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.
NFLX in +2.54% Uptrend, advancing for three consecutive days on December 24, 2024
Moving higher for three straight days is viewed as a bullish sign. Keep an eye on this stock for future growth. Considering data from situations where NFLX advanced for three days, in of 315 cases, the price rose further within the following month. The odds of a continued upward trend are .
Technical Analysis (Indicators)
Bullish Trend Analysis
The Stochastic Oscillator suggests the stock price trend may be in a reversal from a downward trend to an upward trend. of 54 cases where NFLX's Stochastic Oscillator exited the oversold zone resulted in an increase in price. Tickeron's analysis proposes that the odds of a continued upward trend are .
The Momentum Indicator moved above the 0 level on December 24, 2024. You may want to consider a long position or call options on NFLX as a result. In of 83 past instances where the momentum indicator moved above 0, the stock continued to climb. The odds of a continued upward trend are .
The Aroon Indicator entered an Uptrend today. In of 299 cases where NFLX Aroon's Indicator entered an Uptrend, the price rose further within the following month. The odds of a continued Uptrend are .
Bearish Trend Analysis
The 10-day RSI Indicator for NFLX moved out of overbought territory on December 12, 2024. This could be a bearish sign for the stock. Traders may want to consider selling the stock or buying put options. Tickeron's A.I.dvisor looked at 44 similar instances where the indicator moved out of overbought territory. In of the 44 cases, the stock moved lower in the following days. This puts the odds of a move lower at .
The Moving Average Convergence Divergence Histogram (MACD) for NFLX turned negative on December 10, 2024. 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 48 similar instances when the indicator turned negative. In of the 48 cases the stock turned lower in the days that followed. This puts the odds of success at .
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 of 62 cases within the following month. The odds of a continued downward trend are .
NFLX broke above its upper Bollinger Band on December 06, 2024. This could be a sign that the stock is set to drop as the stock moves back below the upper band and toward the middle band. You may want to consider selling the stock or exploring put options.
The Tickeron Price Growth Rating for this company is (best 1 - 100 worst), indicating outstanding price growth. NFLX’s price grows at a higher rate over the last 12 months as compared to S&P 500 index constituents.
The Tickeron Profit vs. Risk Rating rating for this company is (best 1 - 100 worst), indicating low risk on high returns. The average Profit vs. Risk Rating rating for the industry is 85, placing this stock better than average.
The Tickeron SMR rating for this company is (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 PE Growth Rating for this company is (best 1 - 100 worst), pointing to consistent 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 Valuation Rating of (best 1 - 100 worst) indicates that the company is slightly 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 (12.920) is normal, around the industry mean (5.729). P/E Ratio (51.065) is within average values for comparable stocks, (92.419). Projected Growth (PEG Ratio) (1.889) is also within normal values, averaging (2.987). Dividend Yield (0.000) settles around the average of (0.040) among similar stocks. P/S Ratio (8.190) is also within normal values, averaging (29.638).
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