Practical Deep Learning
teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.
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,
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 Neighbors, 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.
The perfect introduction to this dynamic, ever-expanding field,
will give you the skills and confidence to dive into your own machine learning projects.