Learn about the limitations of RNNs, how LSTMs work, and Gated Recurrent Units (GRUs).

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In this video, we will learn about Automatic text generation using Tensorflow, Keras, and LSTM. Automatic text generation is the generation of natural language texts by computer. It has applications in automatic documentation systems, automatic letter writing, automatic report generation, etc. In this project, we are going to generate words given a set of input words. We are going to train the LSTM model using William Shakespeare’s writings.

Long Short-Term Memory (LSTM) networks are a modified version of recurrent neural networks, which makes it easier to remember past data in memory. Generally, LSTM is composed of a cell (the memory part of the LSTM unit) and three “regulators”, usually called gates, of the flow of information inside the LSTM unit: an input gate, an output gate and a forget gate. Intuitively, the cell is responsible for keeping track of the dependencies between the elements in the input sequence. The input gate controls the extent to which a new value flows into the cell, the forget gate controls the extent to which a value remains in the cell and the output gate controls the extent to which the value in the cell is used to compute the output activation of the LSTM unit. The activation function of the LSTM gates is often the logistic sigmoid function. There are connections into and out of the LSTM gates, a few of which are recurrent. The weights of these connections, which need to be learned during training, determine how the gates operate.

🔊 Watch till last for a detailed description
01:20 Text generation using TensorFlow, Keras, and LSTM
05:28 Get started with code
27:53 Build LSTM model and prepare x and y
42:25 LSTM model

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Make your own speech recognition app using MIT app inventor and control gadgets, electrical appliances, and robots. This app will allow you to control any gadgets I’ve made a working video of this app controlling electrical devices using arduino and HC-05 bluetooth module,. check out the video here https://www.youtube.com/watch?v=EJ0WEkG6zuY

circuit diagram and arduino program can find in the link http://mitappsinventor.blogspot.in/p/blog-page.html

In our conversations with developers, business, and industry leaders all over the world, we hear four patterns emerging on how to apply AI to businesses and business scenarios that provide a useful frame for the conversation. Hear Corporate Vice President Steve Guggenheimer discuss these patterns.

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The battle for the 2020 US Presidential election has begun. The Republican nominee Donald Trump and the Democratic nominee Joe Biden are fighting for a place in the White House. In this video, we will analyze the US elections using Twitter Sentiment Analysis in Python. You will understand how to extract tweets, store it in a CSV file, examine the tweets’ polarity, and visualize the results. Finally, you will build a word cloud based on the tweets for Trump and Biden.

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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.

What skills will you learn from this Machine Learning course?

By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems

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🔥NIT Warangal Post Graduate Program in AI & Machine Learning with Edureka: https://www.edureka.co/nitw-ai-ml-pgp
This Edureka “Stock Prediction using Machine Learning” takes you through the basic process of predicting the trends of stock prices using machine learning architecture of LSTM while also making use of prominent Python Libraries such as Tensorflow, Keras, etc. Topics covered in the tutorial are as follow:
01:30 Introduction to Stock Prediction
03:00 LSTM Architecture
05:35 Stock Prediction Model
26:35 Conclusion

🔸Dataset & code: https://bit.ly/2x6BPe0

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Why Machine Learning & AI?

Because of the increasing need for intelligent and accurate decision making, there is an exponential growth in the adoption of AI and ML technologies. Hence these are poised to remain the most important technologies in the years to come.
PG Program in Machine Learning & AI

1. Ranked 4th among NITs by NIRF
2. Ranked among Top 50 Institutes in India
3. Designated as Institute of National Importance
Program Features

1. Mentorship from NITW faculty
2. Placement Assistance
3. Alumni Status
4. Industry Networking
Industry Projects

1. Building a Conversational ChatBot
2. Predictive Model for Auto Insurance
3. E-commerce Website – Sales Prediction
Mentors & Instructors

Dr. RBV Subramaanyam
Professor NITW

Dr. DVLN Somayajulu
Professor NITW

Dr. P. Radha Krishna
Professor NITW

Dr. V. Ravindranath
Professor JNTU Kakinada
Is this program for me?

If you’re passionate about AI & ML and want to pursue a career in this field, this program is for you. Whether you’re a fresher or a professional, this program is designed to equip you with the skills you need to rise to the top in a career in AI & ML.

