If you are curious about artificial intelligence and machine learning but don't know where to start, this is the book you've been waiting for. Focusing on the subfield of machine learning called deep learning, it explains the fundamental concepts and provides you with the foundations you need to start creating your own models.

Instead of simply listing recipes for using existing tools, Practical Deep Learning, 2nd Edition teaches you the why behind deep learning and will inspire you to explore further. All you need is basic programming and high school mathematics knowledge – the book will cover the rest.

After an introduction to Python, you will move on to core topics such as how to create a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models' performance.

You will learn: how to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines, how neural networks work and are trained, how to use convolutional neural networks, and how to develop a successful deep learning model from scratch. You will run experiments along the way, building towards a final case study that incorporates everything you've learned.

This second edition has been fully revised and updated, adding six new chapters to advance your exploration of deep learning from basic CNNs to more advanced models. The new chapters cover detailed tuning, transfer learning, object detection, semantic segmentation, multitask learning, unsupervised 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.

Pages: 624, Dimensions: 17.7x17.7cm