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environment.yml

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- black>=20.8b1
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- conda
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- ipywidgets>=7.6.3
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- jax>=0.2.8
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- jaxlib>=0.1.59
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- jax<=0.2.9
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- jaxlib<=0.1.59
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- jupyter
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- jupyterlab>=3.0.5
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- mamba

notebooks/01-differential-programming/01-neural-nets-from-scratch.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.9"
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"version": "3.9.6"
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},
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"toc-autonumbering": true,
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"toc-showcode": false,

notebooks/01-differential-programming/02-gradient-optimization.ipynb

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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"source": [
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"## Derivatives\n",
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"\n",
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"\n",
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"Hence, \n",
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"\n",
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"$$w = -\\frac{3}{2} = 1.5$$\n",
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"$$w = -\\frac{3}{2}$$\n",
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"\n",
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"To check whether the value of $w$ at the place where $f'(w) = 0$ is a minima or maxima,\n",
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"we can use another piece of knowledge from 1st year undergraduate calculus:\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.2"
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"version": "3.9.6"
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}
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},
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"nbformat": 4,

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