What are the advantages of learning German?

The official languages of Germany, Austria, and Liechtenstein are German, also known as German. The Indo-European Germanic cluster includes modern German, English, and Flemish. The German language is currently the world's 12th most widely spoken language, with 130 million speakers. It is frequently used as a language of instruction and is one of Western countries' most important cultural languages.

It is important to learn it

In recent years, German has been the most popular language to learn. It can be used for business, socialisation, cultural immersion, economic gain, or simply as a recreational activity. We'll also go over some other factors.

German is easy to learn: Mark Twain sums it up with the following quote: "Never knew before what eternity was made for." It's to allow us to learn German. For an English-speaking person, it is quite easy. 

It is used widely throughout Europe: English, German French, Spanish, and German are the official languages of the European Union (EU).
High-quality education is available: If you're interested in studying abroad, it is a smart idea to study German. German universities are known for their high-quality education and professional environments. GISMA Business School is an example. GISMA has programs for postgraduate and graduate students. AMBA accreditation has been awarded in many areas since 2011.

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Germany is an economic powerhouse: Germany is fourth in the EU, fourth in the world. It will improve communication and help you to build productive professional relationships. GISMA Business School has close ties with top-tier German organisations like Uniqlo Continental, Volkswagen Nutzfahrzeuge, and Bornemann. After completing their programs, students can apply for employment in these companies.
German companies are leaders in their fields: Germany is home to many multinational corporations such as Adidas, Lufthansa Volkswagen and Bosch BMW.
Enjoy the rich German culture: Does Kant, Kafka, Beethoven ring a bell? If so, it is because they are the most important novelists and composers.
Great online presence: Germany is home to approximately 6% of all internet content. Germany's.de domain has nearly 8 million domains and is second only to.com.

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How to speak German

You may want to learn German for many reasons: With clever hacks, strategies and tricks, basic German can be learned.

In your daily life, use the language: There are many options.

  • Search for people who speak German to start a conversation.
  • Make German your default language. This will make it easier to learn German words and phrases.
  • German sitcoms and movies can be viewed with or without subtitles.

Every day, learn the German language: Studies show that practicing 15 minutes a day of something is more beneficial than spending hours doing it all at once.
Learn about the topics that interests you: This is one method to learn German.
Learn German in groups: Group study can help you improve your listening, speaking and pronunciation skills.
Do your homework before going to bed: According to a study, sleeping helps the brain process and store information. German can be thought in.

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German tips and tricks

There are many ways to learn German. Make the most of the internet. You can now learn German online. 

  • Language apps: German language apps like 'Duolingo' and 'Fluentu aid language learners in learning the language through short passages and videos.
  • Use German podcasts: German podcasts are a great way for you to learn German, or to improve your pronunciation.
  • Flashcards can be a great way of learning: German-speaking people are using flashcards to learn sentences and phrases.

Why should learn German?

  • German has surpassed English and Spanish as Europe's most widely spoken language.
  • German is one of the world's most widely spoken languages. It is also the most widely spoken language in Central and Eastern Europe. But what about the claim that "all Germans speak English"? This is a fabrication.
  • There have been 92 Nobel Prizes awarded, with many more on the way! In physics, 30 in chemistry, and 25 in medicine, these three countries have received 22 Nobel Prizes. Other Nobel Laureates attended German universities. German-language authors received 11 Nobel Prizes in Literature. The Peace Prize was also given to seven Germans and seven Austrians.
  • German engineers are among the best in the world.
  • Both English and German have a lot in common. Many German terms have similar sounds and appearances to their English counterparts. They have the same "grandparent," Haus, which means "home," and Buch, which means "books." Finger is the same as finger. Hand equals hand Mother is the name of the mother. Mutter is the feminine form of the word mother.
  • Schwamen is a German word that means "to swim." Singen is the same as singen. The word "kommen" means "to come." The word blau refers to the colour blue. Alt is short for "alternative." Windig is a German word that means "windy."
  • Many of the most respected filmmakers of the twentieth century came from German-speaking countries. Rainer Werner Fassbinder, Fritz Lang, Wim Wenders, and a generation of international directors like Fatih Akin and Tom Tykwer are among them. Austrian and German filmmakers such as Lang, Billy Wilder, Ernst Lubitsch, and others developed and affected Hollywood's history.

