樹莓派 3A+ 可以用來打造一個初學者派生程式學習環境︰

Thonny

Credits

Main development of Thonny took place in Institute of Computer Science ofUniversity of Tartu, Estonia.

Development of several features in Thonny 3.0 was proposed and supported by Raspberry Pi Foundation.

Since September 2018 development of Thonny is partially supported by Cybernetica AS.

We are also grateful for the help of several contributors from the open-source community around the world.

 

開始動手寫程式︰

 

踏上通往人工智慧之大道︰

《W!O+ 的《小伶鼬工坊演義》︰神經網絡與深度學習【引言】》

對一本小巧完整而且寫的好的書，又該多說些什麼的呢？於是幾經思慮，就講講過去之閱讀隨筆與念頭雜記的吧！終究面對一個既舊也新的議題，尚待火石電光激發創意和發想。也許只需一個洞見或將改變人工智慧的未來乎？？

Michael Nielsen 先生開宗明義在首章起頭

The human visual system is one of the wonders of the world. Consider the following sequence of handwritten digits:

Most people effortlessly recognize those digits as 504192. That ease is deceptive. In each hemisphere of our brain, humans have a primary visual cortex, also known as V1, containing 140 million neurons, with tens of billions of connections between them. And yet human vision involves not just V1, but an entire series of visual cortices – V2, V3, V4, and V5 – doing progressively more complex image processing. We carry in our heads a supercomputer, tuned by evolution over hundreds of millions of years, and superbly adapted to understand the visual world. Recognizing handwritten digits isn’t easy. Rather, we humans are stupendously, astoundingly good at making sense of what our eyes show us. But nearly all that work is done unconsciously . And so we don’t usually appreciate how tough a problem our visual systems solve.

The difficulty of visual pattern recognition becomes apparent if you attempt to write a computer program to recognize digits like those above. What seems easy when we do it ourselves suddenly becomes extremely difficult. Simple intuitions about how we recognize shapes – “a 9 has a loop at the top, and a vertical stroke in the bottom right” – turn out to be not so simple to express algorithmically. When you try to make such rules precise, you quickly get lost in a morass of exceptions and caveats and special cases. It seems hopeless.

Neural networks approach the problem in a different way. The idea is to take a large number of handwritten digits, known as training examples,

 

and then develop a system which can learn from those training examples. In other words, the neural network uses the examples to automatically infer rules for recognizing handwritten digits. Furthermore, by increasing the number of training examples, the network can learn more about handwriting, and so improve its accuracy. So while I’ve shown just 100 training digits above, perhaps we could build a better handwriting recognizer by using thousands or even millions or billions of training examples.

In this chapter we’ll write a computer program implementing a neural network that learns to recognize handwritten digits. The program is just 74 lines long, and uses no special neural network libraries. But this short program can recognize digits with an accuracy over 96 percent, without human intervention. Furthermore, in later chapters we’ll develop ideas which can improve accuracy to over 99 percent. In fact, the best commercial neural networks are now so good that they are used by banks to process cheques, and by post offices to recognize addresses.

We’re focusing on handwriting recognition because it’s an excellent prototype problem for learning about neural networks in general. As a prototype it hits a sweet spot: it’s challenging – it’s no small feat to recognize handwritten digits – but it’s not so difficult as to require an extremely complicated solution, or tremendous computational power. Furthermore, it’s a great way to develop more advanced techniques, such as deep learning. And so throughout the book we’ll return repeatedly to the problem of handwriting recognition. Later in the book, we’ll discuss how these ideas may be applied to other problems in computer vision, and also in speech, natural language processing, and other domains.

Of course, if the point of the chapter was only to write a computer program to recognize handwritten digits, then the chapter would be much shorter! But along the way we’ll develop many key ideas about neural networks, including two important types of artificial neuron (the perceptron and the sigmoid neuron), and the standard learning algorithm for neural networks, known as stochastic gradient descent. Throughout, I focus on explaining why things are done the way they are, and on building your neural networks intuition. That requires a lengthier discussion than if I just presented the basic mechanics of what’s going on, but it’s worth it for the deeper understanding you’ll attain. Amongst the payoffs, by the end of the chapter we’ll be in position to understand what deep learning is, and why it matters.

………