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 or Programming experience Should have studied PCM in 10+2

Will I get any certificate at the end of the course?

Yes, you will receive a Post-Graduate industry-recognized certificate from E & ICT Academy, NIT Warangal upon successful completion of the course.
For more information, Please write back to us at sales@edureka.in or call us at IND: +91-9606058418 / US: 18338555775 (toll-free).

We’ve developed a new framework for reinforcement learning, a subset of machine learning. This video shows the framework applied to an autonomous RC car that learns to drift around a truck.

Graph enhancements to AI and ML are changing the landscape of intelligent applications. In this webinar, we’ll focus on using graph feature engineering to improve the accuracy, precision, and recall of machine learning models. You’ll learn how graph algorithms can provide more predictive features as well as aid in feature selection to reduce overfitting. We’ll illustrate a link prediction workflow using Spark and Neo4j to predict collaboration and discuss our missteps and tips to get to measurable improvements.

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🔥🔥Intellipaat Machine Learning course: https://www.youtube.com/watch?v=4gqZLajDWh8

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Are you looking for something more? Enroll in our machine learning full course and become a certified professional (https://intellipaat.com/machine-learning-certification-training-course/). It is a 32 hrs instructor led machine learning training provided by Intellipaat which is completely aligned with industry standards and certification bodies.

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Why should you watch this machine learning video?

Machine learning is one of the fastest growing arms of the domain of artificial intelligence. It has far reaching consequences and in the next couple of years we will be seeing every industry deploying the principles of artificial intelligence, machine learning and deep learning technologies at scale.

Why machine learning is important?

Machine learning might just be one of the most important fields of science that we are just moving towards. It differs from other science in the sense that this is one of the one domains where the input and output are not directly correlated and neither do we provide the input for every task that the machine will perform. It is more about mimicking how humans think and solving real world problems like humans without actually the intervention of humans. It focuses on developing computer programs that can be taught to grown and change when exposed to data.
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Using AutoML NLP (Natural Language Processing) to classify and predict multilabel texts with a custom model. The demo consists of 3 parts:
– Uploading dataset
– Training
– Evaluating results and prediction

Download .csv here: https://cloud.google.com/natural-language/automl/docs/sample/happiness.csv

Thank you!

It’s time to revamp the way that businesses communicate and help their customers amid the COVID-19 pandemic. Virtual assistants can help clinical teams, businesses, and communities stay informed and safe during this historical time period. Google’s Rapid Response Virtual Agent is a chatbot that adds massive scale to support any business team, and in this video you’ll easily learn how to set up this chatbot using a simple template.

GitHub → https://goo.gle/39Sv1hs
Rapid Response Virtual Agent → https://goo.gle/3c5SoFM
Build and deploy virtual agent rapidly with Dialogflow → https://goo.gle/2UUEctb
Dialogflow documentation → https://goo.gle/2V5SiH7
Contact Center AI → https://goo.gle/2JMbW5G
Get Started with Google Maps → https://goo.gle/2V2dJZK

For more videos like these, please subscribe → https://goo.gle/GCP

Product: Contact Center AI, Rapid Response Virtual Agent; fullname: Priyanka Vergadia;

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Yuaho Zheng, Director of Engineering at DataVisor, talks about building a fraud detection platform using AI and Big Data at the 2019 AWS Santa Clara Summit.

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** Python Certification Training: https://www.edureka.co/python **
This Edureka video on ‘Speech Recognition in Python’ will cover the concepts of speech recognition module in python with a program using speech recognition to translate speech into text. Following are the topics discussed:

How Speech Recognition Works?
How To Install SpeechRecognition In Python?
Working With Microphones
How To Install Pyaudio In Python?
Use case

Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Reference: https://youtu.be/avH1P41-jh8

#Edureka #PythonEdureka #PythonSpeechrecognition #pythonprojects #pythonprogramming #pythontutorial #speechtotext #speechtotextinpython #PythonTraining

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How it Works?
1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate!

– – – – – – – – – – – – – – – – –
About the Course

Edureka’s Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:

1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
– – – – – – – – – – – – – – – – – – –

Why learn Python?

Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next “Big Thing” and a must for Professionals in the Data Analytics domain.

Who should go for python?

Edureka’s Data Science certification course in Python is a good fit for the below professionals:

· Programmers, Developers, Technical Leads, Architects

· Developers aspiring to be a ‘Machine Learning Engineer’

· Analytics Managers who are leading a team of analysts

· Business Analysts who want to understand Machine Learning (ML) Techniques

· Information Architects who want to gain expertise in Predictive Analytics

· ‘Python’ professionals who want to design automatic predictive models

For more information, Please write back to us at sales@edureka.in or call us at IND: 9606058406/ US: 18338555775 (toll free)

The age of the digital assistant is upon us and as these AI helpers attempt to interface with humans companies like Apple and Google are trying anything to make their interactions more organic. Brent Rose takes Google Assistant, Amazon Echo, Microsoft Cortana and Apple’s Siri out for the ultimate test drive; which AI has the best sense of humor? How will a human audience respond to a stand-up set written entirely by smart gadgets?

Tech writer Brent Rose is on a quest. With a surplus of emerging technologies and scientific discoveries popping up how do we separate facts from hype? Brent takes the goods out of the box—and out of the office—to find out how the new wave of cultural phenomena really holds up.

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WIRED is where tomorrow is realized. Through thought-provoking stories and videos, WIRED explores the future of business, innovation, and culture.

Stand-Up Comedy Using Only Siri, Alexa, Cortana and Google Home | OOO with Brent Rose | WIRED

MechE’s Conrad Tucker is democratizing the access to knowledge by using technology that most people already have access to. He explains how his work with commercial devices like cell phones can change the world of virtual reality for the better.

Covid-19, or the novel coronavirus, has resulted in a pandemic that has reached across the globe. Both the virus and the response to it has impacted individuals, businesses and countries in unprecedented ways… the worlds of payments and fintech are no exception.

In times of general societal and economic upheaval, payments fraud can spike. This can affect organizations, particularly financial institutions, in significant ways. Fortunately tools like AI technology can offer solutions which can protect both consumers and businesses.

Helping us explore AI technology and how it can combat payments fraud is our guest this week, Daniel Faggella. Daniel is the head of Research and CEO of Emerj Artificial Intelligence Research. Emerj helps those in banking, finance, and government navigate AI by cutting through the artificial intelligence hype, leveraging proven best-practices, and making data-backed decisions about mission-critical priorities. Listen in as Dan breaks it all down!

Find show notes and more at: https://www.soarpay.com/podcast/

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Hey Guys,
Hope you enjoying my AI tutorials using Keras and Tensorflow.

This is the video for facial emotion recognition using CNN.
Transfer learning is the best way to perform such a complicated task.
For this task, we will classify the emotions from the frame coming directly through your webcam or any external live camera.

This is a realtime emotion detection easy tutorial using python and Keras.

You can use this video as realtime emotion detection using python.

Please do share and subscribe for more interesting videos.

Dataset :- https://drive.google.com/open?id=1E66iZdNz021aUZGsZjtc3EUu3NqAaIq3

Source Code :- https://github.com/code-by-dt/emotion_detection

Facial Landmark Detection OpenCV Too Easy Tutorial https://youtu.be/16bzzVaqKCk

Computer Vision Programs :- https://www.youtube.com/playlist?list=PLgNUGWgXIL4pWASWqSdAvYupEocaktF2D


