This piece of blog is written to share my experience with beginners for a specific Machine Learning object detection case. Deep learning is an advanced sub-field of Artificial Intelligence (AI) and Machine Learning (ML) that stayed as a scholarly field for a long time. With the abundance of data and exponential increase of computing power, we have been seeing a proliferation of applied deep learning business cases across disciplines. Also a lot of smart people choose to study AI/ML and many large high tech companies (leading cloud ML platforms include AWS SageMaker, Microsoft Azure AI, Google Cloud Platform ML & TensorFlow etc) & start ups heavily invest in this most thriving domain of our time.
Weka is a sturdy brown bird that doesn’t fly. The name is pronounced like this, and the bird sounds like this. It is endemic to the beautiful island of New Zealand, but this is not what we are discussing in this article. In this article, I want to introduce you to the Weka software for Machine Learning. WEKA is short for Waikato Environment for Knowledge Analysis. It is developed by the University of Waikato, New Zealand. It is an open source Java software that has a collection of machine learning algorithms for data mining and data exploration tasks. It is a very powerful tool for understanding and visualizing machine learning algorithms on your local machine. It contains tools for data preparation, classification, regression, clustering, and visualization.
Access a complimentary copy of the Gartner 2019 Magic Quadrant for Data Science and Machine-Learning Platforms to discover the latest trends and see why Dataiku was named a ‘Challenger’ in the industry.
If you have data-savvy analytics talent, then you have a solid foundation to begin your AI journey. The next step: automated machine learning. Will this be the year your team starts implementing AI? Join DataRobot @ 1 PM ET, Feb 7, for more info.
Create a semantic search engine using deep contextualised language representations from ELMo and why context is everything in NLP
The opening of large archives of satellite data such as LANDSAT, MODIS and the SENTINELs has given researchers unprecedented access to data, allowing them to better quantify and understand local and global land change. The need to analyze such large data sets has led to the development of automated and semi-automated methods for satellite image time series analysis. However, few of the proposed methods for remote sensing time series analysis are available as open source software. In this paper we present the R package dtwSat. This package provides an implementation of the time-weighted dynamic time warping method for land cover mapping using sequence of multi-band satellite images. Methods based on dynamic time warping are flexible to handle irregular sampling and out-of-phase time series, and they have achieved significant results in time series analysis. Package dtwSat is available from the Comprehensive R Archive Network (CRAN) and contributes to making methods for satellite time series analysis available to a larger audience. The package supports the full cycle of land cover classification using image time series, ranging from selecting temporal patterns to visualizing and assessing the results.
Periodic planning and prioritization: This ensures that sprints and tasks are aligned with organisational needs, allows stakeholders to contribute their perspectives and expertise, and enable quick iterations and feedback Clearly defined tasks with timelines: This helps keep the data science team productive and on track, and being able to deliver on the given timelines?-?the market moves fast and doesn’t wait.
Text representation (aka text embeddings) is a breakthrough of solving NLP tasks. At the beginning, single word vector represent a word even though carrying different meaning among context. For example, ‘Washington’ can be a location, name or state. ‘University of Washington’ Zalando released an amazing NLP library, flair, makes our life easier. It already implement their contextual string embeddings algorithm and other classic and state-of-the-art text representation algorithms. In this story, you will understand the architecture and design of contextual string embeddings for sequence labeling with some sample codes.
I just love the community that we have on Medium. I recently published an article on using Virtual Environments for Python projects. The article was well received and the feedback from readers opened a new view for me. I wasn’t aware about pew, venv and pipenv previously. Their recommendation helped me learn about the latest technology in the area and further improved my knowledge and experience. After their suggestions, I read about all of them. In this article, I’ll share the new Virtual Environment tools I learnt and how they contrast with one another.
Learn about CART in this guest post by Jillur Quddus, a lead technical architect, polyglot software engineer and data scientist with over 10 years of hands-on experience in architecting and engineering distributed, scalable, high-performance, and secure solutions used to combat serious organized crime, cybercrime, and fraud.
Like many technology companies, Wealthfront uses in-product testing and experimentation to learn and make decisions. In particular, we leverage in-product A/B testing of our existing client base to understand how product decisions cause changes in behavior. However, there are characteristics of our product and business that complicate conducting these experiments and making valid inferences from their results. For example, measuring effects at the client level, rather than the session level, requires care. As does the measurement of effects that can that can take days or weeks to materialize, as opposed to manifesting contemporaneously with treatments. Lastly, a challenge specific to in-product testing of existing clients stems from compliance bias driven by heterogeneity in product usage rates. This phenomenon, which we refer to as ‘Arrival Rate Bias’, is the topic of our next two posts.
The board of the Society for Prevention Research noted recently that extant methods for the analysis of causality mechanisms in prevention may still be too rudimentary for detailed and sophisticated analysis of causality hypotheses. This Special Section aims to fill some of the current voids, in particular in the domain of statistical methods of the analysis of causal inference. In the first article, Bray et al. propose a novel methodological approach in which they link propensity score techniques and Latent Class Analysis. In the second article, Kelcey et al. discuss power analysis tools for the study of causal mediation effects in cluster-randomized interventions. Wiedermann et al. present, in the third article, methods of Direction Dependence Analysis for the identification of confounders and for inference concerning the direction of causal effects in mediation models. A more general approach to the identification of causal structures in non-experimental data is presented by Shimizu in the fourth article. This approach is based on linear non-Gaussian acyclic models. Molenaar introduces vector-autoregressive methods for the optimal representation of Granger causality in time-dependent data. The Special Section concludes with a commentary by Musci and Stuart. In this commentary, the contributions of the articles in the Special Section are highlighted from the perspective of the experimental causal research tradition.