Education is the most powerful weapon which you can use to change the world.
BUY THE BOOK
Why write a book on applied deep learning? After all, try a google search on the subject and you will be overwhelmed by the huge number of results. The problem is that there is no course, blog or book that teaches in a consolidated and beginner friendly way advanced subjects like regularization, advanced optimisers as Adam or RMSProp, mini-batches gradient descent, dynamical learning rate decay, dropout, hyperparameter search, bayesian optimisation, metric analysis and so on.
I found material (and typically of very bad quality) only to implement very basic models on very simple datasets. If you want to learn how to classify the MNIST (hand written digits) dataset of 10 digits you are in luck (almost everyone with a blog havs done that, mostly copying the code you find on the tensorflow website). Searching something else to learn how logistic regression works? Not so easy. How to prepare a dataset to perform an interesting binary classification? Even more difficult.
I felt the need of filling this gap. I spent hours trying to debug models for reasons as dumb as having the labels wrong: instead of 0 and 1 I had 1 and 2, but no blog warned me about that. Is important to do a proper metric analysis when developing your models, but nobody is teaching you how (at least not on easy to access material). This gap needed to be filled. I find that covering more complex examples from data preparation to error analysis is a very efficient and fun way to learn the right techniques. In this book, I will always cover complete and complex examples to explain concepts that are not so easy to understand in any other way. It is not possible to understand why it is important to choose the right learning rate if you don’t see what can happen when you select the wrong value for example. Note that the goal of this course is not to make you a Python or tensorflow expert, or someone that can develop new complex algorithms. Python and tensorflow are simply tools that are very well suited to develop models and get results quickly. Therefore, I use them. I could have used other tools, but those are the ones mostly used by practitioners, so it makes sense to choose them.
Advanced Applied Deep Learning
Convolutional Neural Networks and Object Detection
Published: Springer Nature / Apress (2019)
APRESS website: https://www.apress.com/gp/book/9781484249758
BUY THE BOOK
Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow.
Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models.
Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level.
What You Will Learn
- See how convolutional neural networks and object detection work
- Save weights and models on disk
- Pause training and restart it at a later stage
- Use hardware acceleration (GPUs) in your code
- Work with the Dataset TensorFlow abstraction and use pre-trained models and transfer learning
- Remove and add layers to pre-trained networks to adapt them to your specific project
- Apply pre-trained models such as Alexnet and VGG16 to new datasets
Who This Book Is For
Scientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.