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Dan Faggella founded a Brazilian Jiu-Jitsu Academy to pay for college and grad school which sparked the idea for his eCommerce business, Science of Skill. Science of Skill was created for the sole purpose of eventually selling it and using the exit money to fund an artificial intelligence market research company. He grew Science of Skill past $2 million in revenue, sold it in 2017, and TechEmergence was born completely funded by the exit. If you listen, you will learn: How recurring revenue helps the value of your business Importance of finding a marketing channel that ensures consistent profitability and growth Having key employees that can run the company without the owner can increase the value of the company and provide for a smoother transition/exit Importance of knowing how the processes of your company tie into growth and profit How to figure out and analyze the core metrics of your business Finding the right broker makes a ton of difference when selling a business Building Up Science of Skill Dan went from owning a physical Jiu-Jitsu gym to starting an eCommerce business. He started taping the Jiu-Jitsu seminars he was giving in person and putting them on the internet. He took the curriculum he used and studied himself and turned the videos, information, and skill development exercises into monthly subscription based lessons which would allow the company to have recurring revenue. “I had it impressed on me early on that recurring revenue is a good thing. Recurring revenue is going to help you sell for a higher margin. Recurring revenue is going to give you less nightmare than businesses that don’t have recurring revenue. The only reason I was able to sell that gym was because it was recurring so I think that lesson kind of traveled over. I transferred that to an online business model I could run from anywhere,” explains Dan. The Jiu-Jitsu videos and information quickly extended out into general self-defense and self-protection lessons which attracted a broader audience to Science of Skill making the company more diverse and valuable to a buyer. Starting With The End In Mind TechEmergence was always the goal at the end of the tunnel. Dan wanted to start a purpose led business in AI technology by growing and selling something highly profitable. He spent half his time giving Ted Talks, writing articles, and building a media business around artificial intelligence and spent the other half of his time selling self-defense materials on the internet. Dan wanted to sell Science of Skill quickly to fund his AI research company. He thought it would take a year or so and ultimately, it took over three years. He didn’t realize when founding the eCommerce business that it would be so difficult to sell. “I thought I am going to flip this bad boy and call it a day,” says Dan. Dan was under what he calls a “naïve belief” that he would be able to sell the company easily. In reality, he had to grow the company for four years before a bank would facilitate a seven-figure transaction. The banks required three years of tax returns and valuations. “Banks don’t like stuff that involve the internet,” says Dan. He did find a buyer who had previous experience in the eCommerce space with a bank that backed them. They purchased Science of Skill in early 2017. It was three years longer than what Dan originally planned and was anxious to get TechEmergence going on more of a full-time basis.  Lessons Learned Dan learned a lot throughout the process of starting and selling his eCommerce business. Not only shouldn’t you expect to sell a small company a year after founding it, you also shouldn’t expect a “glamorous exit” unless you have a strategic buyer in your space, proprietary list, or something more than just your cash flow. “One of those naïve notions was that we could get some decent multiple of profit to sell the company. It is unusual for a small business to sell for that much more than 3X profit,” says Dan. Another lesson was that finding the right broker makes a ton of difference. The broker Dan dealt with had previously sold similar eCommerce businesses for around what he hoped for with his exit. Two of Dan’s biggest takeaways that allowed him to successfully scale and exit Science of Skill involved marketing and his team. Figuring out the marketing channel that worked for them was extremely important for growth. They invested heavily in marketing once they nailed down that email marketing was their channel. The next biggest takeaway was the importance of training a good team. “The owner shouldn’t be the guy responsible for bringing money in the front door,” says Dan. Training his team to be able to know and do everything he could allowed him to step away from the company and use his exit money to fund his next venture that had been brewing since the beginning. Contact Information and Bio for Dan: LinkedIn: https://www.linkedin.com/in/danfaggella/ Twitter:…

Fallout 4 – Live Action Opening

Colorized using DeOldify [AI Machine Learning] [IMPRESSIVE RESULTS]

DeOldify is an AI Neural Network software that is capable of colorizing black and white images using thousands of pictures as references.

I took the original video file from the Fallout 4 PC disk, and used DeOldify with the Video training model to restore the
color from the FMV.

This is my second attempt at colorizing this video, and the results are much more natural-looking and stable.


“There’s a proliferation of unstructured data. Companies collect massive amounts of news feed, emails, social media, and other text-based information to get to know their customers better or to comply with regulations. However, most of this data is unused and untouched. Natural language processing (NLP) holds the key to unlocking business value within these huge data sets, by turning free text into data that can be analyzed and acted upon. Join this tech talk and learn how you can get started mining text data effectively and extracting the rich insights it can bring. We will also demonstrate how you can build a text analytics solution with Amazon Comprehend and Amazon Relational Database Service.

