If you've been curious about artificial intelligence and machine learning but didn't know where
to start this is the book you've been waiting for. Focusing on the subfield of machine
learning known as deep learning 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 Practical Deep Learning 2nd Edition teaches you the why of deep learning and will
inspire you to explore further. All you need is basic familiarity with computer programming and
high school math - the book will cover the rest. After an introduction to Python you'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' performance. You'll also learn: How to use classic
machine learning models like k-Nearest Neighbours Random Forests and Support Vector Machines
How neural networks work and how they're trained How to use convolutional neural networks How
to develop a successful deep learning model from scratch. You'll conduct experiments along the
way building to a final case study that incorporates everything you've learned. This second
edition is thoroughly revised and updated and adds six new chapters to further your
exploration of deep learning from basic CNNs to more advanced models. New chapters cover fine
tuning transfer learning object detection semantic segmentation multilabel classification
self-supervised learning generative adversarial networks and large language models. The
perfect introduction to this dynamic ever-expanding field Practical Deep Learning 2nd
Edition will give you the skills and confidence to dive into your own machine learning
projects.
