Understanding time based patterns is critical for any business. Questions like how much inventory to maintain, how much footfall do you expect in your store to how many people will travel by an airline – all of these are important time series problems to solve. This is why time series forecasting is one of the must-know techniques for any data scientist. From predicting the weather to the sales of a product, it is integrated into the data science ecosystem and that makes it a mandatory addition to a data scientist’s skillset. If you are a beginner, time series also provides a good way to start working on real life projects. You can relate to time series very easily and they help you enter the larger world of machine learning.
Flow charts are an important part of a clinical trial report. Making them can be a pain though. One good way to do it seems to be with the grid and Gmisc packages in R. X and Y coordinates can be designated based on the center of the boxes in normalized device coordinates (proportions of the device space – 0.5 is this middle) which saves a lot of messing around with corners of boxes and arrows. A very basic flow chart, based very roughly on the CONSORT version, can be generated as follows…
1. Assuming your data is ready to use — and all you need
2. Not exploring your data set before starting work
3. Expecting too much
4. Not using a control group to test your new data model in action
5. Starting with targets rather than hypotheses
6. Letting your data model go stale
7. Automating without monitoring the final outcome
8. Forgetting the business experts
9. Picking too complex a tool
10. Reusing implementations that don’t fit your problem
11. Misunderstanding fundamentals like causation and cross validation
12. Underestimating what users can understand
In this article, we show that the issue with polynomial regression is not over-fitting, but numerical precision. Even if done right, numerical precision still remains an insurmountable challenge. We focus here on step-wise polynomial regression, which is supposed to be more stable than the traditional model. In step-wise regression, we estimate one coefficient at a time, using the classic least square technique.
AI is becoming more and more human like – basically the vision that was set out when this term was coined. It’s a little scary how good machines are getting but it’s exciting in equal measure. The potential of helping mankind is limitless and with the amount of research going on at the big tech giants like Google, AI will only keeping getting better.
1. Boston Dynamic’s Helping Robot
2. Amazon’s Warehouse Robots
3. An Autonomous Bike Driving Robot
4. Google’s DeepMind AI Taught itself How to Walk, Run, Jump..
5. An Interview with Sophia, the Robot
6. What’s new, Atlas
7. Flippy, the Burger Cook
8. Google Duplex
9. Amazon Go
GraphQL(Graph Query Language) is a powerful query language that has allowed huge organizations, like Facebook and Github, to expose massive amounts of data; gRPC is an open source remote procedure call (RPC) system initially developed at Google. Although these technologies live in extremely different spaces, they can work together and complement each other perfectly.
The purpose of this article is to build a model with Tensorflow. We will see the different steps to do that. The code exposed will allow you to build a regression model, specify the categorical features and build your own activation function with Tensorflow. The data used corresponds to a Kaggle’s competition House Prices: Advanced Regression Techniques.
Matt Winkler delivered a talk at Microsoft Build 2018 explaining what is new in Azure Machine Learning. The Azure Machine Learning platform is built from the hardware level up. It is open to whatever tools and frameworks of your choice. If it runs on Python, you can do it within the tools and frameworks. Services come in three flavors: conversational, pre-trained, and custom AI.
Microsoft is using artificial intelligence and Windows Machine Learning (ML) to improve its products, including Office 365. During the third Build keynote, corporate vice president of the Windows Developer Platform Kevin Gallo used Microsoft Word as an example, stating that the company’s goal is to make everyone a better writer. How Through grammar checking powered by Windows ML and artificial intelligence. “Some areas are very, very hard to detect with traditional algorithms,” he said. “For example, you get into a car, but onto a train. There is a shadow on the road versus there is fog on the road.” He said this problem is personal due to his daughter, Anna. He said she’s struggled with grammar her whole life and felt that she would never be a good writer. Her woes are understandable: The English language is complex, with words having different meanings depending on the subject. His example of a car versus a train is another good point.
Natural language processing (NLP) is a broad field encompassing many different tasks such as text search, translation, named entity recognition, and topic modeling. On a daily basis, we use NLP whenever we search the internet, ask a voice assistant to tell us the weather forecast, or translate web pages written in another language. Businesses use NLP to understand how their customers talk about their product on social media. NLP may even help you receive better healthcare, as the healthcare industry applies it to electronic health records to better understand patients.
In this article, we describe how we orchestrate Kafka, Dataflow and BigQuery together to ingest and transform a large stream of events. When adding scale and latency constraints, reconciling and reordering them becomes a challenge, here is how we tackle it.
The fall of RNN / LSTM We fell for Recurrent neural networks (RNN), Long-short term memory (LSTM), and all their variants. Now it is time to drop them! It is the year 2014 and LSTM and RNN make a great come-back from the dead. We all read Colah’s blog and Karpathy’s ode to RNN. But we were all young and unexperienced. For a few years this was the way to solve sequence learning, sequence translation (seq2seq), which also resulted in amazing results in speech to text comprehension and the raise of Siri, Cortana, Google voice assistant, Alexa. Also let us not forget machine translation, which resulted in the ability to translate documents into different languages or neural machine translation, but also translate images into text, text into images, and captioning video, and … well you got the idea. Then in the following years (2015-16) came ResNet and Attention. One could then better understand that LSTM were a clever bypass technique. Also attention showed that MLP network could be replaced by averaging networks influenced by a context vector. More on this later. It only took 2 more years, but today we can definitely say: “Drop your RNN and LSTM, they are no good!”
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