Korean Hanja Handwriting ! vs Learn Keras Programming

2,500 MOST-USED KOREAN HANJA ! LET'S LEARN HOW TO HAND-WRITE THEM ! THIS APP BREAKS THEM DOWN STROKE BY STROKE ! • SPIN THE WHEEL TO EXPLORE It's so easy and natural. • ADD TO FAVORITES Save the word you like for the future use. • REAL HUMAN VOICE Not robotic. • ENGLISH MEANINGS All of the words have their meanings in English. • KOREAN ARTS & CULTURE Banknotes, coins and poems. • OFF-LINE INDEPENDENT Once downloaded, you don't need internet to use the app. All audios and pictures are integrated within. You can select the word in alphabetical order (English meanings) by clicking “words” in the menu bar. Basic everyday Korean words! Click on the "phonetic" button in the menu bar, you can look up words phonetically. In spoken language, the /r/ sound is sometimes dropped when it is located at the front of a word. For example, 練習(련습) becomes 연습. The checked syllables ending in the stop consonants are /p/, /t/, /k/. For example, 國家(국가) sounds /guk ga/. The final consonant /k/ is checked, stopped abruptly. It is not pronounced. So do not pronounce like /gu+k/. Unlike Japanese, the Korean language doesn’t distinguish long and short vowels. The Hanja is mostly like the traditional Chinese characters. But there are few differences. Korean: 强 敎 郞 凉 爲 Taiwan: 強 教 郎 涼 為 The vowel ㅐ sounds stronger than the loose ㅔ. But in spoken language, the difference is very subtle and interchangeable. Likewise, the ㅒ and ㅖ vowels. e: ㅐ ㅔ ye: ㅒ ㅖ The consonant /ㄹ/ is either /r/ or /l/. For example, /ra/ or /la/ has the same phonetic value. They are equal to 라. The sequence is in the Korean alphabetical order. There are more than 1800 Hanja and 2500 words in the app. 가：家, 佳, 街，... ... 하：下, 夏, 賀，... If you are interested in how to write the Korean words (Hangul), please refer to my another app "Learn Korean Handwriting !" There are over 77 million people who speak Korean worldwide. Its vocabulary is highly influenced by Chinese. The grammar is more similar to Mongolian, Manchu or Japanese languages.

Keras is relatively easy to learn and work with because it provides a python frontend with a high level of abstraction while having the option of multiple back-ends for computation purposes. This makes Keras slower than other deep learning frameworks, but extremely beginner-friendly. Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides. Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win. Built on top of TensorFlow 2, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod. It's not only possible; it's easy. Take advantage of the full deployment capabilities of the TensorFlow platform. You can export Keras models to JavaScript to run directly in the browser It's also easy to serve Keras models as via a web API. Keras is a central part of the tightly-connected TensorFlow 2 ecosystem, covering every step of the machine learning workflow, from data management to hyperparameter training to deployment solutions. Keras is used by CERN, NASA, NIH, and many more scientific organizations around the world (and yes, Keras is used at the LHC). Keras has the low-level flexibility to implement arbitrary research ideas while offering optional high-level convenience features to speed up experimentation cycles. Because of its ease-of-use and focus on user experience, Keras is the deep learning solution of choice for many university courses. It is widely recommended as one of the best ways to learn deep learning. Keras API reference Models API The Model class The Sequential class Model training APIs Model saving & serialization APIs Layers API The base Layer class Layer activations Layer weight initializers Layer weight regularizers Layer weight constraints Core layers Convolution layers Pooling layers Recurrent layers Preprocessing layers Normalization layers Regularization layers Attention layers Reshaping layers Merging layers Locally-connected layers Activation layers Callbacks API Base Callback class ModelCheckpoint BackupAndRestore TensorBoard EarlyStopping LearningRateScheduler ReduceLROnPlateau RemoteMonitor LambdaCallback TerminateOnNaN CSVLogger ProgbarLogger