Big Data Science

2022-03-11 08:38:16 Fusion plasma control with DL
To solve the global crisis, find sources of clean, limitless energy. For example, nuclear fusion, which powers the stars in the universe. On earth, atomic batteries can be used for this, breaking and fusing them under extreme conditions in a tokamak device - a vacuum surrounded by magnetic coils. In it the plasma radiation is hotter than the core of the Sun. The norm of the device in the operating mode is very difficult: the control system must coordinate many magnetic current coils and the voltage on them is several times less in order to achieve that the plasma never touches the walls of the vessel, which can lead to heat loss and, possibly, loss. Deep reinforcement learning has been successfully applied to this problem to create controllers that maintain plasma stability and stable control of various shapes.
Existing control systems for plasma complications and requiring rare control for each of the subsequent magnetic coils. Each controller uses algorithms to evaluate plasma properties in real time and measure magnet voltages. The architecture from the renowned Deep Mind AI Center and the Swiss Center for Plasma Research uses a single neural network to control all coils simultaneously, automatically judging which voltages are best for building plasma, especially with sensors.
https://deepmind.com/blog/article/Accelerating-fusion-science-through-learned-plasma-control Open / Comment

2022-03-10 10:40:54 Yandex DataLens: Lightweight BI from Yandex.Cloud
Yandex DataLens is a free data visualization and analysis service. Main features:
• many data sources: ClickHouse, PostgreSQL, Greenplum, MySQL, CSV files, Google spreadsheets, Metrica and AppMetrica in direct access mode;
• diagrams, tables and data access UI elements for building dashboards;
• support for geodata and integration with maps;
• easy creation of the necessary dashboards without deep knowledge in DS;
• all documentation in Russian and a lot of understandable demos.
Service: https://cloud.yandex.ru/services/datalens
Documentation: https://cloud.yandex.ru/docs/datalens/quickstart Open / Comment

2022-03-06 11:12:04 Open / Comment

2022-03-03 13:20:08 Sentiment analysis in social networks in Python with VADER without developing an ML model
Not every classification problem needs machine learning models: sometimes even simple approaches can give excellent results. For example, VADER (Valence Aware Dictionary and sEntiment Reasoner) is a vocabulary and rule based sentiment analysis model. The project source code is available on Github under the MIT license: https://github.com/cjhutto/vaderSentiment
VADER can efficiently handle dictionaries, abbreviations, capital letters, repetitive punctuation marks, emoticons ( , , , etc.), etc., which are commonly used in social networks to express sentiment, making it an excellent text sentiment tool. The advantage of VADER is that it evaluates the mood of any text without prior training of ML models. The result generated by VADER is a dictionary of 4 keys neg, neu, pos and components (compound):
• neg, neu and pos mean negative, neutral and positive respectively. Their sum must be equal to 1 or close to it in a floating point operation.
• Compound corresponds to the sum of the valency scores of each word in the lexicon and determines the degree of mood, and not the actual value, unlike the previous ones. Its value ranges from -1 (the strongest negative mood) to +1 (the strongest positive mood). The use of a composite score may be sufficient to determine the main tone of the text. Compound ≥ 0.05 for positive mood, compound ≤ -0.05 for negative mood, compound ranges from -0.05 to 0.05 for neutral mood
Try Google Colab: https://colab.research.google.com/drive/1_Y7LhR6t0Czsk3UOS3BC7quKDFnULlZG?usp=sharing
Example: https://towardsdatascience.com/social-media-sentiment-analysis-in-python-with-vader-no-training-required-4bc6a21e87b8 Open / Comment

2022-02-25 07:49:27 3 Face Recognition ML Services APIs: Choose What You Need
• IBM Watson Visual Recognition API for identifying scenes, objects, and faces in images uploaded to the service. It can process unstructured data in a large volume and is suitable as a decision support system. But it is expensive to maintain and does not process structured data directly. The facial recognition method does not support general biometric recognition, and the maximum image size is 10 MB with a minimum recommended density of 32x32 ppi. Suitable for image classification using built-in classifiers, allows you to create your own classifiers and train ML models. https://www.ibm.com/watson
• Kairos Face Recognition API allows developers of ML applications to add face recognition capabilities to their applications by writing just a few lines of code. The Kairos Face Recognition API shows high accuracy in real-life scenarios and performs well in low light conditions as well as partial face hiding. Applies an ethical approach to identifying individuals, taking into account diversity. It is an extensible tool: users can apply additional intelligence to work with video and photos in the real world. Suitable for working with large volumes of images and ensures confidentiality through the secure storage of collected data and regular audits. However, it only supports BMP, JPG, and PNG file types, GIF files are not supported. Slightly slower in operation than the AWS API. https://www.kairos.com/docs/getting-started-with-kairos-face-recognition
• Microsoft Computer Vision API in Azure gives developers access to advanced image processing algorithms. Once an image is loaded or its URL is specified, Microsoft Computer Vision algorithms analyze its visual content in various ways based on the user's choice. An added benefit of this fast API is visual guides, tutorials, and examples. A high SLA guarantees at least 99.9% availability. Through tight integration with other Microsoft Azure cloud services, APIs can be packaged into a complete solution. But if the transaction per second limit is exceeded, the response time will be reduced to the agreed limit. The pricing model is demand-driven, so the service can become expensive if the number of requests spikes. The Microsoft Computer Vision API is great for classifying images with objects, creatures, scenery, and activities, including their identification, categorization, and image tagging. Supports face, mood, age and scene recognition, optical character recognition to detect text content in images. Also provides intelligent photo management and moderated content display restriction. https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/ Open / Comment

