Definitions of what constitutes deep learning vary, but for most practical purposes it is the use of an artificial neural network that is multiple layers deep. Deep learning has proven to be a very effective tool for speech and vision tasks, such as converting speech to text and classifying images, and can be a useful tool for many others. It tends to be a very empirical approach, so you won't have to learn very much theory in order to use deep learning effectively.
Recommended Books

Deep Learning
Ian Goodfellow, Yoshua Bengio and Aaron Courville
Key Features
Key Topics
 Approximate Inference
 Autoencoders
 BackPropagation
 Computer Vision
 Convolutional Networks
 Data Augmentation
 Dropout
 Early Stopping
 Feedforward Networks
 Generative Models
 GradientBased Learning
 Hyperparameter Selection
 Initialization
 Linear Algebra
 Linear Factor Models
 Long ShortTerm Memory Nets
 Machine Learning Basics
 Monte Carlo Methods
 Optimization
 Parameter Norm Penalties
 Pooling
 Probability Theory
 Recurrent Neural Networks
 Regularization
 Representation Learning
 SemiSupervised Learning
 Sequence Modeling
 Structured Probabilistic Models
Description
Deep learning is a relatively new field, and there aren't a lot of books that are geared specifically toward it. This book is divided into three parts. The first part, an introduction to machine learning concepts, is sufficient to get you started in deep learning. The second part, by far the strongest, describes the construction, training, and methods for improving common deep learning models. The third part consists of a collection of research topics in deep learning, and it probably is not terribly useful for most practitioners. There is a book website that purports to have errata and exercises but, as of December 2018, there is only one exercise and the errata is insubstantive. There's also no example code. Overall this is a useful book for learning about deep learning, but you'll need a practical tutorial to implement the ideas.

Deep Learning with Python
François Chollett
Key Features
 Example Python code
Key Topics
 Binary Classification
 Computer Vision
 Convolutional Neural Nets
 Feature Engineering
 Generative Adversarial Networks (GANs)
 Generative Models
 GradientBased Optimization
 Keras
 Keras Functional API
 Long ShortTerm Memory (LSTM)
 Machine Learning Fundamentals
 Mathematical Building Blocks of Neural Networks
 Multiclass Classification
 Overfitting and Underfitting
 Recurrent Neural Networks
 Regression
 Setting up a Deep Learning Workstation
 Tensor Operations
 Variational Autoencoders
 Working with Text Data
Description
This is a very applied book written by the author of one of the most popular deep learning libraries. There are no exercises, but much of the book is devoted to taking you through code applications, so you may not need them. The downside of this book is that it's devoted to a particular library (Keras), and there may be options that would serve you better. However, it's not all that difficult to transition to a different library once you are proficient in another.