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Fix html validation errors

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This commit is contained in:
Michael Pivato
2024-03-03 13:25:45 +10:30
parent 8c03b4d31c
commit fda8ae3d8e
1 changed files with 265 additions and 254 deletions
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index.html
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@@ -1,3 +1,5 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
@@ -41,20 +43,21 @@
<header>
<hgroup>
<h1>Michael Pivato</h1>
<h2>Career summary and interests</h2>
<p>Career summary and interests</p>
</hgroup>
</header>
<p>
A driven and highly motivated full-stack developer, I am always ready to
improve and learn. With a vast knowledge of multiple programming
A driven and highly motivated full-stack developer, I am always ready
to improve and learn. With a vast knowledge of multiple programming
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>
<section id="career">
<h2>Career</h2>
<hgroup id="powerhealth">
<h3>PowerHealth Solutions</h3>
<h4>2019 - Current</h4>
<p>2019 - Current</p>
</hgroup>
<h4>Key Responsibilities</h4>
<ul>
@@ -64,8 +67,8 @@
</li>
<li>
Create and maintain various front- and back-end components to
support consistent theming, quality, and developer experience across
PPM and now the billing product.
support consistent theming, quality, and developer experience
across PPM and now the billing product.
</li>
<li>
Respond to internal and client feedback to improve the costing
@@ -76,20 +79,20 @@
<h4>Key Achievements</h4>
<ul>
<li>
Significant contributions to the redesigned costing product, that is
now in production use and enjoyed by clients.
Significant contributions to the redesigned costing product, that
is now in production use and enjoyed by clients.
</li>
<li>
Create and setup front-end and associated web server back-end
components on the costing and billing products, as well as internal
products.
components on the costing and billing products, as well as
internal products.
</li>
</ul>
<details id="dstgroup">
<summary role="button" class="contrast">DST Group</summary>
<hgroup>
<h4>Cadet at DST Group</h4>
<h5>2018 - 2019</h5>
<p>2018 - 2019</p>
</hgroup>
<h5>Key Responsibilities</h5>
<ul>
@@ -108,8 +111,8 @@
<ul>
<li>Implementation of Raft algorithm for the camera network.</li>
<li>
Implementation of Hand Detection using CNN, and finger recognition
with alternative algorithm.
Implementation of Hand Detection using CNN, and finger
recognition with alternative algorithm.
</li>
</ul>
</details>
@@ -117,7 +120,7 @@
<summary role="button" class="contrast">Kilburn Software</summary>
<hgroup>
<h4>Software Developer at Kilburn Software</h4>
<h5>2016 - 2018</h5>
<p>2016 - 2018</p>
</hgroup>
<h5>Key Responsibilities</h5>
<ul>
@@ -147,7 +150,7 @@
<h2>Education</h2>
<hgroup>
<h3>Bachelor of Information Technology</h3>
<h4>2016 - 2018</h4>
<p>2016 - 2018</p>
</hgroup>
<p>University of South Australia</p>
<p>GPA: 6.89</p>
@@ -167,7 +170,9 @@
<h3 id="technical">Technical</h3>
<ul>
<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>Rust</li>
<li>Flutter/Dart</li>
@@ -198,8 +203,9 @@
which includes this page!
</p>
<p>
Recently my interesets have shifted slightly to large machine learning
models, and have messed around with Stable Diffusion (mainly with
Recently my interesets have shifted slightly to large machine
learning models, and have messed around with Stable Diffusion
(mainly with
<a href="https://github.com/invoke-ai">Invoke AI</a>) and Llama
language models in
<a href="https://github.com/rustformers/llm">rustformers</a>. The
@@ -213,21 +219,23 @@
<a href="https://gitea.michaelpivato.dev/vato007/coster-rs"
>FastCoster</a
>
project, where I reduced the time taken for processing some demo 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
not using SQL Server, and using a custom algorithm in overhead
project, where I reduced the time taken for processing some demo
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 not using SQL Server, and using a custom algorithm in overhead
allocation that significantly reduced memory consumption and the
number of required calculations.
</p>
<hgroup id="picar">
<h3>PiCar</h3>
<p>
<a href="https://gitea.michaelpivato.dev/vato007/picar">Source</a>
</p>
</hgroup>
<p>
This project originally involved communication between a Raspberry Pi
and a Traxxas Slash using the Pi's GPIO to control the steering and
throttle of the RC Car. This was mounted on some 3D printed
This project originally involved communication between a Raspberry
Pi and a Traxxas Slash using the Pi's GPIO to control the steering
and throttle of the RC Car. This was mounted on some 3D printed
brackets.The steering and throttle are set using an iPhone/Android
application connected over WiFi.
</p>
@@ -238,9 +246,9 @@
area and send this information to the controlling phone.
</li>
<li>
Depth Prediction: Using the Pi's camera and an Intel Neural Compute
Stick (NCS), the Pi could process camera data and use a custom
implementation of the
Depth Prediction: Using the Pi's camera and an Intel Neural
Compute Stick (NCS), the Pi could process camera data and use a
custom implementation of the
<a href="https://gitea.michaelpivato.dev/vato007/fast-depth-tf"
>FastDepth</a
>
@@ -248,28 +256,30 @@
</li>
</ul>
<p>
Recently there have been efforts to port the backend to Rust, with the
2D Lidar sensing and control completed. The Python BreezySLAM
Recently there have been efforts to port the backend to Rust, with
the 2D Lidar sensing and control completed. The Python BreezySLAM
implementation is currently unfinished, mainly due to distractions
from other projects
</p>
<hgroup id="depthprediction">
<h3>Depth Prediction</h3>
<p>
<a href="https://gitea.michaelpivato.dev/vato007/fast-depth-tf"
>Source</a
>
</p>
</hgroup>
<p>
From the PiCar project, I explored many different implementations of
monocular depth Prediction and 3D SLAM solutions, including
implementing my own algorithms and trainers for depth prediction that
perform well on constrained devices.
implementing my own algorithms and trainers for depth prediction
that perform well on constrained devices.
</p>
<p>
This gave me a solid foundation on Tensorflow/Keras and computer
vision. It also helped expand my knowledge on machine learning from
university/online study, as I previously had not explored models this
large, or specifically computer vision related models.
university/online study, as I previously had not explored models
this large, or specifically computer vision related models.
</p>
</section>
<footer class="container">
@@ -278,3 +288,4 @@
</div>
</main>
</body>
</html>