Vermischtes über Data Science, Machine Learning und nbdev

<article class="post-detail"> <header> <h2 class="post-title"> <a href="https://python-podcast.de/show/nbdev/">Vermischtes über Data Science, Machine Learning und nbdev</a> (click here to comment) </h2> <!-- link is on one line to avoid underlined whitespace --> <div class="post-card-meta"> <a href="https://python-podcast.de/show/nbdev/"><time datetime="2021-02-19T00:00:00+01:00">19. Februar 2021</time>,</a> <span class="author">Jochen</span> </div> </header> <div class="post-body"> <section class="block-overview"> <section class="block-paragraph"> Mit <a href="https://twitter.com/theuni">Christian</a>&nbsp;haben wir uns heute mal wieder ein bisschen mehr &uuml;ber Machine Learning etc. unterhalten. Was w&auml;re, wenn man Jupyter-Notebooks als IDE verwenden wollte (nbdev)? Was braucht man eigentlich heutzutage so an Hardware, wenn man Modelle trainieren will? Ausserdem haben wir ein bisschen auf der Mikrofon/Headset-Seite aufger&uuml;stet (keine Ahnung, ob man das h&ouml;rt).<br /> <br /> </section> <section class="block-audio"> </section> <section class="block-paragraph"> &nbsp; </section> </section> <section class="block-detail"> <section class="block-paragraph"> &nbsp; <h2>Shownotes</h2> <p>Unsere E-Mail f&uuml;r Fragen, Anregungen &amp; Kommentare: <a href="mailto:[email protected]">[email protected]</a></p> <h3>News aus der Szene</h3> <ul> <li><a href="https://numpy.org/doc/stable/release/1.20.0-notes.html">Numpy 1.20 Release</a></li> <li><a href="https://pandas.pydata.org/docs/whatsnew/v1.2.0.html">Pandas 1.2 Release</a></li> <li><a href="https://spacy.io/usage/v3">Spacy v3 Release</a></li> <li>Ben Gorman:&nbsp;<a href="https://youtu.be/w-Nw8IRiL1Y">Python NumPy For Your Grandma</a>,&nbsp;<a href="https://youtu.be/TF8lxoQhxC0">Python Pandas For Your Grandpa</a></li> <li><a href="https://mypy-lang.blogspot.com/2021/01/mypy-0800-released.html">Mypy 0.800 Release</a></li> <li><a href="https://pip.pypa.io/en/stable/news/#id1">Pip 21.0 Release</a></li> <li><a href="https://github.com/flyingcircusio/appenv">appenv</a>,&nbsp;<a href="https://github.com/flyingcircusio/batou">batou</a></li> </ul> <h3>NBDEV</h3> <ul> <li><a href="https://nbdev.fast.ai/">nbdev</a></li> <li><a href="https://youtu.be/7jiPeIFXb6U">I don&#39;t like notebooks.- Joel Grus</a></li> <li><a href="https://en.wikipedia.org/wiki/Literate_programming">Literate Programming</a></li> <li><a href="https://youtu.be/9Q6sLbz37gk">I Like Notebooks - Jeremy Howard</a></li> <li><a href="https://colab.research.google.com/">google colab</a>&nbsp;<a href="https://mybinder.org/">Binder</a></li> <li>Buch:&nbsp;<a href="https://github.com/fastai/fastbook">Deep Learning for Coders with fastai and PyTorch</a></li> </ul> <h3>Machine Learning Recap</h3> <ul> <li>ocr:&nbsp;<a href="https://github.com/tesseract-ocr/tesseract">Tesseract</a></li> <li><a href="https://de.wikipedia.org/wiki/Vektorprozessor">Vektorrechner</a>&nbsp;/&nbsp;<a href="https://www.nvidia.com/en-us/data-center/tensor-cores/">Tensor Cores</a>&nbsp;/&nbsp;<a href="https://en.wikipedia.org/wiki/Tensor_Processing_Unit">TPUs</a></li> <li>Hardware:&nbsp;<a href="https://timdettmers.com/2020/09/07/which-gpu-for-deep-learning/">Which GPU(s) to Get for Deep Learning</a></li> <li><a href="https://www.kaggle.com/c/criteo-display-ad-challenge">Criteo: Display Advertising Challenge</a></li> <li><a href="https://en.wikipedia.org/wiki/Netflix_Prize">Netflix Prize</a></li> </ul> <a href="https://konektom.org/tags/68796/" style="font-size: 13px;">&Ouml;ffentliches Tag auf konektom</a><br /> </section> </section> </div> </article>