Applied Deep Learning – A Case-Based Approach to Understanding Deep Neural Networks

Applied Deep Learning – A Case-Based Approach to Understanding Deep Neural Networks

Why another book on applied deep learning? That is the question I asked myself before starting to write this volume. After all, do a Google search on the subject, and you will be overwhelmed by the huge number of results. The problem I encountered, however, is that I found material only to implement very basic models on very simple datasets. Over and over again, the same problems, the same hints, and the same tips are offered. If you want to learn how to classify the Modified National Institute of Standards and Technology (MNIST) dataset of ten handwritten digits, you are in luck. (Almost everyone with a blog has done that, mostly copying the code available on the TensorFlow web site). Searching for 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 there was a need to fill this gap. I spent hours trying to debug models for reasons as silly as having the labels wrong. For example, instead of 0 and 1, I had 1 and 2, but no blog warned me about that. It is important to conduct a proper metric analysis when developing models, but no one teaches you how (at least not in material that is easily accessible). 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 have always tried to 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. Therefore, I always explain concepts with real examples and with fully fledged and tested Python code that you can reuse. Note that the goal of this book is not to make you a Python or TensorFlow expert, or someone who 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 most often used by practitioners, so it makes sense to choose them. If you must learn, better that it be something you can use in your own projects and for your own career.

The goal of this book is to let you see more advanced material with new eyes. I cover the mathematical background as much as I can, because I feel that it is necessary for a complete understanding of the difficulties and reasoning behind many concepts. You cannot comprehend why a large learning rate will make your model (strictly speaking, the cost function) diverge, if you don’t know how the gradient descent algorithm works mathematically. In all real-life projects, you will not have to calculate partial derivatives or complex sums, but you will have to understand them to be able to evaluate what can work and what cannot (and especially why). Appreciating why a library such as TensorFlow makes your life easier is only possible if you try to develop a trivial model with one neuron from scratch. It is a very instructive thing to do, and I will show you how in Chapter 10. Once you have done it once, you will remember it forever, and you will really appreciate libraries such as TensorFlow.

In Chapter 1, you will learn how to set up your Python environment and what computational graphs are. I will discuss some basic examples of mathematical calculations performed using TensorFlow. In Chapter 2, we will look at what you can do with a single neuron. I will cover what an activation function is and what the most used types, such as sigmoid, ReLU, or tanh, are. I will show you how gradient descent works and how to implement logistic and linear regression with a single neuron and TensorFlow. In Chapter 3, we will look at fully connected networks. I will discuss matrix dimensions, what overfitting is, and introduce you to the Zalando dataset. We will then build our first real network with TensorFlow and start looking at more complex variations of gradient descent algorithms, such as mini-batch gradient descent. We will also look at different ways of weight initialization and how to compare different network architectures. In Chapter 4, we will look at dynamic learning rate decay algorithms, such as staircase, step, or exponential decay, then I will discuss advanced optimizers, such as Momentum, RMSProp, and Adam. I will also give you some hints on how to develop custom optimizers with TensorFlow. In Chapter 5, I will discuss regularization, including such well-known methods as l1,l2, dropout, and early stopping. We will look at the mathematics behind these methods and how to implement them in TensorFlow. In Chapter 6, we will look at such concepts as human-level performance and Bayes error. Next, I will introduce a metric analysis workflow that will allow you to identify problems having to do with your dataset. Additionally, we will look at k-fold cross-validation as a tool to validate your results. In Chapter 7, we will look at the black box class of problems and what hyperparameter tuning is. We will look at such algorithms as grid and random search and at which is more efficient and why. Then we will look at some tricks, such as coarse-to-fine optimization. I have dedicated most of the chapter to Bayesian optimization—how to use it and what an acquisition function is. I will offer a few tips, such as how to tune hyperparameters on a logarithmic scale, and then we will perform hyperparameter tuning on the Zalando dataset, to show you how it may work. In Chapter 8, we will look at convolutional and recurrent neural networks. I will show you what it means to perform convolution and pooling, and I will show you a basic TensorFlow implementation of both architectures. In Chapter 9, I will give you an insight into a real-life research project that I am working on with the Zurich University of Applied Sciences, Winterthur, and how deep learning can be used in a less standard way. Finally, in Chapter 10, I will show you how to perform logistic regression with a single neuron in Python—without using TensorFlow—entirely from scratch.

I hope you enjoy this book and have fun with it.

Umberto Michelucci

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