IBM Developer Day 2018 뱃지 M2M/IoT

IBM Developer Day 2018 에 가서 받은 전자 뱃지와 센서들



뱃지 기술 스펙

  • MCU: ESP32 DevKitC (분리가능)
  • Display: SPI 2.4″ 16bit color TFT LCD
  • Input: 5way tactile switch (GPIO12, 13, 14, 15, 32), 2 * tactile switch (GPIO2, 4)
  • LED: Blue (GPIO16), Green (GPIO17)
  • 외부 GPIO
    • 일반 I2C, I2S, SPI, ADC, DAC, Digital 센서 연결용 GPIO 6개
    • 미세먼지 센서 연결 전용 GPIO
    • 고출력 적외선 리모콘 신호 출력 핀

튜토리얼 (제공예정)

  • 적외선 리모콘 신호 분석 및 리모콘 스테이션 제작
  • PWM 제어로 부저 소리내기
  • I2C 인터페이스를 활용한 자이로, 조도, 온습도센서 연결
  • 소형 LCD 패널 SPI 제어
  • 미세먼지 모니터링 및 시계열 데이터베이스 저장과 조회
  • ADC 컨버터로 가스센서 아날로그 정보 수집

QnA

  • Q: 이 뱃지는 얼마인가요?

    A: IBM Developer Day 2018 참가자를 대상으로 무료 제공하는 품목으로, 별도 판매하지 않습니다.

  • Q: 행사 이후에는 못쓰나요?

    A:아닙니다. 아두이노, Micropython 롬을 올려 자유롭게 IoT 개발이 가능합니다.

  • Q: 뒤의 보조배터리는 왜 달려있나요?

    A: 행사에서 뱃지 목적으로 사용하기 위한 전원 공급용입니다. 2500mAh 용량이며, 행사 종료 후에는 분리하셔서 별도로 사용하셔도 무방합니다.

  • Q: 배터리가 없으면 어떻게 동작하나요?

    A: ESP32 DevKitC 에 일반 Micro USB 충전기나 보조배터리를 연결해서 전원을 공급할 수 있습니다. 개발할때는 컴퓨터 USB 에 연결하면 개발용 시리얼 연결과 전원 공급이 동시에 됩니다.

  • SDKs & Demos

    Found 1 results
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    TitlePlatformVersionRelease Datesort ascendingDownload
    ESP32 IDF
    RTOS SDKV3.12018.09.07

    Tools

    Found 4 results
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    TitlePlatformVersionRelease Datesort ascendingDownload
    Flash Download Tools (ESP8266 & ESP32)
    Windows PCV3.6.52018.10.11
    ESP-Tuning Tool for TouchSensor
    ZIPV1.02018.09.21
    ESP32 Certification and Test
    ZIPV1.72018.07.27
    ESP32&ESP8266 RF Performance Test Demonstration
    ZIPV2.02018.04.16

    AT

    Found 9 results
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    TitlePlatformVersionRelease Datesort ascendingDownload
    ESP32-WROOM-32 AT Bin V1.1.2
    BinV1.1.22018.09.04
    ESP32-WROVER AT Bin V1.1.2
    BinV1.1.22018.09.04
    ESP32-WROOM-32 AT Bin V1.1.1
    BinV1.1.12018.07.11
    ESP32-WROVER AT Bin V1.1.1
    BinV1.1.12018.07.11
    ESP32-WROVER AT Bin V1.1
    BinV1.12018.06.13
    ESP32-WROOM-32 AT Bin V1.1
    BinV1.12018.06.13
    ESP-WROOM-32 AT Bin V1.0
    BinV1.0.02017.11.17
    ESP32-WROVER AT Bin V0.10
    BinV0.102017.09.18
    ESP-WROOM-32 AT Bin V0.10
    BinV0.102017.06.14

    Documentation

    • Collapse all
    • Expand all
    TitleFormatVersionRelease DateDownload
    ESP32-DevKitC Getting Started Guide
    HTMLlatest2017.07.28
    ESP32-DevKitC-V2 Reference Design r1.0
    ZIPV2-r1.02018.05.08
    ESP32-DevKitC-V4 Reference Design r2.0
    ZIPV4-r2.02018.06.28
    Espressif Products 
Ordering Information
    PDFV2.02018.09.21


모듈에 대한 상세 정보는 위에서...마지막의 "D" 는 듀얼코어의 의미였다.






UP 6리터 보충수통 후기 아쿠아

최근 물생활 용품을 이거 저거 질러댔다.

