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Showing posts with the label Machine Learning

Machine Learning: From hype to real-world applications

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Machine learning: real-world applications from hype How to use emerging technology business value. The hype is real. Artificial Intelligence (AI) and Machine Learning (ML) are all over the media, and everyone wants to get involved in the technology race. The progress that has been made over the last few years has been tremendous, and you've probably heard claims such as "AI is the new electricity" and "AI will revolutionize our society completely." I will not comment on those statements, but whether we can all safely agree, is it certainly very interesting about these technologies. However, all this attention raises the important question: can it really live up to the hype? In some areas, technology caught on, and it even surpassed the hype. Within image recognition, for example, the task of identifying objects and extracting information from images, AI is now moving beyond human-level performance (for example, machines are becoming better than humans at recogni

What is Machine Learning? Definition.

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What is machine learning? Machine-learning algorithms find patterns in data and apply them. And they run so many worlds. Machine-learning algorithms are responsible for artificial intelligence advances and the huge number of applications you hear.  What is the definition of machine learning? Machine-learning algorithms use fact-finders to find the format of a huge * volume of data. And the data, here, covers a lot of things - numbers, words, images, clicks, what you have. If it can be digitally stored, it can be fed into a machine-learning algorithm. Machine learning is a process that powers many of the services we use today - on recommendation systems such as Netflix, YouTube, and Spotify; Search engines like Google and Baidu; Social media feeds like Facebook and Twitter; Voice assistants like Siri and Alexa. Goes to the list. In all of these instances, each platform is collecting as much data about you as possible - what type of genre you would like to see, what links you are clickin

TensorFlow Hub: A Library for Reusable Machine Learning Modules in TensorFlow

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Introducing TensorFlow Hub: Library for Reusable Machine Learning Modules at TensorFlow One of the most fundamental things in software development is the idea of ​​a store of shared code that is easy to ignore. As programmers, libraries instantly make us more effective. In a sense, they change the process of problem-solving programming. When using the library, we often think of programming in terms of building blocks - or modules - that can be glued together. How can a library be considered a machine education developer? Of course, in addition to the share to code, we also want to share pre-trend models. Sharing pre-trained models make it possible for developers to optimize for their domain, without access to computer resources or data used to train the model in the original hands. For example, the NASNet train took thousands of GPU-hours. By sharing the weights learned, a model developer can make it easier for others to reuse and build their work. It's a library idea for machine e

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

ML Meets Fashion

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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

Cloud Machine Learning Engine

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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

Server-less predictions at scale

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Serverless forecasts in measurement Once we are happy with your trained machine learning model, how can we measure predictions? Find it in this part of Cloud Ann Adventure! Google's cloud machine learning engine enables you to create prediction services for your TensorFlow model without any option work. Get more time to work with your data, from a trained model to a diploma, auto-scaled pred forecast service in minutes. Service Predictions: The Final Step So, we have gathered our data, and finally finished the training of the appropriate model and verified that it performs well. We are now ready to finally go to the final stage: serving your prophecies. In the challenge of presenting forecasts when we want to modify an objective-built model for service. In particular, a fast, light-weight model is stable because we do not want any updates during service. Additionally, we want our prediction server to measure up with demand, which adds another layer of complexity to the problem. Exp

Plain and Simple Estimators

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Plain and simple estimators Machine learning is amazing, while it doesn't force you to do advanced math. The tools for machine learning have improved dramatically, and training your own model has never been easier. We use our understanding of our dataset rather than an understanding of raw mathematics as a model code that we gain insights into. In this episode, we're going to train a simple classifier using a handful of lines of code. Here are all the codes we see today. Tensor flow estimators for machine learning We use TenserFlow, Google's open-source machine learning library, to train our classifiers. Tensorflow has a very large API surface, but we are going to focus on high-level APIs, called estimators. I have printed our loading results and we can see that we are now able to access the training data and related labels, or using targeted, named attributes. Build a model Next we will build the model. To do this, we will first set up feature columns. Feature columns defi