Learning Objectives:
– Get an introduction to Natural Language Processing (NLP)
– Learn benefits of new approaches to analytics and technologies that help empower better decisions, e.g., NLP, data prep
– Build a text analytics solution with Amazon Comprehend and Amazon Relational Database Service in a step by step demo”

This video shows a robot picking oranges from a farm. There are no sound in the video because i wanted to show the viewers what the robot is thinking, while picking oranges.

Building a robot to pick oranges isn’t as simple as identifying ripe oranges and putting them into a basket. There are company rules the robot has to follow in order to pick oranges. A few years ago, a employee working for this farming company was climbing a tall orange tree and he accidently fell and broke his leg. The farming company was sued for 1 million dollars in medical bills. This prompted the farm company to issue a rule that states no employee can climb a tree to pick oranges.

In addition to this rule, there are many other rules employees have to follow. The robot has to remember and follow all rules and common sense rules that are not stated in the company policies.

At the beginning, the robot generates a overall search strategy for his search area (lines 4, 5, and 6).

In the video, the robot has to devise search strategies for each type of tree. Since orange trees come in different shapes and sizes, the robot has to generate new search strategies for each tree. For example, a tall tree is searched differently from a small tree.

For more information about human level artificial intelligence, visit my website:

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Google can predict when you’ll die using artificial intelligence Google knows everything (or at least it feels that way), and now it can even tell you when it’s going to die.

The technology giant helped test an artificial intelligence computer system that can predict whether hospital patients will die 24 hours after admission.

What’s more surprising is that the tests put the accuracy of AI predictions at up to 95 percent.

It works by chewing data about patients, such as their age, ethnicity and gender.

This information is then combined with information from the hospital, such as previous diagnoses, current vital signs and any laboratory results.

And what makes the system particularly accurate is that data is typically fed out of the reach of machines, such as doctors’ notes hidden in graphics or PDF files.

Artificial intelligence systems become smarter over time through a process known as machine learning.

The AI ​​was developed by a team of Stanford researchers, the University of Chicago and UC San Francisco.

Then, Google took the AI ​​system and “taught” it using unidentified data from 216,221 adults from two US medical centers. UU

This meant that AI had more than 46 billion data points to aspire to.

Over time, the AI ​​was able to associate certain words with a result (ie, life or death), and understand how likely (or unlikely) it was for someone to die.

What is particularly exciting about the Google system is that researchers can shed almost any kind of information about it.

Stanford professor Nigam Shah told Bloomberg that about 80 percent of the development time devoted to predictive models continues to make the data look presentable for AI.

In this broadcast, originally aired at SXSW 2019, leaders in the business of applying AI will discuss a wide array of business models that have been used to successfully generate crucial training data, how this data has been used, and what that means for your business. We’ll talk through the cost profile of building the data set, identify common technical challenges, and discuss the pros and cons of each approach.

Vegetables Recognition Using Image Processing on Android Device
Student Project 2016
Ratchawut Keunmamuang
Sethikarn Shotuk

Hi! My name is Andre and this week, we will focus on text classification problem. Although, the methods that we will overview can be applied to text regression as well, but that will be easier to keep in mind text classification problem. And for the example of such problem, we can take sentiment analysis. That is the problem when you have a text of review as an input, and as an output, you have to produce the class of sentiment. For example, it could be two classes like positive and negative. It could be more fine grained like positive, somewhat positive, neutral, somewhat negative, and negative, and so forth. And the example of positive review is the following. “The hotel is really beautiful. Very nice and helpful service at the front desk.” So we read that and we understand that is a positive review. As for the negative review, “We had problems to get the Wi-Fi working. The pool area was occupied with young party animals, so the area wasn’t fun for us.” So, it’s easy for us to read this text and to understand whether it has positive or negative sentiment but for computer that is much more difficult. And we’ll first start with text preprocessing. And the first thing we have to ask ourselves, is what is text? You can think of text as a sequence, and it can be a sequence of different things. It can be a sequence of characters, that is a very low level representation of text. You can think of it as a sequence of words or maybe more high level features like, phrases like, “I don’t really like”, that could be a phrase, or a named entity like, the history of museum or the museum of history. And, it could be like bigger chunks like sentences or paragraphs and so forth. Let’s start with words and let’s denote what word is. It seems natural to think of a text as a sequence of words and you can think of a word as a meaningful sequence of characters.

