Name: 用TENSORFLOW TUTORIAL進行深度學習，20分鐘內介紹TensorFlow文法。 (DEEP LEARNING WITH TENSORFLOW TUTORIAL | Introduction to TensorFlow Syntax in 20 Minutes)
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Everyone who uses Python for scientific computing uses NumPy, a third-party package allowing

They are a powerful way of organizing and processing data.

Hence NumPy is a fundamental library for those attempting to manipulate larger chunks of

When your calculations are ready, it is useful to know how to present the results obtained

“Matplotlib” is a two-dimensional plotting library specially designed for visualization

of Python and especially NumPy computations.

It contains a large set of tools allowing you to customize the appearance of the graphs

Finally, TensorFlow is the main library we will use for machine learning.

The good thing about Anaconda is that NumPy and Matplotlib were installed automatically

You don’t have to install the main packages separately as you might have to do if you

were to use some other software for programming in Python.

However, TensorFlow is not included in the automatically installed packages.

That’s a useful programming skill to have.

So, the quickest way to install it is by opening your start menu and searching for the “Anaconda

Type “pip install TensorFlow” in the following way.

“Pip” per sé means either “pip install packages” or “pip installs Python”.

When the operation is done, you will be set up for the whole course.

This is one of the quickest and easiest ways to install modules and packages on your computer

I recommend you to also ‘pip install sklearn’.

Now that we’ve covered the preliminary preparations, we can focus on using Python to create our

Ok  This is our actual TensorFlow intro.

Once again, TensorFlow is a machine learning library developed by Google.

It allows us to construct fairly complicated models with little coding.

To give you a perspective, our practical example required 20 lines of code.

With TensorFlow, it will still be 20 lines of code.

TensorFlow is an amazing framework, and you will see that by the end of the course.

However, it has a peculiar underlying logic.

Remember when you first studied linear algebra or trigonometry?

The logic differed greatly from the mathematics you had seen before, right?

Once you start working with it, it’s super easy, but you must make an extra effort to

The most basic notion we’ll need to define is the computational graph.

Quoting TensorFlow’s ‘About’ section at tensorflow.org, ‘nodes in the graph represent

mathematical operations, while the edges represent the multidimensional data arrays, or tensors,

We won’t be drawing computational graphs, but that’s how such a graph looks

Let’s start coding, and we’ll grasp the rest of the intuition on the go!

Naturally, we’ll start by importing the TensorFlow library.

Then, we will generate fake data, once again.

This code is virtually the same as the one we used before.

There is a single line of code difference.

For each project you work on, you’ll have a dataset.

Perhaps, you are used to xlsx or csv files; however, TensorFlow doesn’t work well with

Therefore, we want a format that can store the information in tensors.

One solution to this problem is npz files.

It allows you to save nd arrays or n-dimensional arrays.

Thinking like computer scientists, we can say tensors can be represented as multidimensional

When we read an npz file, the data is already organized in the desired way.

Often, this is an important part of machine learning preprocessing.

You are given data in a specific file format.

Then you open it, preprocess it, and finally save it into an npz.

Later, you build your algorithm, using the npz, instead of the original file.

As you can see, we have called the inputs and targets we generated: generated inputs

Next, we can simply save them into a tensor friendly file.

The proper way to do that is to use the np savez method.

Then we must indicate the objects we want to save into the file.

The label we want to assign to the nd array equals the array we want to save under that

For us, the label is inputs and is equal to the generated inputs array.

Similarly, the targets are equal to the generated targets.

Note it is not required to call them inputs and targets.

If we would like to, we could call them with arbitrary names, such as Rad1 and Rad2.

Executing the code would save the TF_intro file in the same directory as the Jupyter

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用TENSORFLOW TUTORIAL進行深度學習，20分鐘內介紹TensorFlow文法。 (DEEP LEARNING WITH TENSORFLOW TUTORIAL | Introduction to TensorFlow Syntax in 20 Minutes)

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