docs/august2018.html

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        <b>Full Stack Deep Learning</b>
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      <h1 class="text-center">Full Stack Deep Learning Bootcamp</h1>
      <p class="lead text-center">Hands-on program for developers familiar with the basics of deep learning</p>
      <div class="row">
        <div class="col-md-12">
          <h2 class="my-2">Why</h2>
          <p class="">There are great online courses on how to train deep learning models. Some start with theory, some start with code. But training the model is just one part of shipping a complete deep learning project. Our course is aimed at people who already
            know the basics, and want experience with the full stack.</p>
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          <h2 class="my-2">Content</h2>
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      <p class="">Attendees will learn and get first-hand experience with best practices of all components of a deep learning project:</p>
      <ul class="">
        <li>Formulating the
          <b>problem</b> and estimating project
          <b>cost</b>
        </li>
        <li>Finding, cleaning, labeling, and augmenting
          <b>data</b>
        </li>
        <li>Picking the right
          <b>framework</b> and compute
          <b>infrastructure</b>
        </li>
        <li>Finding the right model
          <b>architecture</b> by running experiments
          <b>in parallel</b></li>
        <li>
          <b>Troubleshooting</b> training and ensuring <b>reproducibility</b></li>
        <li>
          <b>Deploying</b> the model at scale</li>
        <li>Keeping up with fast-moving research areas such as <b>meta-learning</b></li>
      </ul>
      <h2 class="my-2">Project</h2>
      <p class="">Attendees will spend
        <b>30%</b> of the time on developing and deploying a state-of-the-art <b>computer vision</b> and <b>natural language processing</b> system.</p>
      <ul>
        <li>Use the
          <b>most important building blocks</b> of deep learning: convnets, sequence models, and attention</li>
        <li>Receive
          <b>feedback and support</b> along the way</li>
        <li>Learn
          <b>best practices</b> for running experiments</li>
        <li>
          <b>Deploy</b> the final model for widespread use</li>
      </ul>
      <h2 class="my-2">Certification</h2>
      <p class="">Those on the job market can choose to take a
        <b>certification exam</b>, aimed to ensure that you are ready for deep learning engineer technical interviews. The exam covers pre-requisites as well as course content, and can be taken on your own time.</p>
      <h2 class="my-2">Instructors</h2>
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          <h4 class="text-center my-1">Pieter Abbeel</h4>
          <p>Professor at
            <b>UC Berkeley</b>, President of
            <b>Covariant.AI</b>
          </p>
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        <div class="col-sm-4 col-md-3">
          <img class="img-fluid rounded-circle" src="old/assets/sergey.jpeg">
          <h4 class="text-center my-1">Sergey Karayev</h4>
          <p>Director of AI at
            <b>Gradescope</b>
          </p>
        </div>
        <div class="col-sm-4 col-md-3">
          <img class="img-fluid rounded-circle" src="old/assets/josh.jpeg">
          <h4 class="text-center my-1">Josh Tobin</h4>
          <p>Research Scientist at
            <b>OpenAI</b>
          </p>
        </div>
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      <h4 class="">Guest Lecturers</h4>
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          <h4 class="text-center my-1">Andrej Karpathy</h4>
          <p>Director of AI at
            <b>Tesla</b><br />
            <small>Previously Research Scientist at <b>OpenAI</b></small>
          </p>
        </div>
        <div class="col-sm-6 col-md-3">
          <img class="img-fluid rounded-circle" src="old/assets/jai.jpeg">
          <h4 class="text-center my-1">Jai Ranganathan</h4>
          <p>Director of Product at
            <b>Uber</b>
            <br />
            <small>
              Previously Senior Director of Product at <b>Cloudera</b>, Senior Product Manager at <b>VMWare</b>
            </small>
          </p>
        </div>
        <div class="col-sm-6 col-md-3">
          <img class="img-fluid rounded-circle" src="old/assets/yq.jpeg">
          <h4 class="text-center my-1">Yangqing Jia</h4>
          <p>Director of AI Infrastructure at
            <b>Facebook</b>
            <br />
            <small>
              Developed the <b>Caffe</b> framework at <b>UC Berkeley</b>, helped develop <b>Tensorflow</b> at <b>Google</b>
            </small>
          </p>
        </div>
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          <h4 class="text-center my-1">Lukas Biewald</h4>
          <p>Founder of
            <b>Weights &amp; Biases</b>
            <br />
            <small>Previously founded <b>Figure Eight (Crowdflower)</b></small>
          </p>
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          <h2 class="my-2">Schedule</h2>
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          <p class="">Any time within 4 weeks of the bootcamp, attendees can choose to take the certification exam, aimed at showing requisite knowledge for the position of deep learning engineer. A study guide will be part of the program materials.</p>
          <h2 class="my-2">Pre-requisites</h2>
          <ul class="">
            <li>At least one year experience programming in Python.</li>
            <li>At least one deep learning course taken online or in higher-ed.</li>
            <li>Experience with code versioning, Unix environment, and software engineering.</li>
          </ul>
          <h2 class="">Logistics</h2>
          <p class="">The bootcamp will be held Friday-Sunday
              <b>August 3-5, 2018</b> on
              <b>UC Berkeley campus</b>. The three-day bootcamp cost is
            <b>$2450</b>, with a discount for students.</p>
          <p>At most <b>150 people</b> will be admitted to the program. We will carefully read every application. Admission will be on a rolling basis, so applying earlier will improve your chances.</p>
          <p><b>Update (July 10)</b>: The event is officially full. Feel free to fill out the application to be first in line regarding any future offerings! Or simply sign up for opportunities at <a href="https://fullstackdeeplearning.com">https://fullstackdeeplearning.com</a>.</p>
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