Practical Deep Learning

<b><i>Practical Deep Learning</i> teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.</b><br><br>If you&#146;ve been curious about machine learning but didn&#146;t know where to start this is the book you&#146;ve been waiting for. Focusing on the subfield of machine learning known as <i>deep learning</i> it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits <i>Practical Deep Learning</i> teaches you the why of deep learning and will inspire you to explore further.<br>&#160;<br>All you need is basic familiarity with computer programming and high school math&#151;the book will cover the rest. After an introduction to Python you&#146;ll move through key topics like how to build a good training dataset work with the scikit-learn and Keras libraries and evaluate your models&#146; performance.<br>&#160;<br>You&#146;ll also learn:<br><li>How to use classic machine learning models like k-Nearest Neighbors Random Forests and Support Vector Machines</li><li>How neural networks work and how they&#146;re trained</li><li>How to use convolutional neural networks</li><li>How to develop a successful deep learning model from scratch</li>&#160;<br>You&#146;ll conduct experiments along the way building to a final case study that incorporates everything you&#146;ve learned.&#160;<br>&#160;<br>The perfect introduction to this dynamic ever-expanding field <i>Practical Deep Learning</i> will give you the skills and confidence to dive into your own machine learning projects.