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<p>Hello aspiring pyTorch experts,<br>
<br>
the discussion in our last meeting revealed that many of you would
benefit from an overview of pyTorch basics first before diving
into the deep end. We are currently running a doodle poll [1] to
converge on a preferred meeting time.<br>
</p>
<p>We are still looking for volunteers to present a short, informal
summary of selected modules in the basics course [2], which covers
the following topics:<br>
<br>
Module 1: Tensors, Derivatives, Dataset<br>
Module 2: Linear Regression, Loss, Gradient Descent, Cost,
Training<br>
Module 3: Gradient Descent, Optimisation, Validation, early stop<br>
Module 4: Linear Regression in depth: Prediction, multiple outputs<br>
<br>
Digital glory and virtual cookies await! <span style="color:
rgb(32, 33, 36); font-family: arial, sans-serif; font-size:
16px; font-style: normal; font-variant-ligatures: normal;
font-variant-caps: normal; font-weight: 400; letter-spacing:
normal; orphans: 2; text-align: left; text-indent: 0px;
text-transform: none; white-space: normal; widows: 2;
word-spacing: 0px; -webkit-text-stroke-width: 0px;
background-color: rgb(255, 255, 255); text-decoration-thickness:
initial; text-decoration-style: initial; text-decoration-color:
initial; display: inline !important; float: none;">🍪</span><br>
<br>
For more information, visit our slack channel.<br>
- Your DS Group<br>
</p>
<p>[1] <a class="moz-txt-link-freetext" href="https://doodle.com/poll/z3a2whmr9gpt8bp6#calendar">https://doodle.com/poll/z3a2whmr9gpt8bp6#calendar</a><br>
[2] <a class="moz-txt-link-freetext" href="https://www.edx.org/course/pytorch-basics-for-machine-learning">https://www.edx.org/course/pytorch-basics-for-machine-learning</a>
(choose the audit option for a free look at the course)<br>
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