深度学习实战：Keras快速入门与进阶指南

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"Learn Keras for Deep Neural Networks" 是一本旨在教授读者如何使用Keras框架构建深度神经网络的书籍。作者Jojo Moolayil通过数学和编程友好的方式，帮助读者快速掌握实用的深度学习概念。这本书分为三个部分，每个部分包含两个章节，涵盖了从基础到进阶的深度学习知识。在第一部分，读者将被引入深度学习的世界，了解它与机器学习的区别，探讨用于深度学习的各种框架，并深入理解Keras生态系统。书中通过解决实际的商业问题来展示监督学习算法如何利用深度神经网络，包括一个针对回归问题的案例和一个针对分类问题的案例，这些案例基于热门的Kaggle数据集。第二部分则涉及深度学习中的关键环节——超参数调优。这部分内容帮助读者优化模型，以构建更为健壮的深度学习应用。通过实践，读者可以掌握如何调试和验证深度学习模型，提升模型性能。最后一部分，作者引导读者深入了解深度学习领域的最新发展和研究，让读者能够跟进行业动态，扩展对深度学习原理的理解，并将其应用到其他流行框架中。通过阅读此书，读者将能够： 1. 掌握快速的深度学习实践概念，利用Keras框架设计、开发、训练、验证和部署深度神经网络。 2. 学习并运用最佳实践来调试和验证深度学习模型，确保模型质量和准确性。 3. 学会将深度学习集成到更大的软件服务或产品中，实现深度学习即服务（DLaaS）。 4. 理解如何将深度学习原理扩展到TensorFlow、PyTorch等其他流行框架，增强技术的可迁移性。本书适合具备基本编程技能的软件工程师和数据工程师，无论他们是为了职业转型还是企业项目，都可从中受益，系统地学习深度学习并应用到实际项目中。

Examples for ML could be a system that could predict whether

a student will fail or pass in a test by learning from the historical test

results and student attributes. Here, the system is not encoded with a

comprehensive list of all possible rules that can decide whether a student

will pass or fail; instead, the system learns on its own based on the patterns

it learned from the historical data.

So, where does DL stand within this context? It happens that while ML

works very well for a variety of problems, it fails to excel in some specific

cases that seem to be very easy for humans: say, classifying an image as a

cat or dog, distinguishing an audio clip as of a male or female voice, and

so on. ML performs poorly with image and other unstructured data types.

Upon researching the reasons for this poor performance, an inspiration

led to the idea of mimicking the human brain’s biological process, which

is composed of billions of neurons connected and orchestrated to adapt

to learning new things. On a parallel track, neural networks had already

been a research topic for several years, but only limited progress had been

made due to the computational and data limitations at the time. When

researchers reached the cusp of ML and neural networks, there came the

field of DL, which was framed by developing deep neural networks (DNNs),

that is, improvised neural networks with many more layers. DL excelled at

the new frontiers where ML was falling behind. In due course, additional

research and experimentation led to the understanding of where we could

leverage DL for all ML tasks and expect better performance, provided there

was surplus data availability. DL, therefore, became a ubiquitous field

to solve predictive problems rather than just being confined to areas of

computer vision, speech, and so on.

Today, we can leverage DL for almost all use cases that were earlier

solved using ML and expect to outperform our previous achievements,

provided that there is a surplus of data. This realization has led to

distinguishing the order of the fields based on data. A new rule of thumb

was established: ML would not be able to improve performance with

increased training data after a certain threshold, whereas DL was able to

Chapter 1 an IntroduCtIon todeep LearnIng andKeras

The nature of the learning process in the brain is quite intuitive. Rather

than learning the structure of the face to identify people, the brain learns

the deviation from a generic face (e.g., how different an individual’s eyes

are from the reference eye), which can then be quantified as an electrical

signal with a defined strength. Similarly, it learns deviations from all parts

of the face from a reference base, combines these deviations into new

dimensions, and finally gives an output. All of this happens so quickly that

none of us realizes what our subconscious mind has actually done.

Similarly, the neural network showcased in the preceding illustration

tries to mimic the same process using a mathematical approach. The

input is consumed by neurons in the first layer and an activation function

is calculated within each neuron. Based on a simple rule, it forwards an

output to the next neuron, similar to the deviations learned by the human

brain. The larger the output of a neuron, the larger the significance of that

input dimension will be. These dimensions are then combined in the next

layer to form additional new dimensions, which we probably can’t make

sense of. But the system learns it intuitively. The process, when multiplied

several times, develops a complex network with several connections.

Now that the structure of the neural network is understood, let’s

understand how the learning happens. When we provide the input data

to the defined structure, the end output would be a prediction, which

could be either correct or incorrect. Based on the output, if we provide

a feedback to the network to adapt better by using some means to make

a better prediction, the system learns by updating the weight for the

connections. To achieve the process of providing feedback and defining

the next step to make changes in the correct way, we use a beautiful

mathematical algorithm called “backpropagation.” Iterating the process

several times step by step, with more and more data, helps the network

update the weights appropriately to create a system where it can make a

decision for predicting output based on the rules it has created for itself

through the weights and connections.

Chapter 1 an IntroduCtIon todeep LearnIng andKeras

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