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mtk2xfugaku committed Sep 16, 2024
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42 changes: 38 additions & 4 deletions blog/blog_home.html
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</style>
</head>
<body>
<header></header>

<div id="heading">
<ul>
<li><a href="#">home</a></li>
<li><a href="#">blogs</a></li>
<li><a href="#">about</a></li>
</ul>
</div>
<div id="blog-entry">
<a href="./post_1/code.html"><h2>Eager Mode vs Graph Mode: Understanding graph building APIs in Deep Learning Frameworks</h2></a>
<a href="./post_1/code.html">Differentiable Programming</a>
<p id="date">Sep 16 2024</p>
<p>
With the advent of deep learning, more and more fields have been seeing an increase in the use
of automatic differentiation techniques such as autograd, JAX.
</p>
<button><a href="./post_1/code.html">read more</a></button>
</div>
<div id="blog-entry">
<a href="#"><h2>Eager Mode vs Graph Mode: Understanding graph building APIs in Deep Learning Frameworks</h2></a>
<p id="date">Date: Sep 16 2024</p>
<p>
Deep learning frameworks need to trade off between researcher friendly APIs and high performance,
and this divide has lead to two ways of building the computational graph one allows higher powerformance
and other allows more friendlier API.
</p>
<button><a href="./post_1/code.html">read more</a></button>
<button><a href="#">read more</a></button>
</div>

<div id="blog-entry">
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117 changes: 37 additions & 80 deletions blog/post_1/code.html
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<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/languages/python.min.js"></script>
<title>Document</title>
<title>solitary Blog</title>
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</style>
</head>
<body>
<h1>Under construction!</h1>
<header>
<h1><a href="#">Solitary Crow's Blog</a></h1>
<div id="header">
<ul>
<li><a href="https://mtk2xfugaku.github.io">home</a></li>
<li><a href="https://mtk2xfugaku.github.io/blog_home.html">blog home</a></li>
<li><a href="https://mtk2xfugaku.github.io/about.html">about me</a></li>
<li><a href="#">contact</a></li>
<li><a href="#">about</a></li>
<li><a href="#">home</a></li>
<li><a href="#">blogs</a></li>
</ul>
</header>
<h1>Eager Mode vs Graph Mode: Understanding graph building APIs in Deep Learning Frameworks</h1>

<h2>Tensorflow 1.x API</h2>
<pre>
<code class="language-python">
from src import load_env
import os
load_env()
API_KEY = os.getenv("API_KEY")
</code>
</pre>
<h2>Sample Rust code</h2>
<pre>
<code class="language-rust">
use std::fs::File;
use std::io::prelude::*;
use std::path::Path;
fn readfile() -> String {
let path = Path::new("sample.txt");
let display = path.display();
let _file = match File::open(path) {
Err(why) => panic!("error while reading file {}, {}", display, why);
Ok(_file) => _file;
};
// do more stuff here....
}
</code>
</pre>
</div>
<div id="heading">
<h1>Differentiable Programming</h1>
<p id="date">Sep 16 2024</p>
<h2>What is Differentiable Programming</h2>
<p>
Differentiable programming is a programming paradigm in which a numeric computer program can be
differentiated throughout via automatic differentiation.This allows for gradient-based optimization of
parameters in the program, often via gradient descent, as well as other learning approaches that are
based on higher order derivative information. Differentiable programming has found use in a wide variety
of areas, particularly scientific computing and machine learning.One of the early proposals to adopt such
a framework in a systematic fashion to improve upon learning algorithms was made by the Advanced Concepts
Team at the European Space Agency in early 2016
</p>
</div>
</body>
<script>
// highlight the code block
hljs.highlightAll();
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</html>

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