What on earth Committing five bucks On a daily basis Resembles a By having Share Shares of stock

What investing a day looks like with dividend stocks. If you guys are new here, I like to talk about investing. A lot of people don’t know where to start. So today I’m going to give you a realistic approach of where to begin and what’s achievable. I like dividend investing myself because it’s super passive and you don’t have to maintain it or know how to run a business to start. The great thing about this is that you don’t have to initially put in thousands of dollars in hope to see if the business will be profitable or not. And that’s super risky when typically, a majority of new business fail within two year.

But this is not the case with companies that is already paying dividends. So, I want to show you a realistic approach how you can make your money work for you by investing a day. If you can just cut going out 2 times a week or stop drinking your regular Starbucks drink every day, you’ll make your money back in the future and you can even treat yourself to something even better.

So, let’s run some numbers real quick. a day. And for the sake of simplicity let say a month has 30 days. times 30 is 0 a month. That should be realistic right and it won’t cause too much trouble in your life.

Now I know there’s a lot of people and statistics that shows us that if you just invest in a broad index fund, you’ll get a yearly 7-10% return on your money, on average. But here’s the thing, let’s not try to aim for that kind of return. Let’s just be humble and shoot for a 5% yield a year.

Now we are going to use a compounding interest calculator for this. There’s a bunch of calculators you can use and the one I found was just a random one.

There’s obviously going to be some taxes but if you’re investing for the long term for retirement, you might want to use a Roth IRA to avoid taxes.

In order to open a Roth IRA you’ll first need to open a Webull Account:
Sign up for Webull for the 2 free stocks:

How To Open Up A Webull Account (Step-by-Step)

How to Easily Open a Roth IRA on Webull (Step-by-Step on Desktop)

How To Open A Roth IRA On Webull (Step-by-Step on Mobile)

How to Create a Million Dollar with a Roth IRA (1 Simple Strategy)

Now I know that this isn’t a million dollar or so for retirement and you might be dissuaded by all of this. By here’s the thing, you’re only investing a day or 0 a month. Over time you should be investing more once you make more money. This is for people who’s just starting out.

If you are interested in knowing where to find these dividend stocks check these videos out:

10 Best Dividend Paying Stocks 2020 (Dividend Kings)

How To Find High Dividend Blue Chip Stocks To Invest

My Dividend Growth Strategy (Full Breakdown)

Investing With Webull For Beginners Playlist (Tutorial on Opening Webull Account Also Inside):

Overall, I hope you are able to see how just by investing a day can have a greater impact in your future. Essentially you don’t have to work for the money, the money will work for you. If you have ,000 invested with a 5% dividend yield, you’ll basically get 500 for the year without having to sell stocks. That 0 could be your little mini vacation treat all paid for.

Legal Disclosure: I’m not a financial advisor nor a certify public accountant. These videos are for entertainment/educational purposes based off of my personal opinions. Investing in any type of investment involves risk and you need do your own research or seek out a licensed professional if necessary. There is no guarantee that you’ll gain or lose on investments.
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A lot of money can be made trading penny stocks. However, with great reward comes great risk! In this video, we’re going to look at 5 TOP ASX penny stocks for December 2020 and possibly into 2021 and determine when they are good buys.

Investing is for the long term, trading is for short to medium term. I don’t see these stocks as long term investments at this stage. Just potential setups for a good trade if we are prepared to be patient.

*Not Financial Advice – Always DYOR!*

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✔︎ Timestamps
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Make cash Buying in Carries by having Wallstreet Trapper

Leon Howard aka Wallstreet Trapper grew up in the turbulent streets of New Orleans. His mother was shot in front of him when he was 9 years old. He took to the street life early and was arrested and tried as an adult at the age of 16 for attempted murder and armed robbery. He was found guilty and sentenced to 10 years in Louisiana state penitentiary.

Link to Trapper University: https://www.thetrapperuniversity.com/?affcode=348432_5wox28qq

He was introduced to the stock market while he was incarcerated and fell in love with it instantly. He read every book and newspaper he could get his hands on and watched CNBC every morning religiously.

When he was released from jail he began working as an ironworker. While he was working he invested 70% of his income. After putting together a strategic plan he quit his job and became a full-time investor and entrepreneur. He now travels the country to teach other people with similar backgrounds about stocks and investing. He credits the stock market for changing his life and getting him out of the cycle of destruction that he was raised in. He now is on a mission to help educate as many people as possible about investing to provide a level of hope that he was not afforded as a youth.