그 중 하나가 UP 6리터 보충수통인데 피그메우스와 하스타투스를 각각 50마리씩 투입해서,

이 작은 녀석들이 환수시에 스트레스를 덜 받게 하기 위해서다.

1리터 짜리는 너무 작아서 필요 없을테니 6리터 짜리로 샀다.

간단한 제품이라 자세한 스펙은 필요 없고 아래처럼 어항 2개에 올려서 물 보충을 해봤다.



오~ 진작에 사서 쓸걸! 물이 아래처럼 졸졸 나온다. 그냥 최대로 열어 놓고 사용하면 되겠다. 굳이 조절 안해도 될듯.


미역국 라면 후기 기타

집에 라면이 다 떨어진 김에 어떤 걸 먹어볼까 하다가 새로 나왔다는 미역국 라면을 동네 마트에서 사왔다.

가격이 좀 쎄길래 망설였지만 괜찮다는 후기를 본 기억이 있어서 일단 도전!

이래 저래 장점들로 도배된 앞면, 사실일까?

끓이기 전에 뒷면 조리법을 보자. 스프가 두개인데 건더기 스프를 끓기 전에 먼저 넣으라고 한다.

끓이고 나서 보니 이유가 있었던거다! 

미역국 끓여본 사람은 알겠지만 대부분 건미역을 물에 불리는 작업이 필요한데, 비슷한 이유라고 생각 된다.


끓이고 나서 사진 한방. 올~ 건더기가 제법 풍성하다. 

끓이는 시간은 꼬들한 라면을 좋아하는 관계로 조리법에 나온 2분을 채우지 않았다.

먹어보니 국물도 괜찮고 면발도 다른 오뚜기 라면들에 비해 쫄깃한듯(쌀가루 때문인가?).

면을 다 건져 먹고 햇반도 하나 데워 국물에 말아 먹었다. 아~ 이 행복함 ㅋㅋㅋ

결론, 가격은 좀 부담 되지만 대만족이다. 미역국 먹고 싶을때 또 사다 먹어봐야겠다.

고춧 가루가 안들어가서 자극적이지도 않고 국물도 진국이다. 이걸로 해장해도 될듯.

싱겁게 먹는 사람은 액상 스프를 좀 덜 넣길 권한다. 살짝 짜게 느껴질수도...


Essential libraries for Machine Learning in Python NoSQL/Big Data/DB

https://medium.freecodecamp.org/essential-libraries-for-machine-learning-in-python-82a9ada57aeb?mkt_tok=eyJpIjoiTkdNek5UQXhaalV4TkdNMSIsInQiOiJjSW9CSTdUN20zMTV3UmV5S3BnSjBKZ09NZFJoMnNkdHNlR2FWMVNINVRxNUx5djNwK0U5dGZMeGFMa2orb1RtSXlcLzJoc1N1ajNYVkJuWWlVNHJyUEhocGdUaXVrVWpUdXRZK0dGSnFnSVwvd1c4Z1lPRkx0cnhsbG0ySXpUXC9GZCJ9

Below are some of the most commonly used libraries in machine learning:

Scikit-learn for working with classical ML algorithms

Scikit-learn is one the most popular ML libraries. It supports many supervised and unsupervised learning algorithms. Examples include linear and logistic regressions, decision trees, clustering, k-means and so on.

It builds on two basic libraries of Python, NumPy and SciPy. It adds a set of algorithms for common machine learning and data mining tasks, including clustering, regression and classification. Even tasks like transforming data, feature selection and ensemble methods can be implemented in a few lines.

For a novice in ML, Scikit-learn is a more-than-sufficient tool to work with, until you start implementing more complex algorithms.

Tensorflow for Deep Learning

If you are in the world of machine learning, you have probably heard about, tried or implemented some form of deep learning algorithm. Are they necessary? Not all the time. Are they cool when done right? Yes!

The interesting thing about Tensorflow is that when you write a program in Python, you can compile and run on either your CPU or GPU. So you don’t have to write at the C++ or CUDA level to run on GPUs.

It uses a system of multi-layered nodes that allows you to quickly set up, train, and deploy artificial neural networks with large datasets. This is what allows Google to identify objects in photos or understand spoken words in its voice-recognition app.

Theano is also for Deep Learning

Theano is another good Python library for numerical computation, and is similar to NumPy. Theano allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

What sets Theano apart is that it takes advantage of the computer’s GPU. This allows it to make data-intensive calculations up to 100 times faster than when run on the CPU alone. Theano’s speed makes it especially valuable for deep learning and other computationally complex tasks.

The final release of Theano library was last year — 2017, version 1.0.0 with a lot of new features, interface changes and improvements.

