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The digitization of historical handwritten document images is important for the preservation of cultural heritage. Moreover, the transcription of text images obtained from digitization is necessary to provide efficient information access to the content of these documents. Handwritten Text Recognition (HTR) has become an important research topic in the areas of image and computational language ... [Show full abstract] processing that allows us to obtain transcriptions from text images. State-of-the-art HTR systems are, however, far from perfect. One difficulty is that they have to cope with image noise and handwriting variability.
Utilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks. This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applications of the following deep learning models to anomaly detection: various types of Autoencoders, Restricted Boltzmann Machines, RNNs & LSTMs, and Temporal Convolutional Networks. The book explores unsupervised and semi-supervised anomaly detection along with the basics of time series-based anomaly detection.
This channel publishes interviews with data scientists from big companies like Google, Uber, Airbnb, etc. From these videos, you can get an idea of what it is like to be a data scientist and acquire valuable advice to apply in your life. A new ML Youtube channel that everyone should check out, Machine Learning 101 posts explainer videos on beginner AI concepts. The channel also posts podcasts with expert data scientists and professionals working on AI in commercial industries. FreeCodeCamp is an incredible non-profit organization. It is an open-source community that offers a collection of resources that helps people learn to code for free and create their projects.
Data cleaning: Some people consider this feature engineering but it is really its own step. In short, you need to make sure the data is even useable before feature engineering is even possible. It involves fixing errors in the data, handling missing values, handling outliers, one-hot encoding, scaling features,and countless other things. In my opinion, data cleaning is the only step worse than feature engineering so anyone who finds a way to automate this step will be my new hero. Mean encoding: This step involves transforming categorical features like zip code into information useable by the model. For example, you might create a column that shows the average sales revenue for a zip code.
In this project, we use GridDB to create a Machine Learning platform where we Kafka is used to import stock market data from Alphavantage, a market data provider. Tensorflow and Keras train a model that is then stored in GridDB, and then finally uses LSTM prediction to find anomalies in daily intraday trading history. The last piece is that the data is visualized in Grafana and then we configure GridDB to send notifications via its REST Trigger function to Twilio's Sendgrid. The actual machine learning portion of this project was inspired by posts on Towards Data Science and Curiously. This model and the data flow is also applicable to many other datasets such as predictive maintenance or machine failure prediction or wherever you want to find anomalies in time series data.
Several steps need to be performed during the preparation phase to transform images/sounds into numerical vectors accepted by the algorithms. Regression on text data: Training data consists of texts whose numerical scores are already known. Several steps need to be performed during the preparation phase to transform the text into numerical vectors accepted by the algorithms. Examples: Housing prices, Customer churn, Customer Lifetime Value, Forecasting (time series), and Anomaly Detection.
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