He’s 100% self-educated but his knowledge in stocks is on par with an investment banker. In episode 44 he covered and explained every area of stocks in detail. He covered REIT’s, ETF’s, Indexed funds, he explained his strategy in evaluating a company to know if it’s a good stock to buy, he explained how to set up a stock account for a child and much more. He also explained how anyone can buy back their financial freedom by way of stock market investing for income. #stocks #investing #wallstreettrapper

Link to: Trapper University: https://www.thetrapperuniversity.com/?affcode=348432_qwoiqliv

Guest IG: @wall_street_trapper
Boot Tip: Wall Street Trappin 101, 102, & 103

Most preferred Using the net Moment in time Number Speculation By having Ignoring Data sales

Some recent over the internet the right time show guess by having omitted records auctions on auction:
[wprebay kilowatt=“on the web time period series assessment with the use of missing facts“ num=“0″ ebcat=“267″]

Developing a The right time Series Prediction Product by having parsnip & XGBoost | two (2) of two

The 2nd part of a tutorial from the #Shiny Web Apps Course – This video is from the Demand #Forecast section where our students build a predictive model to forecast sales demand with #parsnip & #XGBoost. Learn more: https://university.business-science.io/p/ds4b-102-r-shiny-web-application-business-level-1/

Get the ML Cheat Sheet with XGBoost Hyperparameters here: https://www.business-science.io/cheatsheet-machine-learning-regression.html

Continual Neural Networks like twitter (LSTM / RNN) Application by having Keras is that Python

#RNN #LSTM #RecurrentNeuralNetworks #Keras #Python #DeepLearning

In this tutorial, we implement Recurrent Neural Networks with LSTM as example with keras and Tensorflow backend. The same procedure can be followed for a Simple RNN.

We implement Multi layer RNN, visualize the convergence and results. We then implement for variable sized inputs.

Recurrent Neural Networks RNN / LSTM / GRU are a very popular type of Neural Networks which captures features from time series or sequential data. It has amazing results with text and even Image Captioning.

In this example we try to predict the next digit given a sequence of digits. Same concept can be extended to text images and even music.

Find the codes here
GitHub : https://github.com/shreyans29/thesemicolon
Good Reads : http://karpathy.github.io/

Check out the machine learning, deep learning and developer products

Data Science book Recommendations :

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Seq2seq Product on Time-series Data: Exercising and training and Having by using TensorFlow that in fact Masood Krohy

Masood Krohy at April 9, 2019 event of montrealml.dev

Title: Seq2seq Model on Time-series Data: Training and Serving with TensorFlow

Summary: Seq2seq models are a class of Deep Learning models that have provided state-of-the-art solutions to language problems recently. They also perform very well on numerical, time-series data which is of particular interest in finance and IoT, among others. In this hands-on demo/code walkthrough, we explain the model development and optimization with TensorFlow (its low-level API). We then serve the model with TensorFlow Serving and show how to write a client to communicate with TF Serving over the network and use/plot the received predictions.

Code on GitHub: https://github.com/patternedscience/time-series-tf-serving

Bio: Masood Krohy is a Data Science Platform Architect/Advisor and most recently acted as the Chief Architect of UniAnalytica, an advanced data science platform with wide, out-of-the-box support for time-series and geospatial use cases. He has worked with several corporations in different industries in the past few years to design, implement and productionize Deep Learning and Big Data products. He holds a Ph.D. in computer engineering.

This video is a production of PatternedScience Inc.
Website: https://www.patterned.science
LinkedIn: https://www.linkedin.com/company/patterned-science/
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WEKA API several/19: Having Analysis (Division)

To access the code go to the Machine Learning Tutorials Section on the Tutorials page here: http://www.brunel.ac.uk/~csstnns

Using WEKA in java

Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com

Digital Signal Processing (DSP) refers to the process whereby real-world phenomena can be translated into digital data for analysis, manipulation, and synthesis. DSP has transformed the way we interact with the physical world when it comes to recording sound, taking pictures, or analyzing other natural phenomena. None of this would be possible without the Fast Fourier Transform (FFT) algorithm, which allows huge amounts of data sampled in the time domain to be quickly transformed into the frequency domain. In this Digital Signal Processing Tutorial, we discuss implementations of the FFT in JavaScript and how we as developers can leverage JavaScript libraries such as DSP.js for signal analysis, synthesis, and manipulation.

Watch this video to learn:

– What is Digital Signal Processing (DSP)
– What is the Fast Fourier Transform (FFT) algorithm
– How does DSP work
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Marios Michailidis & Mathias Muller, Aqua.aje that in fact Time frame Group by having Water Driverless Ojai through Aqua World New york

This video was recorded in San Francisco on February 5th, 2019.