Pandas for data extraction and preparation

Pandas is a very popular library that provides high-level data structures which are simple to use as well as intuitive.

It has many inbuilt methods for grouping, combining data and filtering as well as performing time series analysis.

Pandas can easily fetch data from different sources like SQL databases, CSV, Excel, JSON files and manipulate the data to perform operations on it. There are two main structures in the library:

  • “Series” — one dimensional
  • “Data Frames” — two dimensional.

For more details on how to use Series and Dataframes, check out my other blog post.

Matplotlib for data visualization

Image source: https://github.com/nschloe/matplotlib2tikz

The best and most sophisticated ML is meaningless if you can’t communicate it to other people.

So how do you actually turn around value from all this data that you have? How do you inspire your business analysts and tell them “stories” full of “insights”?

This is where Matplotlib comes to the rescue. It is a standard Python library used by every data scientist for creating 2D plots and graphs. It’s pretty low-level, meaning it requires more commands to generate nice-looking graphs and figures than with some advanced libraries.

However, the flip side of that is flexibility. With enough commands, you can make just about any kind of graph you want with Matplotlib. You can build diverse charts, from histograms and scatterplots to non-Cartesian coordinates graphs.

It supports different GUI backends on all operating systems, and can also export graphics to common vector and graphic formats like PDF, SVG, JPG, PNG, BMP, GIF, etc.

Seaborn is another data visualization library

Image source: seaborn.pydata.org/

Seaborn is a popular visualization library that builds on Matplotlib’s foundations. It is a higher-level library, meaning it’s easier to generate certain kinds of plots, including heat maps, time series, and violin plots.


The 50 Best Public Datasets for Machine Learning NoSQL/Big Data/DB

https://medium.com/datadriveninvestor/the-50-best-public-datasets-for-machine-learning-d80e9f030279?mkt_tok=eyJpIjoiTkdNek5UQXhaalV4TkdNMSIsInQiOiJjSW9CSTdUN20zMTV3UmV5S3BnSjBKZ09NZFJoMnNkdHNlR2FWMVNINVRxNUx5djNwK0U5dGZMeGFMa2orb1RtSXlcLzJoc1N1ajNYVkJuWWlVNHJyUEhocGdUaXVrVWpUdXRZK0dGSnFnSVwvd1c4Z1lPRkx0cnhsbG0ySXpUXC9GZCJ9


Dataset Finders

Kaggle: A data science site that contains a variety of externally contributed interesting datasets. You can find all kinds of niche datasets in its master list, from ramen ratings to basketball data to and even seattle pet licenses.

UCI Machine Learning Repository: One of the oldest sources of datasets on the web, and a great first stop when looking for interesting datasets. Although the data sets are user-contributed, and thus have varying levels of cleanliness, the vast majority are clean. You can download data directly from the UCI Machine Learning repository, without registration.

VisualData: Discover computer vision datasets by category, it allows searchable queries.

General Datasets

Public Government datasets

Data.gov: This site makes it possible to download data from multiple US government agencies. Data can range from government budgets to school performance scores. Be warned though: much of the data requires additional research.

Food Environment Atlas: Contains data on how local food choices affect diet in the US.

School system finances: A survey of the finances of school systems in the US.

Chronic disease data: Data on chronic disease indicators in areas across the US.

The US National Center for Education Statistics: Data on educational institutions and education demographics from the US and around the world.

The UK Data Service: The UK’s largest collection of social, economic and population data.

Data USA: A comprehensive visualization of US public data.

Finance & Economics

Quandl: A good source for economic and financial data — useful for building models to predict economic indicators or stock prices.

World Bank Open Data: Datasets covering population demographics, a huge number of economic, and development indicators from across the world.

IMF Data: The International Monetary Fund publishes data on international finances, debt rates, foreign exchange reserves, commodity prices and investments.

Financial Times Market Data: Up to date information on financial markets from around the world, including stock price indexes, commodities and foreign exchange.

Google Trends: Examine and analyze data on internet search activity and trending news stories around the world.

American Economic Association (AEA): A good source to find US macroeconomic data.

Machine Learning Datasets:

Images

Labelme: A large dataset of annotated images.

ImageNet: The de-facto image dataset for new algorithms, organized according to the WordNet hierarchy, in which hundreds and thousands of images depict each node of the hierarchy.

LSUN: Scene understanding with many ancillary tasks (room layout estimation, saliency prediction, etc.)

MS COCO: Generic image understanding and captioning.

COIL100 : 100 different objects imaged at every angle in a 360 rotation.

