Hands-On Machine Learning with Scikit-Learn and TensorFlow: by Aurélien Géron

By Aurélien Géron

Through a chain of contemporary breakthroughs, deep studying has boosted the complete box of computing device studying. Now, even programmers who recognize just about not anything approximately this know-how can use uncomplicated, effective instruments to enforce courses able to studying from facts. This useful publication exhibits you how.

By utilizing concrete examples, minimum idea, and production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron is helping you achieve an intuitive knowing of the strategies and instruments for construction clever structures. You’ll examine various options, beginning with basic linear regression and progressing to deep neural networks. With routines in each one bankruptcy that will help you follow what you’ve discovered, all you wish is programming adventure to get started.

  • Explore the laptop studying panorama, relatively neural nets
  • Use scikit-learn to trace an instance machine-learning undertaking end-to-end
  • Explore numerous education versions, together with help vector machines, choice timber, random forests, and ensemble methods
  • Use the TensorFlow library to construct and teach neural nets
  • Dive into neural web architectures, together with convolutional nets, recurrent nets, and deep reinforcement learning
  • Learn innovations for education and scaling deep neural nets
  • Apply functional code examples with out buying over the top laptop studying concept or set of rules details

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Additional resources for Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

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Is it suddenly smarter? In this chapter we will start by clarifying what Machine Learning is and why you may want to use it. Then, before we set out to explore the Machine Learning continent, we will take a look at the map and learn about the main regions and the most notable landmarks: supervised versus unsupervised learning, online versus batch learning, instance-based versus model-based learning. Then we will look at the workflow of a typical ML project, discuss the main challenges you may face, and cover how to evaluate and fine-tune a Machine Learning system.

Figure 1-21. A more representative training sample If you train a linear model on this data, you get the solid line, while the old model is represented by the dotted line. As you can see, not only does adding a few missing countries significantly alter the model, but it makes it clear that such a simple linear model is probably never going to work well. It seems that very rich countries are not happier than moderately rich countries (in fact they seem unhappier), and conversely some poor countries seem happier than many rich countries.

It will run much faster, the data will take up less disk and memory space, and in some cases it may also perform better. Yet another important unsupervised task is anomaly detection — for example, detecting unusual credit card transactions to prevent fraud, catching manufacturing defects, or automatically removing outliers from a dataset before feeding it to another learning algorithm. The system is trained with normal instances, and when it sees a new instance it can tell whether it looks like a normal one or whether it is likely an anomaly (see Figure 1-10).

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