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
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

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

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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


Related posts:

http://www.edureka.co/blog/importance-and-benefits-of-data-warehousing/?utm_source=youtube&utm_medium=referral&utm_campaign=types-dimension

http://www.edureka.co/blog/architecture-of-a-data-warehouse/?utm_source=youtube&utm_medium=referral&utm_campaign=types-dimension

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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‘.
For more information, please write back to us at [email protected]
Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐒𝐞𝐥𝐞𝐧𝐢𝐮𝐦 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠(Use code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") : https://www.edureka.co/seleniu....m-certification-trai
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:
00:00:00 Introduction
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?
00:02:39 Roadmap
00:04:22 Job Roles
00:07:11 Roles and Responsibilities
00:09:03 Skills
00:15:05 Companies Hiring Test Automation Engineer

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⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

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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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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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🔵 PMP Online Training: http://bit.ly/3XugO44
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🔵 Cybersecurity Online Training: http://bit.ly/3tXgw8t
🌕 Java Online Training: http://bit.ly/3tRxghg
🔵 Big Data Online Training: http://bit.ly/3EvUqP5
🌕 RPA Online Training: http://bit.ly/3GFHKYB
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🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC
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🌕 Professional Certificate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh

🌕 Artificial and Machine Learning PGD with E&ICT Academy
NIT Warangal: http://bit.ly/3OuZ3xs

📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 2023 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

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