Slides from the session can be viewed here: https://www.slideshare.net/0xdata/marios-michailidis-mathias-muller-h2oai-time-series-with-h2o-driverless-ai-h2o-world-san-francisco

Driverless AI is H2O.ai’s latest flagship product for automatic machine learning. It fully automates some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and model deployment. Driverless AI turns Kaggle-winning grandmaster recipes into production-ready code, and is specifically designed to avoid common mistakes such as under- or overfitting, data leakage or improper model validation, some of the hardest challenges in data science. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high modeling accuracy.

Driverless AI is now equipped with time-series functionality. Time series helps forecast sales, predict industrial machine failure and more. With the time series capability in Driverless AI, H2O.ai directly addresses some of the most pressing concerns of organizations across industries for use cases such as transactional data in capital markets, in retail to track in-store and online sales, and in manufacturing with sensor data to improve supply chain or predictive maintenance.

Bio: Marios Michailidis is a Competitive Data Scientist at H2O.ai. He holds a Bsc in accounting Finance from the University of Macedonia in Greece, an Msc in Risk Management from the University of Southampton and a PhD in machine learning at from UCL . He has worked in both marketing and credit sectors in the UK Market and has led many analytics’ projects with various themes including: acquisition, retention, recommenders, fraud detection, portfolio optimization and more. He is the creator of KazAnova, a freeware GUI for credit scoring and data mining 100% made in Java as well as is the creator of StackNet Meta-Modelling Framework. In his spare time he loves competing on data science challenges and was ranked 1st out of 500,000 members in the popular Kaggle.com data competition platform. He currently ranks 3rd.

Bio: A Kaggle Grandmaster and a Data Scientist at H2O.ai, Mathias Müller holds an AI and ML focused diploma (eq. M.Sc.) in computer science from Humboldt University in Berlin. During his studies, he keenly worked on computer vision in the context of bio-inspired visual navigation of autonomous flying quadrocopters. Prior to H2O.ai, he as a machine learning engineer for FSD Fahrzeugsystemdaten GmbH in the automotive sector. His stint with Kaggle was a chance encounter as he stumbled upon the data competition platform while looking for a more ML-focused platform as compared to TopCoder. This is where he entered his first predictive modeling competition and climbed up the ladder to be a Grandmaster. He is an active contributor to XGBoost and is working on Driverless AI with H2O.ai.

In the first half of this video, Jo-Fai will share his joyful (yet sometimes very painful) Kaggle experience since joining the data mining competition platform. Coming from a rather traditional engineering background, data science was once like a complete myth to him. Joe will explain why participating in Kaggle is one of the most effective ways to kick-start a data science career. He will also explain how he used H2O for two Kaggle competitions: Rossmann Store Sales (2015) and Santander Product Recommendation (2016).

View slides here: http://bit.ly/2lsrD3F


Jo-fai (or Joe) Chow is a data scientist at H2O.ai. Before joining H2O, he was in the business intelligence team at Virgin Media in UK where he developed data products to enable quick and smart business decisions. He also worked remotely for Domino Data Lab in US as a data science evangelist promoting products via blogging and giving talks at meetups. Joe has a background in water engineering. Before his data science journey, he was an EngD research engineer at STREAM Industrial Doctorate Centre working on machine learning techniques for drainage design optimization. Prior to that, he was an asset management consultant specialized in data mining and constrained optimization for the utilities sector in UK and abroad. He also holds a MSc in Environmental Management and a BEng in Civil Engineering.

Abhishek’s Talk

In the second part of this talk, Abhishek will present his research in applying deep learning for time series prediction. He is focused on applying these new methods in the field of astronomy to light curves.

View slides here: http://bit.ly/2mLX4qF


Abhishek Malali is a Master’s of Engineering student at Harvard University specializing in Computational Sciences. He focuses on applying machine learning research to time series applications. Currently he is working on time series prediction on irregular time series using deep learning architectures.

Resulting in a Time period Series Forecast Unit by having parsnip & XGBoost | one of two

A quick tutorial from the #Shiny Web Apps Course – This video is from the Demand #Forecast section where our students build a predictive model to forecast sales demand with #parsnip & #XGBoost. Learn more: https://university.business-science.io/p/ds4b-102-r-shiny-web-application-business-level-1/

Get the ML Cheat Sheet with XGBoost Hyperparameters here: https://www.business-science.io/cheatsheet-machine-learning-regression.html