Let's Talk About Organic Learning!

When I tried to learn a foreign language in the past, it seemed to be a rude awakening at the end of the course. When the course ends, when the tapes finish or the course ends, new information can not be obtained. To be irritated is that I know that I spend a lot of time learning languages. And after the intense learning method, I can not keep building the foundation that I know.

Mango's organic learning program is designed to help users continue to add vocabulary and cultural insight beyond the mango program! The focus of these courses is to teach you various useful questions and to ask users about words they do not know, ask for additional information about specific topics and get explanations from the people they are talking about In order to use important communication. , And more. The more you implement the organic learning, the more you will learn after the course!

Intuitive language construction integrates vocabulary, pronunciation, grammar, culture, and understanding and retention strategies into organic learning making Mango a fun, easy and highly effective resource for foreign languages ​​and ESL users I will!

You can not teach you about each of the thousands of words that make up the language, but you can teach you how to find this information when using that language. For example, you may find yourself in Spain and you may not know the name of the particular fruit you want to buy in the farmers market. After using mango, you can say fruit and say: "Come to Spanish with scorpion (pointing at the object)?" How do you say this in Spanish? (Text translation: this is what is called Spanish). Now you can not only learn correct words but it also makes it possible for you to join in the conversation and improve your communication skills. If you ask me, this is a convenient situation for both sides.

Demand-based information - Vocabulary and structure in many classrooms have never been used in everyday conversation. Speaking organic words (people speaking) allows your brain to concentrate on the most commonly used words. Learning based on what you need to know than learning about the case means fewer words are forgotten and less wasteful. Built-in feedback loop - feedback from the environment is the basis of quick learning. Immersion has an effective feedback-based feedback system "I do not know" "This is not fun". If you are hurting your head or discovering your eyes, you will soon report that what you are doing is not for you. This is too easy (boring) or too hard (can not understand)

Let's talk about meta-learning. It is a confusing topic. I wrote an article discussing the decomposition of meta-learning and "learning to learn". I noticed that there is another meta-learning more familiar to practitioners. This meta-learning can be understood as an algorithm for searching and selecting different DL architectures. Hyperparameter optimization is an example of this, but there are other finer algorithms that follow the same search architecture.

Let's talk about good news first. The first is a surprising discovery that learning can be learned using a neural network (meta learning). More specifically, several research groups trained a neural network to perform a stochastic gradient descent method (SGD). In addition to being able to demonstrate neural networks that they learned SGD, network performance is also superior to manually adjusted human approaches! The two papers submitted were "Deep Intensive Learning to Improve Convergence Rates" and "Optimization as a Minority Learning Model". Unfortunately, these two groups were created by Deep Mind before and this shows that you can do this in the article 'Learning stage learning with Gradient Descent'. The last two papers trained LSTM and the first paper was trained through RL. I originally thought that it would take more time to implement meta-learning, but it was much faster than I thought!