Fix html validation errors

This commit is contained in:
Michael Pivato
2024-03-03 13:25:45 +10:30
parent 8c03b4d31c
commit fda8ae3d8e

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@@ -1,4 +1,6 @@
<head> <!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" /> <meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" /> <meta name="viewport" content="width=device-width, initial-scale=1" />
<title>Michael Pivato</title> <title>Michael Pivato</title>
@@ -11,8 +13,8 @@
<link rel="mask-icon" href="/safari-pinned-tab.svg" color="#5bbad5" /> <link rel="mask-icon" href="/safari-pinned-tab.svg" color="#5bbad5" />
<meta name="msapplication-TileColor" content="#da532c" /> <meta name="msapplication-TileColor" content="#da532c" />
<meta name="theme-color" content="#ffffff" /> <meta name="theme-color" content="#ffffff" />
</head> </head>
<body> <body>
<main class="container"> <main class="container">
<aside> <aside>
<nav class="closed-on-mobile"> <nav class="closed-on-mobile">
@@ -41,20 +43,21 @@
<header> <header>
<hgroup> <hgroup>
<h1>Michael Pivato</h1> <h1>Michael Pivato</h1>
<h2>Career summary and interests</h2> <p>Career summary and interests</p>
</hgroup> </hgroup>
</header> </header>
<p> <p>
A driven and highly motivated full-stack developer, I am always ready to A driven and highly motivated full-stack developer, I am always ready
improve and learn. With a vast knowledge of multiple programming to improve and learn. With a vast knowledge of multiple programming
languages, paradigms and related technicologies, I have successfully languages, paradigms and related technicologies, I have successfully
implemented many applications that have been used and loved by clients. implemented many applications that have been used and loved by
clients.
</p> </p>
<section id="career"> <section id="career">
<h2>Career</h2> <h2>Career</h2>
<hgroup id="powerhealth"> <hgroup id="powerhealth">
<h3>PowerHealth Solutions</h3> <h3>PowerHealth Solutions</h3>
<h4>2019 - Current</h4> <p>2019 - Current</p>
</hgroup> </hgroup>
<h4>Key Responsibilities</h4> <h4>Key Responsibilities</h4>
<ul> <ul>
@@ -64,8 +67,8 @@
</li> </li>
<li> <li>
Create and maintain various front- and back-end components to Create and maintain various front- and back-end components to
support consistent theming, quality, and developer experience across support consistent theming, quality, and developer experience
PPM and now the billing product. across PPM and now the billing product.
</li> </li>
<li> <li>
Respond to internal and client feedback to improve the costing Respond to internal and client feedback to improve the costing
@@ -76,20 +79,20 @@
<h4>Key Achievements</h4> <h4>Key Achievements</h4>
<ul> <ul>
<li> <li>
Significant contributions to the redesigned costing product, that is Significant contributions to the redesigned costing product, that
now in production use and enjoyed by clients. is now in production use and enjoyed by clients.
</li> </li>
<li> <li>
Create and setup front-end and associated web server back-end Create and setup front-end and associated web server back-end
components on the costing and billing products, as well as internal components on the costing and billing products, as well as
products. internal products.
</li> </li>
</ul> </ul>
<details id="dstgroup"> <details id="dstgroup">
<summary role="button" class="contrast">DST Group</summary> <summary role="button" class="contrast">DST Group</summary>
<hgroup> <hgroup>
<h4>Cadet at DST Group</h4> <h4>Cadet at DST Group</h4>
<h5>2018 - 2019</h5> <p>2018 - 2019</p>
</hgroup> </hgroup>
<h5>Key Responsibilities</h5> <h5>Key Responsibilities</h5>
<ul> <ul>
@@ -108,8 +111,8 @@
<ul> <ul>
<li>Implementation of Raft algorithm for the camera network.</li> <li>Implementation of Raft algorithm for the camera network.</li>
<li> <li>
Implementation of Hand Detection using CNN, and finger recognition Implementation of Hand Detection using CNN, and finger
with alternative algorithm. recognition with alternative algorithm.
</li> </li>
</ul> </ul>
</details> </details>
@@ -117,7 +120,7 @@
<summary role="button" class="contrast">Kilburn Software</summary> <summary role="button" class="contrast">Kilburn Software</summary>
<hgroup> <hgroup>
<h4>Software Developer at Kilburn Software</h4> <h4>Software Developer at Kilburn Software</h4>
<h5>2016 - 2018</h5> <p>2016 - 2018</p>
</hgroup> </hgroup>
<h5>Key Responsibilities</h5> <h5>Key Responsibilities</h5>
<ul> <ul>
@@ -147,7 +150,7 @@
<h2>Education</h2> <h2>Education</h2>
<hgroup> <hgroup>
<h3>Bachelor of Information Technology</h3> <h3>Bachelor of Information Technology</h3>
<h4>2016 - 2018</h4> <p>2016 - 2018</p>
</hgroup> </hgroup>
<p>University of South Australia</p> <p>University of South Australia</p>
<p>GPA: 6.89</p> <p>GPA: 6.89</p>
@@ -167,7 +170,9 @@
<h3 id="technical">Technical</h3> <h3 id="technical">Technical</h3>
<ul> <ul>
<li>Java - Gradle, JUnit, Jooq, Swing</li> <li>Java - Gradle, JUnit, Jooq, Swing</li>
<li>Angular and Web - including Typescript/Javascript, CSS, HTML</li> <li>
Angular and Web - including Typescript/Javascript, CSS, HTML
</li>
<li>SQL - primarily MSSQL/T-SQL</li> <li>SQL - primarily MSSQL/T-SQL</li>
<li>Rust</li> <li>Rust</li>
<li>Flutter/Dart</li> <li>Flutter/Dart</li>
@@ -198,8 +203,9 @@
which includes this page! which includes this page!
