What You Need to Know about Deep Learning
Within the family of computerized learning, you might think of “deep learning” as the gifted child of machine learning. Climb further up the family tree, and you’ll find Artificial Intelligence as the revered grandfather of them all.
Here’s how it breaks down.
At the top of the hierarchy is Artificial Intelligence (AI). AI is designed to perform tasks as programmed, i.e., it is fed a sophisticated algorithm and programming and is let loose on things like massive data sets. AI can be trained to find patterns, solve puzzles, improve efficiencies, and so on.
Below AI you will find “machine learning,” which derives from AI. As the AI gathers and analyzes huge amounts of information, it can use new information to learn and refine its knowledge of a process and its execution of a task. Over time, the algorithm gets ‘smarter.’ In essence, machine learning focuses on solving real-world problems with neural networks designed to mimic a super-human’s own decision-making.
Deep learning is the final tier in this hierarchy, but also the most ambitious. Deep learning derives from machine learning but can be compared to you learning something new on your own. Through its own algorithm and computing work, deep learning is essentially using its own brain, known as its “Deep Neural Network,” to solve just about any problem which requires “thought” – human or artificial.
These deep neural networks are designed to operate just as the neural networks found in the human brain. These networks – logical constructions which ask a series of binary true/false questions, or extract a numerical value, of every bit of data which pass through them—can classify information according to the answers received. In this sense, deep learning involves feeding a computer system a lot of data, which it can use to make decisions about other data.
Deep learning can be applied to any form of data – machine signals, computer vision, audio, video, social network filtering, bioinformatics and drug design, speech, written words, the list goes on – to produce nearly immediate conclusions that, to the unknowing eye, probably seem as if they had been arrived at by humans who spent a lot of time thinking about the conclusion.
Where is Deep Learning being Used?
Deep learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decipher your interests and figure out what you should watch or buy next, and even by researchers at MIT to try and predict the future.
Where deep learning is likely to have the most immediate impact is in the field of autonomous, self-driving vehicles. As the vehicles make more trips and store more experiences (data), it will apply that new data to becoming an even better driver, and avoiding the same mistakes twice (unlike humans).
But there are so many more and endless uses for deep learning. A system recently developed by a team of British and American researchers was shown to be able to correctly predict a court’s decision, once it was fed the basic facts of the case. Deep learning will also be the driver of gains in precision medicine, as it can better understand which medicines work better for a very specific version of a disease on a very particular type of person. The list goes on.
Contributor
Sergey Savastiouk, Ph.D. has a degree in Applied Mathematics from Moscow University and has extensive experience as an entrepreneur, investor, manager, and mathematician. His professional expertise is in applied mathematics, mathematical modeling, system and pattern analysis, and software and hardware system integration. He has served as the CEO of several hi-tech start-up companies and nonprofit organizations, which has given him proven capabilities in business strategy for high-tech start-up companies, market assessment, company formation, team building, product development, marketing, and sales. He has published numerous articles in journals and magazines on related fields. As a retail investor, he spent 15 years developing his proprietary trading and quantitative algorithms (now Tickeron’s A.I.), which brought him significant returns in trading the stock market. His current work and goal in founding Tickeron is to bring professional, sophisticated stock market analysis capabilities to retail investors via an easy-to-use interface.
NFLX in downward trend: 10-day moving average moved below 50-day moving average on September 22, 2026
The 10-day moving average for NFLX crossed bearishly below the 50-day moving average on September 22, 2026. This indicates that the trend has shifted lower and could be considered a sell signal. In 10 of 14 past instances when the 10-day crossed below the 50-day, the stock continued to move higher over the following month. The odds of a continued downward trend are 71%.
Technical Analysis (Indicators)
Bearish Trend Analysis
The Momentum Indicator moved below the 0 level on September 04, 2026. You may want to consider selling the stock, shorting the stock, or exploring put options on NFLX as a result. In 43 of 78 cases where the Momentum Indicator fell below 0, the stock fell further within the subsequent month. The odds of a continued downward trend are 55%.
