توضیحات
The Hundred-Page Machine Learning Book
Preface
Let’s start by telling the truth: machines don’t learn. What a typical “learning machine”
does, is finding a mathematical formula, which, when applied to a collection of inputs (called
“training data”), produces the desired outputs. This mathematical formula also generates the
correct outputs for most other inputs (distinct from the training data) on the condition that
those inputs come from the same or a similar statistical distribution as the one the training
data was drawn from
Why isn’t that learning? Because if you slightly distort the inputs, the output is very likely
to become completely wrong. It’s not how learning in animals works. If you learned to play
a video game by looking straight at the screen, you would still be a good player if someone
rotates the screen slightly. A machine learning algorithm, if it was trained by “looking”
straight at the screen, unless it was also trained to recognize rotation, will fail to play the
game on a rotated screen
So why the name “machine learning” then? The reason, as is often the case, is marketing
Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence,
coined the term in 1959 while at IBM. Similarly to how in the 2010s IBM tried to market
the term “cognitive computing” to stand out from competition, in the 1960s, IBM used the
new cool term “machine learning” to attract both clients and talented employees
As you can see, just like artificial intelligence is not intelligence, machine learning is not
learning. However, machine learning is a universally recognized term that usually refers
to the science and engineering of building machines capable of doing various useful things
without being explicitly programmed to do so. So, the word “learning” in the term is used
by analogy with the learning in animals rather than literally
Who This Book is For
This book contains only those parts of the vast body of material on machine learning developed
since the 1960s that have proven to have a significant practical value. A beginner in machine
learning will find in this book just enough details to get a comfortable level of understanding
of the field and start asking the right questions.
Practitioners with experience can use this book as a collection of directions for further
self-improvement. The book also comes in handy when brainstorming at the beginning of a
project, when you try to answer the question whether a given technical or business problem
is “machine-learnable” and, if yes, which techniques you should try to solve it
سلام دوستان گرامی و عزیز فروشگاه فایل فیل استورتمامی کتاب های نسخه انگلیسی موجود در فروشگاه فیل استوربا قیمت دلارفروخته می شود در فروشگاه های مطرح مثل امازون اما ما این کتاب ها را با قیمت ریالی می فروشیم مثلا اگر کتاب 100 دلار است ما این کتاب را با قیمت 100 هزار تومان قرار دادیم یعنی یعنی با دلار زیر 1000هزار تومان امیدواریم که شما دوستان از فروشگاه فایل ما حمایت لازم را داشته باشید
این کتاب در سایت امازون با قیمت 34 دلار به فروش می رسدلینک کتاب در امازون
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