So, it has some meaning and it is usually like,if we take English language for example,it is usually easy to find the boundaries of words because in English we can split upa sentence by spaces or punctuation and all that is left are words.Let’s look at the example,Friends, Romans, Countrymen, lend me your ears;so it has commas,it has a semicolon and it has spaces.And if we split them those,then we will get words that are ready for further analysis like Friends,Romans, Countrymen, and so forth.It could be more difficult in German,because in German, there are compound words which are written without spaces at all.And, the longest word that is still in use is the following,you can see it on the slide and it actually stands forinsurance companies which provide legal protection.So for the analysis of this text,it could be beneficial to split that compound word intoseparate words because every one of them actually makes sense.They’re just written in such form that they don’t have spaces.The Japanese language is a different story.

www.myoutdesk.com Watch this video of Myoutdesk’s raving clients where they describe working with a Virtual Assistant. You’ll hear them speak about their good and bad experiences of having a Virtual Assistant as part of their team.

This Video about Real Estate Virtual Assistants helps prospective clients understand how top agents are using Virtual Assistants to close hundreds of deals each year. These are not single agents, rather quite successful business owners talking about what their Virtual assistant has done to help them achieve or keep their “Top Agent” status

At one point in the Video Stace Bohlender describes how his Virtual assistant worked through the last hurricane they had, showing pictures of water flowing through his house, listen to him as he describes what he saw from his VA first hand.

This video features:
Stace Bohlender
Tracy Combs
Chris Upham
Kyle Miller
Jeremy Mellick

Speech Recognition using Python

Learn how to convert audio into text using python.

Code here : https://github.com/umangahuja1/Youtube/blob/master/Python_Extras/speech.py

Stay Tuned 🙂

Follow here for more

Quora : https://getsetpython.quora.com/
Facebook : https://www.facebook.com/getsetpython/
Twitter : https://twitter.com/umangahuja_1

Through life changing accidents, and data minded through NASCAR, human beings are finding ways to rebuild one another so that we are better, faster, and stronger than ever before and all with the help of A.I.. Once nothing more than the stuff of comic books and TV shows, we truly have the technology to become modern superheroes.

The Age of A.I. is a 8 part documentary series hosted by Robert Downey Jr. covering the ways Artificial Intelligence, Machine Learning and Neural Networks will change the world.

You choose — watch all episodes uninterrupted with YouTube Premium now, or wait to watch new episodes free with ads. Learn more at: https://support.google.com/youtube/answer/6358146

Check out YouTube Premium at: https://www.youtube.com/premium/originals

See if Premium is available in your country at: https://support.google.com/youtube/answer/6307365

Navkarsys.com presents another video explaining how to setup the software, how to generate license key and how to download data and generate reports using realsoft attendance management software.

Prof. Emily Mower Provost (Computer Science & Engineering, University of Michigan) talks at the Michigan AI Symposium 2018.

With the new age of silent, stealthy attacks that lie low in networks for weeks and months, legacy approaches like rules and signatures are proving inadequate on their own. New ‘immune system’ technologies based on advanced mathematics and machine learning are being deployed today.
Real-world examples of subtle, unknown threats that routinely bypass traditional controls would be addressed in this session.

Carl Salji
Technical Director
United Kingdom

Dr. Andrei Borshchev, CEO at The AnyLogic Company, presents at the GE EDGE & Controls Symposium 2019. Topics with video time links below:

– 4:46 What is simulation modeling? What is AnyLogic?
– 8:02 Digital Twins. 10:37 Case study: Gas Turbine Fleet
– 14:08 Why AI and Simulation? Some AI terminology. How can simulation help AI?
– 21:21 Example of Deep Reinforcement Learning using simulation: Traffic Light Control (detailed)
– 29:53 Case study: AI trained by simulation model in Ferromagnetic Core Production
– 32:26 Conclusion. What are the challenges?

AI white paper – https://www.anylogic.com/resources/white-papers/artificial-intelligence-and-simulation-in-business/
Find out more about simulation modeling – https://www.anylogic.com/

#AnyLogic #Simulation #AI #DynamicSimulation #DigitalTwin

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