My Best Buy Learning Network
CCNA certification proves you have what it takes to navigate the ever-changing landscape of IT. CCNA exam covers networking fundamentals, IP services, security fundamentals, automation and programmability. Designed for agility and versatility, CCNA validates that you have the skills required to manage and optimize today's most advanced networks.
my best buy learning network
Your free Cisco Learning Network membership includes free study resources to supplement your learning journey. Access training videos, webinars and the CCNA Community, where you can ask technical questions, join discussions, and receive study tips to help you achieve your CCNA.
Network engineers are responsible for designing, implementing, monitoring and managing the local and wide area networks of an organization to ensure maximum uptime for users. The role can include the following tasks:
Network engineers will work in house or be assigned to project management teams working with outside clients. As part of an organization's IT team, network engineers work closely with business analysts, network architects and IT managers. A network engineer job description may include the following duties and responsibilities:
Depending on the size of the company, network administrators might be responsible for supporting the desktop computers as well as other networked devices. Network administrators must have a thorough understanding of switches, routers and networked communications.
CompTIA now offers a number of exam training options for CompTIA Network+ to fit your particular learning style and schedule, many of which may be used in combination with each other as you prepare for your exam.
In addition to the write-ups, the Guide categorizes each college by price and the average amount of debt that students accumulate. Each school is also rated on a one-to-five scale in three categories: academics, social life, and quality of life. Other useful lists include the schools that no longer require the ACT or SAT for admissions and the colleges with strong programs for students with learning disabilities.
GCChub GCChub is a platform or organization that aims to connect "the right people" with "the best projects." By doing this, GCChub hopes to bring attention to projects that may have otherwise gone unnoticed by the community. Vision: That's...
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Smart thermostats offer a variety of features that will not only help keep your home comfortable, but can reduce heating and cooling costs. The most basic models are relatively inexpensive but are still equipped with Wi-Fi radios that allow you to connect the device to your home network and the internet. They can be controlled from a mobile device such as a smartphone or tablet using a mobile app, and some models also let you control things from a web browser.
ATLAS (Apple Technical Learning Administration System) is AppleCare Service Training's website for online training and other learning resources. Use ATLAS to prepare for the certifications required to service Apple products.
Ideally the network would learn how to output buy and sell signals such that the difference between the buy and sell signals, ie. profit, is maximized. For example when price is trending upward it would issue a buy, and when it starts to slope down a sell occurs. The optimized network would adjust itself to buy at the lowest point and sell at the highest point possible.
There will be several scenarios for example where the stock is trending up, but buying isn't the best decision to take. At the very least, you should have three target values to train your network (1: Buy, 0: Wait, -1: Sell) - but even if you did that, your approach is still problematic because your conflating a prediction problem and a decision/optimization problem.
2) You might be better off using reinforcement learning instead of conventional neural networks. W/R to your problem, reinforcement learning has two advantages: In reinforcement learning, a model is trained to maximize a target function, as opposed to conventional neural networks where the model is trained to minimize a loss function. And reinforcement learning is better suited for problems where the goal is a long term goal, compared to supervised learning.
It's almost a truism to say that in any practical problem, it's not sufficient to throw neural networks at a problem and expect to strike it rich. Often, constructing better features is the harder, and most profitable, part of developing a better machine learning model.
Framed a different way, how do you plan to distill all relevant market data and present it to a neural network? Historical data is only a part of the puzzle, since, as we all know, past performance is no guarantee of future returns. A price bar for a security is just part of the picture. What do you make of the Fed's plan to raise interest rates in 2019? Also, it appears that a yield-curve inversion could be on the horizon, and that often precedes recessions. The same goes for declines in microchip demand. How does your model "price in" that information?
An example of using reinforcement learning for stock trades can be found in Maxim Lapan, Deep Reinforcement Learning Hands-On. The code for that chapter (and the rest of the book) is on Github.. (Full disclosure: I've made minor contributions to the repository.)
Reinforcement learning is an attractive way to frame the problem, since the core task of RL is to train an agent how to make optimal decisions (profit) when interacting with an environment (the stock market). Estimating discounted future rewards is a part of reinforcement learning objectives, and is the machine learning analogue to market gurus (Jim Cramer, Warren Buffet, whoever) making guesses predictions about market moves.
Additionally, RL is probably a better framework than supervised learning because labeling a financial time series and providing it to a model seems both labor-intensive and pointless. The goal isn't to build a model that reproduces a particular analyst's hindsight, but to build a model that can correctly estimate future return and act accordingly. In this scenario, we don't have the rewards data available - but by running experiments, the agent can (hopefully) learn optimal actions.
Marcos López de Prado's Advances in Financial Machine Learning (2018) is something of a how-to manual for the sorts of people who have a few million dollars lying around to fund a multi-year R&D effort. It outlines how using machine learning in finance is fundamentally distinct from funding active managers, and how the task requires a multi-disciplinary approach (such as high-performance computing specialists, machine learning experts, programmers, data engineers).
It's easier to start learning about machine learning on simpler problems, or problems that we understand well. I have a political science degree; when I started doing machine learning in earnest, I would work on problems concerning elections data, because I understand the underlying material very well. Unless you consider yourself a finance expert, I'd start with a topic that's a little simpler. Or, at a minimum, I'd study the core elements of financial modeling, including ARIMA models, CAPM and Fama-French for a start. David Ruppert's Statistics and Data Analysis for Financial Engineering provides a good overview of these topics among others.
We tested the best VPN services -- focusing on the number of servers, ability to unlock streaming services, and more -- to determine a No. 1 overall. Plus, we tell you whether free VPNs are worth trying.
By far, the standout feature of any Synology NAS is the company's exceptional DiskStation Manager (DSM) software. While the Synology hardware is on-par with many other NAS offerings, it's when that hardware is combined with the DSM software that Synology's offerings become best-in-class.
Western Digital has a pretty broad selection of NAS devices, but we realized we hadn't provided you with a lower-cost entry-level unit. At under $200 (without drives), the EX2 can add considerable value to your network, provide you with some fault tolerance, and help protect your data.
App support wasn't as comprehensive as some of the other vendors we're spotlighting here, but if this is your first NAS or you're just getting started sharing and protecting files on your home network, this is a great go-to starting point.
We had some issues five years ago with an early WD RAID device, but all indications are that the company best known for its network drives has overcome any early reliability issues. In fact, the biggest complaint we found among user reviews was that this is not an external hard drive, as the reviewer thought it was, but was instead a full-function NAS. It is, in fact, a very nice full-function NAS and gets our nod as best entry-level NAS.
It's kind of odd that Drobo hasn't updated its one NAS storage array since 2017, but that goes to Drobo's main focus as a direct-attached storage solution. Even though it's been around for a while, the Drobo 5N2 has to go into our list of the best NAS devices, chiefly because its RAID functionality is just so good.
Let's clarify where this device fits: If you want a server with lots of apps and features, the Drobo is absolutely not for you. But if you want brain-dead easy RAID that keeps your drives safe and available on your LAN, and you don't really care about much else, the 5N2 is a win. The Drobo justifiably won my best-in-show award for RAID performance, which was flawless in my testing. It also landed at the very bottom for network features, so you win some, and you lose some. Go ahead and read and watch my full review for the in-depth details. 041b061a72