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Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements.
It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to known movements.
Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is that this feature engineering requires deep expertise in the field.
Recently, deep learning methods such as recurrent neural networks and one-dimensional convolutional neural networks, or CNNs, have been shown to provide state-of-the-art results on challenging activity recognition tasks with little or no data feature engineering.
In this tutorial, you will discover the ‘Activity Recognition Using Smartphones‘ dataset for time series classification and how to load and explore the dataset in order to make it ready for predictive modeling.
Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements.
It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to known movements.
Classical approaches to the problem involve hand crafting features from the time series data based on fixed-size windows and training machine learning models, such as ensembles of decision trees. The difficulty is that this feature engineering requires deep expertise in the field.
Recently, deep learning methods such as recurrent neural networks and one-dimensional convolutional neural networks or CNNs have been shown to provide state-of-the-art results on challenging activity recognition tasks with little or no data feature engineering.
Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. Unfortunately, current visualization packages are rudimentary and not immediately helpful to the novice. For example, we couldn't find a library that visualizes how decision nodes split up the feature space. So, we've created a general package (part of the animl library) for scikit-learn decision tree visualization and model interpretation.
A brief overview of how to use fastText to train powerful text classifiers in a python notebook. - mpuig/textclassification
Introduction Have you ever wondered how Netflix suggests movies to you based on the movies you have already watched? Or how does an e-commerce websites display options such as "Frequently Bought Together"? They may look relatively simple options but behind the scenes, a complex statistical algorithm executes in order to
Donkey Car trained with Double Deep Q Learning (DDQN) in Unity Simulator.
Toolkit for Text Generation and Beyond. Contribute to asyml/texar development by creating an account on GitHub.
Indoor movement prediction involves using wireless sensor strength data to predict the location and motion of subjects within a building. It is a challenging problem as there is no direct analytical model to translate the variable length traces of signal strength data from multiple sensors into user behavior.
The AI podcast hosts accessible, big-picture conversations at MIT and beyond about the nature of intelligence with some of the most interesting people in the world thinking about AI from the perspective of deep learning, robotics, AGI, neuroscience, philosophy, psychology, cognitive science, economics, physics, mathematics, and more.
License plate detection is a common use case which has been solved (somewhat) several times, but felt that we could provide something better than the current options.
You are handed data and told to develop a forecast model. What do you do? This is a common situation; far more common than most people think. Perhaps you are sent a CSV file. Perhaps you are given access to a database. Perhaps you are starting a competition. The problem can be reasonably well defined: …
Everyone loves neural networks. Until they start criticising your code, and your worth as a person…
TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.
Features
- Noun phrase extraction
- Part-of-speech tagging
- Sentiment analysis
- Classification (Naive Bayes, Decision Tree)
- Language translation and detection powered by Google Translate
- Tokenization (splitting text into words and sentences)
- Word and phrase frequencies
- Parsing
- n-grams
- Word inflection (pluralization and singularization) and lemmatization
- Spelling correction
- Add new models or languages through extensions
- WordNet integration
“Don’t think of the overwhelming majority of the impossible.”
“Grew up your bliss and the world.”
“what we would end create, creates the ground and you are the one to warm it”
“look and give up in miracles”
All the quotes above have been generated by a computer, using a program that consists of less than 20 lines of python code.
Resumio is a Telegram Bot that creates a summary from a web article. Just copy & paste the url or share the url with Resumio and you will get back a 4-paragraphs summary.
Aim
Since my first week on this platform, I have been fascinated by the topic of time series analysis. This kernel is prepared to be a container of many broad topics in the field of time series analysis. My motive is to make this the ultimate reference to time series analysis for beginners and experienced people alike.
Some important things
- This kernel is a work in progress so every time you see on your home feed and open it, you will surely find fresh content.
- I am doing this only after completing various courses in this field. I continue to study more advanced concepts to provide more knowledge and content.
- If there is any suggestion or any specific topic you would like me to cover, kindly mention that in the comments.
- If you like my work, be sure to upvote(press the like button) this kernel so it looks more relevant and meaningful to the community.
Machine learning methods can be used for classification and forecasting on time series problems. Before exploring machine learning methods for time series, it is a good idea to ensure you have exhausted classical linear time series forecasting methods. Classical time series forecasting methods may be focused on linear relationships, nevertheless, they are sophisticated and perform …
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.