The Moving Average Convergence Divergence Histogram (MACD) for NFLX turned negative on September 04, 2026. This could be a sign that the stock is set to turn lower in the coming weeks. Traders may want to sell the stock or buy put options. Tickeron's A.I.dvisor looked at 43 similar instances when the indicator turned negative. In 29 of the 43 cases the stock turned lower in the days that followed. This puts the odds of success at 67%.
NFLX moved below its 50-day moving average on September 17, 2026 date and that indicates a change from an upward trend to a downward trend.
Following a 3-day decline, the stock is projected to fall further. Considering past instances where NFLX declined for three days, the price rose further in 50 of 62 cases within the following month. The odds of a continued downward trend are 71%.
The Aroon Indicator for NFLX entered a downward trend on October 06, 2026. This could indicate a strong downward move is ahead for the stock. Traders may want to consider selling the stock or buying put options.
Bullish Trend Analysis
The RSI Oscillator points to a transition from a downward trend to an upward trend -- in cases where NFLX's RSI Oscillator exited the oversold zone, 29 of 39 resulted in an increase in price. Tickeron's analysis proposes that the odds of a continued upward trend are 74%.
The Stochastic Oscillator shows that the ticker has stayed in the oversold zone for 13 days. The price of this ticker is presumed to bounce back soon, since the longer the ticker stays in the oversold zone, the more promptly an upward trend is expected.
Following a +2.43% 3-day Advance, the price is estimated to grow further. Considering data from situations where NFLX advanced for three days, in 221 of 307 cases, the price rose further within the following month. The odds of a continued upward trend are 72%.
NFLX may jump back above the lower band and head toward the middle band. Traders may consider buying the stock or exploring call options.
Fundamental Analysis (Ratings)
The Tickeron SMR rating for this company is 21 (best 1 - 100 worst), indicating very strong sales and a profitable business model. SMR (Sales, Margin, Return on Equity) rating is based on comparative analysis of weighted Sales, Income Margin and Return on Equity values compared against S&P 500 index constituents. The weighted SMR value is a proprietary formula developed by Tickeron and represents an overall profitability measure for a stock.
The Tickeron Price Growth Rating for this company is 64 (best 1 - 100 worst), indicating fairly steady price growth. NFLX’s price grows at a lower rate over the last 12 months as compared to S&P 500 index constituents.
The Tickeron Valuation Rating of 85 (best 1 - 100 worst) indicates that the company is significantly overvalued in the industry. This rating compares market capitalization estimated by our proprietary formula with the current market capitalization. This rating is based on the following metrics, as compared to industry averages: P/B Ratio (9.560) is normal, around the industry mean (18.873). P/E Ratio (21.770) is within average values for comparable stocks, (91.420). Projected Growth (PEG Ratio) (1.183) is also within normal values, averaging (3.965). Dividend Yield (0.000) settles around the average of (0.005) among similar stocks. P/S Ratio (6.798) is also within normal values, averaging (2.968).
The Tickeron Profit vs. Risk Rating rating for this company is 91 (best 1 - 100 worst), indicating that the returns do not compensate for the risks. NFLX’s unstable profits reported over time resulted in significant Drawdowns within these last five years. A stable profit reduces stock drawdown and volatility. The average Profit vs. Risk Rating rating for the industry is 76, placing this stock worse than average.
The Tickeron PE Growth Rating for this company is 95 (best 1 - 100 worst), pointing to worse than average earnings growth. The PE Growth rating is based on a comparative analysis of stock PE ratio increase over the last 12 months compared against S&P 500 index constituents.
The Tickeron Seasonality Score of 95 (best 1 - 100 worst) indicates that the company is significantly overvalued in the industry. The Tickeron Seasonality score describes the variance of predictable price changes around the same period every calendar year. These changes can be tied to a specific month, quarter, holiday or vacation period, as well as a meteorological or growing season.
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