Artificial Intelligence Future

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

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

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 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 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

Meetings of machine learning fashion Training models with MNIS datasets is often considered the "hello world" of machine learning. This has happened many times, but unfortunately, only one model does well on MNIS, which does not mean that it predicts high performance on other datasets, especially when we have most of the image data today that are more complex than handwritten ones. Fashionable machine learning Zalando decided to make it MNIS fashionable again, and recently released a fashion-mnist dataset. This is exactly the same format as the ‘regular’ MNIS data except for pictures of different clothing types, shoes, and bags. It is still in the middle of 10 categories and the images are still 2 by 28 pixels. Are in pixels. Train a model to find out what kind of clothes are shown! Line classifier We'll start creating a line classification, and see how we do it. In general, we use the approximate framework of TensorFlow to easily write and maintain our code. As a reminde

Training delivery in the cloud: cloud machine learning engine In the previous episode, we talked about the problems we face when your dataset is too big to fit on your local machine, and we discussed how we can move data to the cloud, with scalable storage. Today we are in the second half of that problem - those computer resources are falling apart. When training large models, the current approach involves training in parallel. Our data is split and sent to multiple working machines, and then the model must keep the information and signals that it is receiving from each machine, again together, to create a fully trained model. Do you like configuration If you wish, you can configure them to spin some virtual machines, install the necessary libraries, network them together, and run distributed machine learning. And then when you're done, you want to be sure to take those machines down. While some may find it easy on the surface, it can be challenging if you are not familiar with thi