</p> </p>
<p> <p>
Recently my interesets have shifted slightly to large machine learning Recently my interesets have shifted slightly to large machine
models, and have messed around with Stable Diffusion (mainly with learning models, and have messed around with Stable Diffusion
(mainly with
<a href="https://github.com/invoke-ai">Invoke AI</a>) and Llama <a href="https://github.com/invoke-ai">Invoke AI</a>) and Llama
language models in language models in
<a href="https://github.com/rustformers/llm">rustformers</a>. The <a href="https://github.com/rustformers/llm">rustformers</a>. The
@@ -213,21 +219,23 @@
<a href="https://gitea.michaelpivato.dev/vato007/coster-rs" <a href="https://gitea.michaelpivato.dev/vato007/coster-rs"
>FastCoster</a >FastCoster</a
> >
project, where I reduced the time taken for processing some demo data project, where I reduced the time taken for processing some demo
on the costing product from ~1.5 hours to ~7 seconds on a data on the costing product from ~1.5 hours to ~7 seconds on a
laptop/desktop, or ~36 seconds on a smartphone. This was mainly due to laptop/desktop, or ~36 seconds on a smartphone. This was mainly due
not using SQL Server, and using a custom algorithm in overhead to not using SQL Server, and using a custom algorithm in overhead
allocation that significantly reduced memory consumption and the allocation that significantly reduced memory consumption and the
number of required calculations. number of required calculations.
</p> </p>
<hgroup id="picar"> <hgroup id="picar">
<h3>PiCar</h3> <h3>PiCar</h3>
<p>
<a href="https://gitea.michaelpivato.dev/vato007/picar">Source</a> <a href="https://gitea.michaelpivato.dev/vato007/picar">Source</a>
</p>
</hgroup> </hgroup>
<p> <p>
This project originally involved communication between a Raspberry Pi This project originally involved communication between a Raspberry
and a Traxxas Slash using the Pi's GPIO to control the steering and Pi and a Traxxas Slash using the Pi's GPIO to control the steering
throttle of the RC Car. This was mounted on some 3D printed and throttle of the RC Car. This was mounted on some 3D printed
brackets.The steering and throttle are set using an iPhone/Android brackets.The steering and throttle are set using an iPhone/Android
application connected over WiFi. application connected over WiFi.
</p> </p>
@@ -238,9 +246,9 @@
area and send this information to the controlling phone. area and send this information to the controlling phone.
</li> </li>
<li> <li>
Depth Prediction: Using the Pi's camera and an Intel Neural Compute Depth Prediction: Using the Pi's camera and an Intel Neural
Stick (NCS), the Pi could process camera data and use a custom Compute Stick (NCS), the Pi could process camera data and use a
implementation of the custom implementation of the
<a href="https://gitea.michaelpivato.dev/vato007/fast-depth-tf" <a href="https://gitea.michaelpivato.dev/vato007/fast-depth-tf"
>FastDepth</a >FastDepth</a
> >
@@ -248,28 +256,30 @@
</li> </li>
</ul> </ul>
<p> <p>
Recently there have been efforts to port the backend to Rust, with the Recently there have been efforts to port the backend to Rust, with
2D Lidar sensing and control completed. The Python BreezySLAM the 2D Lidar sensing and control completed. The Python BreezySLAM
implementation is currently unfinished, mainly due to distractions implementation is currently unfinished, mainly due to distractions
from other projects from other projects
</p> </p>
<hgroup id="depthprediction"> <hgroup id="depthprediction">
<h3>Depth Prediction</h3> <h3>Depth Prediction</h3>
<p>
<a href="https://gitea.michaelpivato.dev/vato007/fast-depth-tf" <a href="https://gitea.michaelpivato.dev/vato007/fast-depth-tf"
>Source</a >Source</a
> >
</p>
</hgroup> </hgroup>
<p> <p>
From the PiCar project, I explored many different implementations of From the PiCar project, I explored many different implementations of
monocular depth Prediction and 3D SLAM solutions, including monocular depth Prediction and 3D SLAM solutions, including
implementing my own algorithms and trainers for depth prediction that implementing my own algorithms and trainers for depth prediction
perform well on constrained devices. that perform well on constrained devices.
</p> </p>
<p> <p>
This gave me a solid foundation on Tensorflow/Keras and computer This gave me a solid foundation on Tensorflow/Keras and computer
vision. It also helped expand my knowledge on machine learning from vision. It also helped expand my knowledge on machine learning from
university/online study, as I previously had not explored models this university/online study, as I previously had not explored models
large, or specifically computer vision related models. this large, or specifically computer vision related models.
</p> </p>
</section> </section>
<footer class="container"> <footer class="container">
@@ -277,4 +287,5 @@
</footer> </footer>
</div> </div>
</main> </main>
</body> </body>
</html>