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  <div class="vevent h-event"> <h1 class="summary p-name">Introduction to Deep Learning With Tensorflow and Keras</h1> <div class='date'><time class="dtstart dt-start" title="2018-01-27T09:00:00" datetime="2018-01-27T09:00:00">Saturday, January 27, 2018 from 9am</time>–<time class="dtend dt-end" title="2018-01-27T13:00:00" datetime="2018-01-27T13:00:00">1pm</time></div> <div class="location vcard p-location h-card"> <a class="url" href='http://calagator.org/venues/202395673'><span class='fn org p-name'>Alchemy Code Lab</span></a> <div class="adr p-adr h-adr"> <div class="street-address p-street-address">30 NW 10th Ave.</div> <span class="locality p-locality">Portland</span> , <span class="region p-region">OR</span> <span class="postal-code p-postal-code">97209</span> (<a href='https://maps.google.com/maps?q=30%20NW%2010th%20Ave.,%20Portland%20OR%2097209%20'>map</a>) </div> </div> <div class="description p-description"> <h1>Introduction to Deep Learning with Tensorflow and Keras</h1> <p>Deep Learning (DL) is a fast growing and exciting sub-field of machine learning and AI that has recently made extraordinary advancements in many applications such as image recognition, natural language processing, speech transcription, classification and forecasting.</p> <p>This 2 day introductory course focuses on giving you a firm foundation of deep learning techniques using a combination of presentations and hands-on exercises. The presentations introduce essential background and theory and allow for questions and discussions. And the exercises use Jupyter notebooks with Keras and Tensorflow to show you the essentials of building and training neural nets.</p> <p>Deep learning can transform any industry. This course will help you find creative ways to apply it to yours.</p> <h2>You'll learn:</h2> <ul> <li>The fundamentals of deep learning including <ul> <li>how to build dense / fully connected networks</li> <li>how to use convolutional layers</li> <li>when to use recurrent LSTM/GRU networks</li> <li>how to choose an optimizer and loss function</li> <li>when to use dropout and batch normalization</li> <li>how to prepare training and test data</li> <li>and more</li> </ul> </li> <li>How to navigate the options and decisions for different model architectures.</li> <li>Efficiently use and fine-tune pre-trained models.</li> </ul> <h2>So that you can:</h2> <ul> <li>identify important and appropriate questions</li> <li>collect and prepare real world data</li> <li>design and train effective models</li> </ul> <p>... to get the answers you need for your business and career.</p> <h2>You'll get the most out of this course if:</h2> <ul> <li>You have a familiarity with Python and Numpy.</li> <li>You have a deep curiosity and interest in learning about machine learning.</li> <li>You want to understand and work with cutting edge deep learning approaches with complex data.</li> </ul> <p><strong>Note: You don't need to be a math wizard</strong> - this is a hands on practical class. Though knowing more is always helpful, deep understanding of linear algebra and and calculus are not necessary. We will cover all the math basics necessary to get started in the class.</p> <h2>Topics</h2> <p>Over the 2 days we will cover</p> <ol> <li>Deep Learning Introduction and Overview</li> <li>Dense NNs and Keras</li> <li>Essential Theory</li> <li>Convolutional NNs</li> <li>Data Preparation</li> <li>Keras API Deep Dive</li> <li>Transfer Learning</li> <li>Advanced Architectures</li> <li>Introduction to Tensorflow</li> <li>Practical Training Tips</li> <li>Tensorflow Deep Dive</li> <li>Production and Deployment</li> </ol> <h2>Preparation</h2> <p>If you'd like to work with software installed on your own laptop we suggest you download and install <a href="https://conda.io/docs/">Conda</a> and use to to install Python3, Numpy, Pandas, Jupyter, Tensorflow and Keras.</p> <p>A <a href="https://www.docker.com">docker</a> image with the necessary software pre-installed will also be made available.</p> <p>If you can't install Conda or Docker, let us know when you register and we will strive to provide access to a running Jupyter Notebook instance.</p> <p>Also, feel free to bring a CSV of a data set you are interested in exploring and a description of what you'd like to predict.</p> <h2>Schedule</h2> <p>Saturday Jan 27 and Feb 3rd, 9AM - 1PM</p> <p>Alchemy Code Lab 30 NW 10th Ave Portland, OR 97209 alchemycodelab.com</p> <h2>More information and future classes</h2> <p>For more information and information on future classes please contact Julio and sign up for announcements at <a href="https://e-string.com/training/">https://e-string.com/training/</a></p> </div> <h3>Links</h3> <ul> <li><a class="url u-url" href="https://www.eventbrite.com/e/introduction-to-deep-learning-with-tensorflow-and-keras-2-day-workshop-tickets-41893286949">Website</a></li> </ul> </div>