Cocalc 01 Interact Ipynb

In this lecture we will begin to learn about creating dashboard-type GUI with iPython widgets! The interact function (ipywidgets.interact) automatically creates user interface (UI) controls for exploring code and data interactively. It is the easiest way to get started using IPython's widgets. At the most basic level, interact auto-generates UI controls for function arguments, and then calls the function with those arguments when you manipulate the controls interactively. To use interact, you need to define a function that you want to explore. Here is a function that prints its only argument x.

When you pass this function as the first argument to interact along with an integer keyword argument (x=10), a slider is generated and bound to the function parameter. Note that the semicolon here just prevents an out cell from showing up. When you move the slider, the function is called, which prints the current value of x. CoCalc: Collaborative Calculations and Data Science The basic behavior is as follows: Interact provides a series of widgets. Each widget has an output that can be directly inspected or used to trigger some callbacks (i.e.

run some code as soon as the widget changes value): the abstract supertype that gives this behavior is called AbstractObservable. Let's see this in practice. Note that display works in a Jupyter notebook or in Atom/Juno IDE. Interact can also be deployed in Jupyter Lab, but that requires installing an extension first: To deploy the app as a standalone Electron window, one would use Blink.jl: The app can also be served in a webpage via Mux.jl:

The value of our button can be inspected using getindex: You can use NetworkX to construct and draw graphs that are undirected or directed, with weighted or unweighted edges. An array of functions to analyze graphs is available. This tutorial takes you through a few basic examples and exercises. Note that many exercises are followed by a block with some assert statements. These assertions may be preceded by some setup code.

They are provided to give you feedback that you are on the right path -- receiving an AssertionError probably means you've done something wrong. https://networkx.github.io/documentation/networkx-2.2/ https://networkx.github.io/documentation/networkx-2.2/tutorial.html Recall that import statements go at the top of your code, telling Python to load an external module. In this case we want to load NetworkX, but give it a short alias nx since we'll have to type it repeatedly, hence the as statement. Interact.jl provides interactive widgets for IJulia.

Interaction relies on Observables.jl reactive programming package. Observables provides the type Observable which represent time-varying values. For example, a Slider widget can be turned into a "observable of numbers". Execute the following two cells, and then move the slider. You will see that the value of observe(s) changes accordingly. Let us now inspect the types of these entities.

You can have many instances of the same widget in a notebook, and they stay in sync: A slider is useless if you cannot do more with it than just watch its value. Thankfully we can transform one signal into another, which means we can transform the signal of values that the slider takes into, say a signal of it's squares: Go ahead and vary the slider to see this in action. Hello, and thank you for letting us join you on your deep learning journey, however far along that you may be! In this chapter, we will tell you a little bit more about what to expect in this book, introduce the key concepts behind deep learning, and train our first models on different tasks.

It doesn't matter if you don't come from a technical or a mathematical background (though it's okay if you do too!); we wrote this book to make deep learning accessible to as many people... A lot of people assume that you need all kinds of hard-to-find stuff to get great results with deep learning, but as you'll see in this book, those people are wrong. <> is a list of a few thing you absolutely don't need to do world-class deep learning. Deep learning is a computer technique to extract and transform data–-with use cases ranging from human speech recognition to animal imagery classification–-by using multiple layers of neural networks. Each of these layers takes its inputs from previous layers and progressively refines them. The layers are trained by algorithms that minimize their errors and improve their accuracy.

In this way, the network learns to perform a specified task. We will discuss training algorithms in detail in the next section. Deep learning has power, flexibility, and simplicity. That's why we believe it should be applied across many disciplines. These include the social and physical sciences, the arts, medicine, finance, scientific research, and many more. To give a personal example, despite having no background in medicine, Jeremy started Enlitic, a company that uses deep learning algorithms to diagnose illness and disease.

Within months of starting the company, it was announced that its algorithm could identify malignant tumors more accurately than radiologists. Here's a list of some of the thousands of tasks in different areas at which deep learning, or methods heavily using deep learning, is now the best in the world: This is a collection of interactions, mostly from the book. If you have are reading a print version of the book, or are reading it online via Github or nbviewer you will be unable to run the interactions. So I have created this notebook. Here is how you run an interaction if you do not have IPython installed on your computer.

Go to try.juptyer.org in your browser. It will launch a temporary notebook server for you. Click the New button and select Python 3. This will create a new notebook that will run Python 3 for you in your browser. Copy the entire contents of a cell from this notebook and paste it into a 'code' cell in the notebook on your browser. Jupyter notebook Handouts/Handout-01/Handout-01.ipynb

Welcome to PHYS 48T! This is a practical course on data science (DS) and machine learning (ML). We will learn about the ideas underlying the computational tools of these fields, and spend a lot of time applying those tools to various examples. The course will be programming-intensive. There are various programming languages and tools for DS and ML; the most popular are Comes out of the field of statistics; a bit of a quirky language, but very widely used in the DS/ML community, especially among academic statisticians.

It is open source, and research ideas are quickly implemented into R packages by volunteers/authors. We won't use R in this course, but I recommend that you take a quick look at it when you get a chance. A general-purpose programming language that is both highly readable and suitable for large-scale projects. Has a very wide range of packages, not just for DS and ML, but for all sorts of purposes. We will be using Python throughout this course. Before you start, you need to create a Cocalc account and add your partner as a collaborator [TODO: Link to instructions]

Navigate to www.cocalc.com and sign in using your university email address. Click on project NSCI0007_20_21 in the project list to load your project. Your project is essentially a virtual computer hosted in the cloud, and it comes preinstalled with all the software and tools you need to get Python programming straight away! Click on the Handouts folder then the 01_Introduction folder. DEFINITIONS: Cocalc - the online platform we will be using, providing access to virtual computers hosted in the cloud. Project - Every student has a Cocalc account allowing access to a project, which is a virtual computer including operating system (Linux) and software libraries.

Jupyter Notebook - a type of file which contains Python code and formatted text, allowing us to combine computations, results and descriptive text in a single file. It is also sometimes called an IPython Notebook, and has the extension .ipynb. Python - the programming language allowing us to perform scientific computing. Let's build up from the basics: what is a Jupyter Notebook? A notebook is a document made of cells. You can write in some of them (markdown cells) or you can perform calculations in Python (code cells) and run them like this:

Cool, huh? This combination of prose and code makes Jupyter Notebook ideal for experimentation: we can see the rationale for each experiment, the code, and the results in one comprehensive document. Try it yourself now. Click "Copy & Edit" in the top right to get your own editable version of this notebook, then click the cell above and hit Shift-Enter. Other renowned institutions in academia and industry use Jupyter Notebook, including Google, Microsoft, IBM, Bloomberg, Berkeley and NASA among others. Even Nobel-winning economists use Jupyter Notebooks for their experiments and some suggest that Jupyter Notebooks will be the new format for research papers.

A type of cell in which you can write text is called a Markdown cell. Markdown is a very popular markup language. To specify that a cell is Markdown you need to click in the drop-down menu in the toolbar and select Markdown.

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