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Japan, known as Nihon, Nihon-koku or Nippon (日本) in Japanese, is a nation of islands in East Asia.

The "Land of the Rising Sun" is a country where the past meets the future. Japanese culture stretches back millennia, yet has also been quick to adopt and create the latest modern fashions and trends.

Japan is often difficult to understand for those educated in the west. It can seem full of contradictions. Many Japanese corporations dominate their industries, yet if you read the financial news it seems like Japan is practically bankrupt. Cities are as modern and high tech as anywhere else, but tumbledown wooden shacks can still be spotted next to glass fronted designer condominiums. Japan has beautiful temples and gardens which are often surrounded by garish signs and ugly buildings. The most acclaimed restaurant in the country, which costs hundreds of dollars for dinner, is a small shop located in a subway station seating less than a dozen people. In the middle of modern skyscrapers you’ll discover sliding wooden doors which lead to traditional chambers with tatami mats, shoji screens, and calligraphy, suitable for traditional tea ceremonies. These juxtapositions can seem perplexing or jarring to those used to the more uniform nature of European and North American cities, but if you let go, and accept the layered aesthetics, you’ll find interesting and surprising places throughout the country.

Japan has often been seen in the West as a land combining tradition and modernity, and many traditional structures and practices are preserved, but modern structures and practices definitely dominate your experience in Japan. Japan was the first Asian country to independently modernize, and the country continues to embrace new technologies and aesthetics, but unlike in many countries, Japan does not feel a particular need to attack or remove older technologies, structures, or practices. New things are mostly just layered beside old things. That’s not to say that Japan embraces the large scale preservation of historical structures or that people generally practice traditional ceremonies, but people generally believe that if a small number of people want to continue on a tradition or preserve a building that they own, they should be allowed to do that. In this way, development mostly happens in a piecemeal fashion, one building at a time, rather than in large redevelopment projects. Many urban blocks evolve to line up dozens of narrow buildings spanning fifty or more years of design history. Clothing styles evolve along a dozen paths at the same time rather than singular mass fashion trends. An individual that embraces a particular subculture and its fashions may alternately conform to vary different norms when working or at home, but there is little sense of conflict between these roles.

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This is my solution to lrCostFunction.m function in Programming assignment 3 from the famous Machine Learning course by Andrew Ng.

Github: https://github.com/AladdinPerz....on/Courses/tree/mast

In this tutorial we build a Sequence to Sequence (Seq2Seq) with Attention model from scratch in Pytorch and apply it to machine translation on a dataset with German to English sentences, specifically the Multi30k dataset.

Resources to learn more:
https://github.com/bentrevett
https://youtu.be/SysgYptB198
https://youtu.be/quoGRI-1l0A
https://arxiv.org/abs/1409.0473
https://pytorch.org/tutorials/....intermediate/seq2seq

Comment on resources:
I think bentrevett on Github is awesome and this series of videos was heavily inspired in this video by his Seq2Seq Tutorials and I really recommend checking him out, he puts out a lot of great tutorials on his Github.

People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
ML Course (affiliate): https://bit.ly/3qq20Sx

DL Specialization (affiliate): https://bit.ly/30npNrw
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Detectives are at a loss in solving 25 consecutive weeks of mysterious murders and suicides in a not-so-distant future Los Angeles when a sudden break leads them closer to the truth.

Dispatcher is a passion project of mine developed as a pitch pilot and was my first narrative indie film effort captured and delivered in 8K. It premiered and was well received in Las Vegas and Los Angeles in 2017.

As always, best experienced in a quiet darker room, full screen, ideally in 4K or 8K.

If you have inquiries, reach me at phfx.com.

Music licensed via MusicBed.

Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics.

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Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency.

Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words.

One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word.

The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector.

Two popular tools:
Word2Vec: https://code.google.com/archive/p/word2vec/
Glove: http://nlp.stanford.edu/projects/glove/

Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse.

Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language.

Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis:

“He turned around a team otherwise known for overall bad temperament”

In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive.

Credits
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Basic mathematics from a language model? Rob Miles on GPT3, where it seems like size does matter!

More from Rob Miles: http://bit.ly/Rob_Miles_YouTube

https://www.facebook.com/computerphile
https://twitter.com/computer_phile

This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham: https://bit.ly/nottscomputer

Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi *****

Role playing dimensions, slowly changing dimensions, conformed dimensions, degenerate and junk are some of the types of dimensions that this video speaks about. It gives an insight to each of the types of dimensions.
Topics covered are as follows:
1. Types of dimensions
2. Slowly changing dimensions
3. Conformed dimensions
4. Degenerate dimension
5. Junk dimension
6. Role playing


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Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics related to ‘Types of Dimension’ have been covered in our course ‘Data warehousing‘.
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This Edureka video on "how to become a test automation engineer" will provide you with detailed and comprehensive knowledge on how to become a successful Test Automation Engineer. It will guide you through the path one should take with the appropriate skills and the roles and responsibilities of Test Automation Engineer.
Topics Covered:
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00:01:07 What is Software Testing
00:01:57 Who is a Test Automation Engineer
00:02:18 What does a Test Automation Engineer do?
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00:07:11 Roles and Responsibilities
00:09:03 Skills
00:15:05 Companies Hiring Test Automation Engineer

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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐂𝐲𝐛𝐞𝐫 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 : https://www.edureka.co/cyberse....curity-certification 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This Edureka live session on "How to become a Cybersecurity Engineer" covers all the basic aspects of becoming a certified Cybersecurity Engineer.
Below is the list of topics covered in this session:
00:00:00 Introduction
00:00:28 Agenda
00:00:59 Cybersecurity as a profession
00:02:15 Job roles in cybersecurity
00:02:30 Who is a cybersecurity Engineer
00:03:30 Cybersecurity engineer - Roles & Responsibilities
00:05:50 Cybersecurity Engineer - Skills
00:08:22 Cybersecurity engineer career pathway
00:11:24 Cybersecurity Engineer Salary

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