To learn German Language from professionals, Visit Sevenmentor.



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What are the advantages of learning German?
Jerad  Bailey

Jerad Bailey


Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

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sophia tondon

sophia tondon


5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Visit Blog- https://www.xplace.com/article/8743

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Jackson  Crist

Jackson Crist


Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

  • Temporal difference learning (TD learning)
  • Parameters
  • QL & SARSA
  • Comparison
  • Implementation
  • Conclusion

We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning

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Few Shot Learning — A Case Study (2)

In the previous blog, we looked into the fact why Few Shot Learning is essential and what are the applications of it. In this article, I will be explaining the Relation Network for Few-Shot Classification (especially for image classification) in the simplest way possible. Moreover, I will be analyzing the Relation Network in terms of:

  1. Effectiveness of different architectures such as Residual and Inception Networks
  2. Effects of transfer learning via using pre-trained classifier on ImageNet dataset

Moreover, effectiveness will be evaluated on the accuracy, time required for training, and the number of required training parameters.

Please watch the GitHub repository to check out the implementations and keep updated with further experiments.

Introduction to Few-Shot Classification

In few shot classification, our objective is to design a method which can identify any object images by analyzing few sample images of the same class. Let’s the take one example to understand this. Suppose Bob has a client project to design a 5 class classifier, where 5 classes can be anything and these 5 classes can even change with time. As discussed in previous blog, collecting the huge amount of data is very tedious task. Hence, in such cases, Bob will rely upon few shot classification methods where his client can give few set of example images for each classes and after that his system can perform classification young these examples with or without the need of additional training.

In general, in few shot classification four terminologies (N way, K shot, support set, and query set) are used.

  1. N way: It means that there will be total N classes which we will be using for training/testing, like 5 classes in above example.
  2. K shot: Here, K means we have only K example images available for each classes during training/testing.
  3. Support set: It represents a collection of all available K examples images from each classes. Therefore, in support set we have total N*K images.
  4. Query set: This set will have all the images for which we want to predict the respective classes.

At this point, someone new to this concept will have doubt regarding the need of support and query set. So, let’s understand it intuitively. Whenever humans sees any object for the first time, we get the rough idea about that object. Now, in future if we see the same object second time then we will compare it with the image stored in memory from the when we see it for the first time. This applied to all of our surroundings things whether we see, read, or hear. Similarly, to recognise new images from query set, we will provide our model a set of examples i.e., support set to compare.

And this is the basic concept behind Relation Network as well. In next sections, I will be giving the rough idea behind Relation Network and I will be performing different experiments on 102-flower dataset.

About Relation Network

The Core idea behind Relation Network is to learn the generalized image representations for each classes using support set such that we can compare lower dimensional representation of query images with each of the class representations. And based on this comparison decide the class of each query images. Relation Network has two modules which allows us to perform above two tasks:

  1. Embedding module: This module will extract the required underlying representations from each input images irrespective of the their classes.
  2. Relation Module: This module will score the relation of embedding of query image with each class embedding.

Training/Testing procedure:

We can define the whole procedure in just 5 steps.

  1. Use the support set and get underlying representations of each images using embedding module.
  2. Take the average of between each class images and get the single underlying representation for each class.
  3. Then get the embedding for each query images and concatenate them with each class’ embedding.
  4. Use the relation module to get the scores. And class with highest score will be the label of respective query image.
  5. [Only during training] Use MSE loss functions to train both (embedding + relation) modules.

Few things to know during the training is that we will use only images from the set of selective class, and during the testing, we will be using images from unseen classes. For example, from the 102-flower dataset, we will use 50% classes for training, and rest will be used for validation and testing. Moreover, in each episode, we will randomly select 5 classes to create the support and query set and follow the above 5 steps.

That is all need to know about the implementation point of view. Although the whole process is simple and easy to understand, I’ll recommend reading the published research paper, Learning to Compare: Relation Network for Few-Shot Learning, for better understanding.

#deep-learning #few-shot-learning #computer-vision #machine-learning #deep learning #deep learning