb
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07_ensemble_learning_and_random_forests.ipynb
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08_dimensionality_reduction.ipynb
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09_unsupervised_learning.ipynb
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10_neural_nets_with_keras.ipynb
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11_training_deep_neural_networks.ipynb
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12_custom_models_and_training_with_tensorflow.ipynb
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13_loading_and_preprocessing_data.ipynb
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14_deep_computer_vision_with_cnns.ipynb
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15_processing_sequences_using_rnns_and_cnns.ipynb
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extra_autodiff.ipynb
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@ -91,7 +91,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"(24, 10)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"df(3, 4)" "df(3, 4)"
] ]
@ -132,7 +143,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"([8, 6], [6, 0])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"d2f(3, 4)" "d2f(3, 4)"
] ]
@ -190,7 +212,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"[24.000400000048216, 10.000000000047748]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"df(3, 4)" "df(3, 4)"
] ]
@ -213,7 +246,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"(24.000400000048216, 10.000000000047748)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"def dfdx(x, y):\n", "def dfdx(x, y):\n",
" return gradients(f, [x,y])[0]\n", " return gradients(f, [x,y])[0]\n",
@ -245,7 +289,19 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"[[7.999999951380232, 6.000099261882497],\n",
" [6.000099261882497, -1.4210854715202004e-06]]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"d2f(3, 4)" "d2f(3, 4)"
] ]
@ -341,7 +397,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"42"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"x.value = 3\n", "x.value = 3\n",
"y.value = 4\n", "y.value = 4\n",
@ -407,7 +474,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"[-5.46761419430053]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"from math import sin\n", "from math import sin\n",
"\n", "\n",
@ -468,7 +546,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"(24.0, 10.0)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"dfdx.evaluate(), dfdy.evaluate()" "dfdx.evaluate(), dfdy.evaluate()"
] ]
@ -496,7 +585,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": 19,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"[[8.0, 6.0], [6.0, 0.0]]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"[[d2fdxdx.evaluate(), d2fdxdy.evaluate()],\n", "[[d2fdxdx.evaluate(), d2fdxdy.evaluate()],\n",
" [d2fdydx.evaluate(), d2fdydy.evaluate()]]" " [d2fdydx.evaluate(), d2fdydy.evaluate()]]"
@ -606,7 +706,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 21,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"6.0 + 4.0ε"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"3 + DualNumber(3, 4)" "3 + DualNumber(3, 4)"
] ]
@ -622,7 +733,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 22,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"15.0 + 41.0ε"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"DualNumber(3, 4) * DualNumber(5, 7)" "DualNumber(3, 4) * DualNumber(5, 7)"
] ]
@ -638,7 +760,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 23,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"42.0"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"x.value = DualNumber(3.0)\n", "x.value = DualNumber(3.0)\n",
"y.value = DualNumber(4.0)\n", "y.value = DualNumber(4.0)\n",
@ -674,7 +807,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 25,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"24.0"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"dfdx" "dfdx"
] ]
@ -683,7 +827,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 26,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"10.0"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"dfdy" "dfdy"
] ]
@ -782,7 +937,15 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 29, "execution_count": 29,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"((x) * (x)) * (y) + y + 2\n"
]
}
],
"source": [ "source": [
"print(f)" "print(f)"
] ]
@ -791,7 +954,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 30, "execution_count": 30,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"42"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"result" "result"
] ]
@ -800,7 +974,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 31, "execution_count": 31,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"24.0"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"x.gradient" "x.gradient"
] ]
@ -809,7 +994,18 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 32, "execution_count": 32,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"10.0"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"y.gradient" "y.gradient"
] ]
@ -841,7 +1037,19 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 34, "execution_count": 34,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"[<tf.Tensor: shape=(), dtype=float32, numpy=24.0>,\n",
" <tf.Tensor: shape=(), dtype=float32, numpy=10.0>]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"x = tf.Variable(3.)\n", "x = tf.Variable(3.)\n",
"y = tf.Variable(4.)\n", "y = tf.Variable(4.)\n",
@ -864,7 +1072,27 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 35, "execution_count": 35,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:Calling GradientTape.gradient on a persistent tape inside its context is significantly less efficient than calling it outside the context (it causes the gradient ops to be recorded on the tape, leading to increased CPU and memory usage). Only call GradientTape.gradient inside the context if you actually want to trace the gradient in order to compute higher order derivatives.\n"
]
},
{
"data": {
"text/plain": [
"[[<tf.Tensor: shape=(), dtype=float32, numpy=8.0>,\n",
" <tf.Tensor: shape=(), dtype=float32, numpy=6.0>],\n",
" [<tf.Tensor: shape=(), dtype=float32, numpy=6.0>, None]]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"x = tf.Variable(3.)\n", "x = tf.Variable(3.)\n",
"y = tf.Variable(4.)\n", "y = tf.Variable(4.)\n",

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extra_gradient_descent_comparison.ipynb
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math_differential_calculus.ipynb
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math_linear_algebra.ipynb
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tools_matplotlib.ipynb
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tools_numpy.ipynb
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tools_pandas.ipynb
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