I Sold Astounding Sony Stocks and shares Now & Owned…For what reason?

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Well today I sold all my Apple stocks and put that money in Tesla Stock…
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We Put $10,000 Right now & We’re Relaying Our Store Collection (Discuss. 2 through Surplus Income)

Books that have helped us on our Financial Independence Journey
—–
The Bogleheads‘ Guide to Investing: https://amzn.to/2UjEc4j
The Intelligent Investor: https://amzn.to/2MKGokh
A Random Walk Down Wall Street: https://amzn.to/2zIVI8p
The Book on Rental Property Investing: https://amzn.to/2UkwgQ6
Building Wealth One House at a Time: https://amzn.to/2ZJQuDW
Rich Dad Poor Dad: https://amzn.to/2ZD29IK
The Total Money Makeover: https://amzn.to/2Lc0thn
The 0 Startup: https://amzn.to/2UnBpqK
Freakonomics: https://amzn.to/2HCnDeJ

Our Rich Journey, We Invested ,000 Today & We’re Sharing Our Stock Portfolio (Ep. 1 – Dividend Income): We decided to invest ,000 in high dividend stocks. We selected ten stocks and we’re sharing those stocks and our portfolio with you. In this video, we share with you the ten stocks that we invested in, our criteria for picking the stocks that we selected, and we show you our gains and losses on the stocks to date. We’ll also be investing an additional 3 each month (roughly ,000 a year). So, each month, we’ll share our high dividend individual stock portfolio with you, including our gains and losses!

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Check out our most recent Our Rich Journey videos:
End Of Year Money Moves – 2018: https://youtu.be/HmHNAHMLdYs
Achieving Financial Independence in a Consumer Culture: https://youtu.be/Mfgq0VI6U5Q
How to Retire Early & Afford Healthcare (Less than 0/month for Family): https://youtu.be/pApBuNJZflU
Recession Coming – How to Thrive: https://youtu.be/XEAmANYN4kY
Exposing Bad Money Advice: https://youtu.be/DFIGzRyoIbg

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TensorTraffic that in fact traffic prophecy by using equipment getting to know that Pawel Gora

Description
Together with an international group of scientists, I am developing TensorFlow-based tool, TensorTraffic, for predicting traffic simulation outcomes, such as delays / total waiting times on a red signal, as a function of traffic control settings. We achieved great results, which may find applications in transport planning, real-time traffic management and many other fields.

Abstract
Together with an international group of scientists, I am developing TensorFlow-based tool, TensorTraffic, for predicting traffic simulation outcomes, such as delays / total waiting times on a red signal, as a function of traffic control settings. We achieved great results, which may find applications in transport planning, real-time traffic management and many other fields.

First, I will talk about a goal of the research and problem we would like to solve, which is: approximating traffic simulation outcomes fast and with an acceptable accuracy. The motivation behind this goal is to have a meta-model of traffic, which can allow to effectively evaluate quality of a large number of settings (e.g., traffic control strategies or road infrastructure settings).

Then, I will explain our approach and experiemnts. We built a dataset consisting of almost 120 000 traffic simulation scenarios, using Traffic Simulation Framework software, each case corresponded to a different traffic signal control strategy, simulations computed the total waiting time of all cars on a red signal in a given region of Warsaw (Stara Ochota district, 15 crossroads with traffic signals). Then, we built meta-models based on machine learning, in order to approximate outcomes of traffic simulations faster and with an acceptable accuracy. First, we obtained very good accuracy of predictions using neural networks (average error 1.18%), later we even improved these results using XGBoost (average error lower than 1%). What is important, results of approximations can be obtained a few orders of magnitude faster than by running traffic simulations.

Finally, I will tell about conclusions and possible directions of a future research. I will show how achieved results may be applied in real-time traffic management and transport planning, which may lead to significant reduction of the required time and cost of traffic engineering works, as well as better results in terms of traffic efficiency. Thus, this may lead to developing AI-based tools able to support traffic engineers in their work. An interesting conclusion is that similar approach may be used in many other cases, in which it is required to run a large number of simulations, e.g., weather forecast, medicine, bioinformatics. There may be also important theoretical conclusions, related to automata theory and computational complexity.

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Introduction of Valuable time Combination of Projecting | Step 7 (seven) | ARIMA Forecasting real life Example in R

Introduction of Time Series Forecasting | Part 7 | ARIMA Forecasting real life Example in R

Hi guys.. in this part 6 of time series forecasting video series I have taken a real life example of rain fall in india and predicted the future years rains with by producing the arima model and then using the forecast package, I predicted the next few years rain fall values.

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