Welcome to The ALT Code!

Kaggle image dataset used in the video: https://www.kaggle.com/c/carva....na-image-masking-cha

Aladdin Persson viedeo about UNET: https://youtu.be/oLvmLJkmXuc

In this video I build the UNET neural network architecture and use it for image segmentation in the Carvana Kaggle competition. Unfortunately, I couldn't train the model due to some tensorflow bugs, but I'll try to solve them and show you the result in an upcoming video. If you have any recommendation or advice leave it in the comments and I will gladly read it.

Video sections:
00:00 Intro
02:03 Start coding
04:25 Problems and bugs
05:24 Building nn architecture
09:24 More problems and bugs (same)

I hope you've liked the content. If so, remember to leave a like, comment and subscribe to the channel so you don't miss any video.
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My thoughts and some examples of Github Copilot.

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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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14 Cool applications just built on top of OpenAI's GPT-3 (generative predictive transformer) API (currently in private beta).

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( MongoDB Certification Training - https://www.edureka.co/mongodb )
This Edureka MongoDB video covers in-depth knowledge about how to design a schema in MongoDB & how to creates Data Models. The topics covered in this video are:

Data Modeling in MongoDB
Different Types of Data Models
Challenges in Designing Data Models in MongoDB
Real world use-case

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#edureka #MongoDB #DataModeling #SchemaDesign #MongoDBTutorial #MongoDBTraining

About the Course

This MongoDB® Training Certification course will help you master one of the most popular NoSQL databases. This course is designed to equip you with the skills of a Mongo DB experienced professional. You will gain knowledge in mastering data modeling, ingestion, query and Sharding, and much more, to become job ready. The course covers a range of NoSQL and MongoDB® topics which will help you gain various development, administering, and engineering skills.

Course Objective

Gain insight into the 'Roles' played by MongoDB® experts
Learn how to design Schema using Advanced Queries
Troubleshoot Performance issues
Understand MongoDB® Aggregation framework
Learn MongoDB® Backup and Recovery options and strategies
Understand scalability and availability in MongoDB® using Sharding
Set up a replicated cluster, Managing ReplicaSets, etc.
Learn Shards, Keys, Config Server, Query Router, etc.
Learn about various MongoDB® tools to develop and deploy your applications
Learn MongoDB® administration activities - Health Check, Backup, Recovery, Performance tuning, etc.
Understand Hadoop and MongoDB Integration
Perform Data Migration in MongoDB with Hadoop (MongoDB to Hive)
Learn to integrate MongoDB with tools like Jaspersoft and Pentaho
Integrate MongoDB with GUI Tool Robomongo
Manage MongoDB deployments using MongoDB Cloud products
Who should go for this course?
Database Admin
Database Server Engineer
Data Analytics and Visualization Engineer
Developer working on Web, Cloud, Mobile, and other social technologies
Applications Engineer
All topics related to ‘MongoDB’ have extensively been covered in our course ‘MongoDB Development and Administration’.

For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).

🔥Edureka Online Training: https://www.edureka.co
This Edureka video on 'Procedure Oriented Programming vs Object-oriented Programming' will help you know some of the most important differences between these two programming paradigms. Here are all the topics discussed in this video:
0:00 Introduction
1:11 What is a programming paradigm?
1:41 What is Procedure oriented Programming?
6:31 What is object-oriented programming?
13:42 POP vs OOP

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This Edureka's Blockchain video consists of the frequently asked Blockchain Interview Questions which will help you in the preparation of Blockchain Interviews.

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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

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This Edureka Data Science Tutorial (Data Science Blog Series: https://goo.gl/1CKTyN) will help you to learn Data Science concepts from scratch with examples. It starts with Why Data Science is needed, goes on to explain how a problem is solved in Data Science, and towards the end an awesome demonstration which involves Hadoop and R Integration! Below are the topics covered in this Data Science for Beginners tutorial:
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4) How a problem is solved in Data Science?
5) Data Science Components
6) Demo
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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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Is there any eligibility criteria for this program?
A potential candidate must have one of the following prerequisites: Degrees like BCA, MCA, and B.Tech.

About Course:
Why Learn Python for Data Science?
Python has been one of the premier, flexible, and powerful open-source languages that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, It has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. It's continued to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger.