Posts

Visualize your data with Facets

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Imagine your data with Facets The data is messed up. It is often tainted with unbalanced, incorrectly labeled, and wacky values to throw away your analysis and machine learning training. The first step in cleaning your dataset is to understand where it needs to be cleaned. Today, I only have tools for work. Imagine your data Understanding your data is the first step to cleaning up your dataset machine learning. But it can be difficult to do, especially in any kind of generalized way. An open source project from Google Research helps us look at statistics and slices into all sorts of etiquettes, which helps us see how our datasets are organized. By allowing us to detect that the data may not look as we expected, aspects help reduce road accidents. Let's see how it is. The team has a demo page on the web so you can use Fates from Chrome without installing anything. In addition, Aspects Visualization uses polymer web components supported by timescript code, so it can be easily embedde

TensorFlow Object Detection API, ML Engine, and Swift

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TensorFlow Item Search API, ML Engine, and Swift Note: As of this writing there is no official TensorFlow library for Swift, I used Swift to build client applications for predictive requests against my model. That may change in the future, but Taylor has the final say. The TensorFlow Object Detection APO demo helps you identify the location of objects in the image which can lead to some super cool applications. But because I spend more time taking photos of people, rather than things, I want to see if the same technique can be applied to identify faces. Turns out it worked well! I used it to build the Taylor Swift detector in the picture above. In this post I will outline the steps to take the T-Swift images from the iOS app which predicts against the trained model: Pre flow streams: Resize, label, split them into training and test sets, and convert to Pascal VOC format Convert images to TFRecords to be fed into the Item Search API Train the model on the Cloud ML engine using MobileNet

Using tf. Print() in TensorFlow

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Using tf.Press () in TensorFlow I know you will definitely use debuggers correctly, every time, and will never use print statements to debug your code. Right? Because if you did, you might find that TensorFlow's print statement doesn't work like normal print statements. Today I'm going to show you how TenserFlow's print statement works, and how to make the most of it, hopefully saving you some confusion along the way. Printing in TensorFlow There are a few ways to print things out when writing the TensorFlow code. Of course, there is the classic Python built-in, print (or function print (), if we're going to be Python about it). And then there's TensorFlow's print function, TFprint (note the capital P). When working with TensorFlow, it should not be forgotten that everything is ultimately a graph calculation. This means that if you print a TensorFlow operator using a Python print, it only shows a description of what that operation is because no values ​​have

Datalab: Running notebooks against large datasets

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How Datalab: Running a notebook against a large dataset Streaming your big data into your local computer environment is slow and expensive. In this episode of AI Adventure, we'll take a look at how to bring a notebook environment to your database! What's better than an interactive Python notebook? An interactive Python notebook with fast and easy data connectivity, of course! We saw how useful Jupiter notebooks are. This time we will see how to take it further by running it in the cloud with many extra goodies. Data, but big When you work with larger and larger datasets in the cloud, it becomes increasingly unnecessary to interact using your local machine. It is difficult to download statistically representative samples of data to check your code and rely on data streaming a stable connection to train locally. So what should a data scientist do? If you can't bring data to your computer, bring your data to your computer! Let's see how we can run a notebook environment in

Python package manager

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Which Python package manager should you use? Many who touch the code have different preferences when it comes to their programming environment. Vim vs Emacs. Places against tabs. Virtualenv vs Anaconda. Today I want to work with you and share my environment data for machine learning. You definitely don't have to copy my setup, but a few bits of it can serve as a useful inspiration for your development environment. Pip First, we have to talk about pipes. Pip is Python's package manager. It's been built on Python for a while now, so if you have Python, you've probably installed the pipe. Pip installs packages such as TensorFlow and Nampi, Ponds and Jupiter, and more, with their dependencies. Pipe installation <your_Liveite_library> Many Python resources are distributed as pipe packages. Sometimes you need a file in a Python script folder. txt. Typically, that file outlines all the pipe packages that the project uses, and you can install everything in that file using

Data Science with Jupyter Notebooks

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Interactive data science with Jupyter notebook The way I run Python code live on the screen is by using a Python package called Jupiter. Jupyter is built into the IPython project and allows Interactive Python to run in your browser. But it is more than that. From special command "magic" and bash commands to plugins, Jupiter greatly enhances the Python coding experience. If you are already using Jupiter, I hope I can improve your workflow and show you some new tricks. If you are not yet using Jupyter, then log in. Installation and startup The easiest way to install Jupiter is to install the pipe using Jupiter, although if you use a package Python distribution like Anacondo, you may have already installed it. Be sure to activate your Python environment first. We dive. When you turn on Local Jupyter, you'll connect to a locally running webserver through your browser, usually on the 8888 port. Start your notebook by running Jupiter Notebook in your work directory. Normally Ju

Introduction to Kaggle Kernels

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Introduction to Kaggle Kernel Kaggle is a platform for data science and sharing. You may have heard of some of their competitions, which often have cash prizes. It is also a great place to practice data science and learn from the community. What are Kaggle kernels? Kaggle kernels are basically Jupiter notebooks in the browser that run right before your eyes, all for free. Tell me again that in this case, you missed it, because it's so amazing: Kaggle is a free platform for running Jupiter notebooks in the kernel browser! This means you can avoid the hassle of setting up a local environment and have a Jupyter notebook environment inside your browser, wherever you have an internet connection anywhere in the world. Not only that, but the processing power for notebooks also comes from the server in the cloud, not as your local machine, so you can do a lot of data burning and machine learning on the laptop battery! http://blog.kaggle.com/2017/09/21/product-launch-amped-up-kernels-resour

Wrangling data with Pandas

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Wrangling data with Panda Pandas are majestic eaters of bamboo and sleep very well for long periods. But they also have a secret power: Champy in the big dataset. Today, we introduce the most powerful and popular tools of Data Wrangling, and it is also called Ponds! When you think of data science, pandas are probably not the first to come to mind. These black and white bears often eat bamboo and sleep, without doing data science. But today, we will use Panda to run our datasets and set it up for machine learning. I can’t judge the entire library in just one video, but hopefully, this observation will help you go, and I’ll let you explore the fascinating world of pandas in depth. Ponds is an open-source Python library that provides easy-to-use, high-performance data structures, and data analysis tools. Kundli bear leaves, the name comes from the word ‘panel data’, which refers to the multi-dimensional data set encountered in econometrics. Install Pip within your Python environment to in