Visual Genome: Very detailed visual knowledge base with captioning of ~100K images.

Google’s Open Images: A collection of 9 million URLs to images “that have been annotated with labels spanning over 6,000 categories” under Creative Commons.

Labelled Faces in the Wild: 13,000 labeled images of human faces, for use in developing applications that involve facial recognition.

Stanford Dogs Dataset: Contains 20,580 images and 120 different dog breed categories.

Indoor Scene Recognition: A very specific dataset and very useful, as most scene recognition models are better ‘outside’. Contains 67 Indoor categories, and 15620 images.

Sentiment Analysis

Multidomain sentiment analysis dataset: A slightly older dataset that features product reviews from Amazon.

IMDB reviews: An older, relatively small dataset for binary sentiment classification features 25,000 movie reviews.

Stanford Sentiment Treebank: Standard sentiment dataset with sentiment annotations.

Sentiment140: A popular dataset, which uses 160,000 tweets with emoticons pre-removed.

Twitter US Airline Sentiment: Twitter data on US airlines from February 2015, classified as positive, negative, and neutral tweets

Natural Language Processing

HotspotQA Dataset: Question answering dataset featuring natural, multi-hop questions, with strong supervision for supporting facts to enable more explainable question answering systems.

Enron Dataset: Email data from the senior management of Enron, organized into folders.

Amazon Reviews: Contains around 35 million reviews from Amazon spanning 18 years. Data include product and user information, ratings, and the plaintext review.

Google Books Ngrams: A collection of words from Google books.

Blogger Corpus: A collection 681,288-blog posts gathered from blogger.com. Each blog contains a minimum of 200 occurrences of commonly used English words.

Wikipedia Links data: The full text of Wikipedia. The dataset contains almost 1.9 billion words from more than 4 million articles. You can search by word, phrase or part of a paragraph itself.

Gutenberg eBooks List: Annotated list of ebooks from Project Gutenberg.

Hansards text chunks of Canadian Parliament: 1.3 million pairs of texts from the records of the 36th Canadian Parliament.

Jeopardy: Archive of more than 200,000 questions from the quiz show Jeopardy.

SMS Spam Collection in English: A dataset that consists of 5,574 English SMS spam messages

Yelp Reviews: An open dataset released by Yelp, contains more than 5 million reviews.

UCI’s Spambase: A large spam email dataset, useful for spam filtering.

Self-driving

Berkeley DeepDrive BDD100k: Currently the largest dataset for self-driving AI. Contains over 100,000 videos of over 1,100-hour driving experiences across different times of the day and weather conditions. The annotated images come from New York and San Francisco areas.

Baidu Apolloscapes: Large dataset that defines 26 different semantic items such as cars, bicycles, pedestrians, buildings, streetlights, etc.

Comma.ai: More than 7 hours of highway driving. Details include car’s speed, acceleration, steering angle, and GPS coordinates.

Oxford’s Robotic Car: Over 100 repetitions of the same route through Oxford, UK, captured over a period of a year. The dataset captures different combinations of weather, traffic and pedestrians, along with long-term changes such as construction and roadworks.

Cityscape Dataset: A large dataset that records urban street scenes in 50 different cities.

CSSAD Dataset: This dataset is useful for perception and navigation of autonomous vehicles. The dataset skews heavily on roads found in the developed world.

KUL Belgium Traffic Sign Dataset: More than 10000+ traffic sign annotations from thousands of physically distinct traffic signs in the Flanders region in Belgium.

MIT AGE Lab: A sample of the 1,000+ hours of multi-sensor driving datasets collected at AgeLab.

LISA: Laboratory for Intelligent & Safe Automobiles, UC San Diego Datasets: This dataset includes traffic signs, vehicles detection, traffic lights, and trajectory patterns.

Bosch Small Traffic Light Dataset: Dataset for small traffic lights for deep learning.

LaRa Traffic Light Recognition: Another dataset for traffic lights. This is taken in Paris.

WPI datasets: Datasets for traffic lights, pedestrian and lane detection.

Clinical

MIMIC-III: Openly available dataset developed by the MIT Lab for Computational Physiology, comprising de-identified health data associated with ~40,000 critical care patients. It includes demographics, vital signs, laboratory tests, medications, and more.

Note:

If you are aware of other high-quality, public datasets, which you recommend to people in regards to machine learning, deep learning, etc. Please feel free to suggest them along with the reasons, why they should be included.

If the reason is strong, We will include them in the list. Also, please let us know your experience with using any of these datasets in the comments section.

Happy machine learning!

Sources:

https://www.dataquest.io/blog/free-datasets-for-projects


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