2022-02-23 09:02:52 Hide a spicy photo from strangers? Easy with nudity detection API
Python program by DeepAI (https://deepai.org/machine-learning-model/nsfw-detector)
evaluates the image and estimates the likelihood that it covers areas of the human body that are usually found under clothing. The nudity check is a dynamic label, a certainty algorithm, or a false percentage. The user can set a different threshold in their app for what it considers to be nudity, and the image detection algorithm will return the percentage chance that the image contains natural content. This ML system is applicable not only to photos, but also to videos: neural networks analyze the video stream and give probabilistic feedback on the “adultness” of consumption.
See an example of using the API here: https://medium.com/mlearning-ai/i-tried-a-python-nude-detector-with-my-photo-446dba1bbfc8 Open / Comment

2022-02-21 07:51:24 Open / Comment

2022-02-18 16:43:38 Don't like documenting code? Give it to the AI!
AI Doc Writer is a VS Code extension that documents code using AI. Simply select the necessary lines of code in the development environment and press Cmd / Ctrl +. AI Doc Writer will create a short description of each feature and options. The tool from Microsoft (https://marketplace.visualstudio.com/items?itemName=mintlify.document) supports Python, JavaScript, TypeScript, PHP and Java languages, as well as JSX and TSX files. Of course, this cannot be called software documentation in the form in which the Customer understands it, however, the presence of understandable comments makes the code maintainable and readable. More examples:
https://betterprogramming.pub/too-lazy-to-write-documentation-let-the-ai-write-it-for-you-8574f7cd11b2 Open / Comment

2022-02-16 07:31:16 Clustimage - Python library for image clustering
Unsupervised clustering in image recognition is a multi-step process. It includes preprocessing, feature extraction, similarity clustering, and estimation of the optimal number of clusters using a quality measure. All of these steps are implemented in the Clustimage Python package, which takes only paths or raw pixel values as input.
The goal of clustimage is to detect natural groups or clusters of images using the ilhouette, dbindex and their derivatives methods, in combination with clustering methods (agglomerative, kmeans, dbscan and hdbscan). Clustimage helps you determine the most robust clustering by efficiently searching by parameter and evaluating clusters. In addition to image clustering, the model can also be used to find the most similar images for a new invisible sample.
To try this open source library (https://github.com/erdogant/clustimage), you first need to install it: pip install clustimage, then import the package into your project: from clustimage import Clustimage.
See an example with explanations here: https://towardsdatascience.com/a-step-by-step-guide-for-clustering-images-4b45f9906128 Open / Comment

2022-02-14 08:24:57 Neural networks for selfies on Google Pixel 6: accurate alpha matting in portrait mode
Matting an image is the process of extracting a precise alpha mask that separates the foreground and background objects of an image. This is not only necessary for professional designers when designing advertising photos, but has also become a popular entertainment for smartphone users. Send friends a selfie with the Eiffel Tower in the background while in a room with a grandmother's carpet? Easy with Google Pixel 6: a convolutional neural network from a sequence of encoder-decoder blocks to gradually evaluate high-quality alpha matting will preserve all the details, including fine hairs.
The input RGB image is combined with a coarse alpha matte (generated with a low resolution people segmenter) which is passed as input to the network. The new Portrait Matting model uses the MobileNetV3 backbone and a shallow decoder with few layers to first predict the low resolution advanced alpha mask. Then a shallow codec and a series of residual blocks are applied to process the high resolution image and the refined alpha mask from the previous step. The shallow codec relies more on lower level functions than the previous MobileNetV3 backbone, focusing on high resolution structural functions to predict the final transparency values for each pixel. This way the model can refine the original foreground alpha mask and accurately extract very fine details. This neural network architecture works effectively on Pixel 6 using Tensorflow Lite. The ML model also uses a variety of training datasets that cover a wide range of skin tones and hairstyles.
https://ai.googleblog.com/2022/01/accurate-alpha-matting-for-portrait.html Open / Comment