This type of chart is used as a trading tool to visualise and analyse the price movements over time for securities, derivatives, currencies, stocks, bonds, commodities, etc. Although the symbols used in Candlestick Charts resemble a Box Plot, they function differently and therefore, are not to be confused with one another.Candlestick Charts display multiple bits of price information such as the open price, close price, highest price and lowest price through the use of candlestick-like symbols. Each symbol represents the compressed trading activity for a single time period (a minute, hour, day, month, etc). Each Candlestick symbol is plotted along a time scale on the x-axis, to show the trading activity over time. The main rectangle in the symbol is known as the real body, which is used to display the range between the open and close price of that time period. While the lines extending from the bottom and top of the real body is known as the lower and upper shadows (or wick). Each shadow represents the highest or lowest price traded during the time period represented. When the market is Bullish (the closing price is higher than it opened), then the body is coloured typically white or green. But when the market is Bearish (the closing price is lower than it opened), then the body is usually coloured either black or red. Candlestick Charts are great for detecting and predicting market trends over time and are useful for interpreting the day-to-day sentiment of the market, through each candlestick symbol’s colouring and shape. For example, the longer the body is, the more intense the selling or buying pressure is. While, a very short body, would indicate that there is very little price movement in that time period and represents consolidation. Candlestick Charts help reveal the market psychology (the fear and greed experienced by sellers and buyers) through the various indicators, such as shape and colour, but also by the many identifiable patterns that can be found in Candlestick Charts. In total, there are 42 recognised patterns that are divided into simple and complex patterns. These patterns found in Candlestick Charts are useful for displaying price relationships and can be used for predicting the possible future movement of the market. You can find a list and description of each pattern here. Please bear in mind, that Candlestick Charts don’t express the events taking place between the open and close price – only the relationship between the two prices. So you can’t tell how volatile trading was within that single time period. The post Data Visualization Explained: Candlestick Chart appeared first on Data Science PR. Via https://datasciencepr.com/data-visualization-explained-candlestick-chart/?utm_source=rss&utm_medium=rss&utm_campaign=data-visualization-explained-candlestick-chart Every once in a while, I see a tweet or post that asks whether one should use tool X or software Y in order to “make their data analysis reproducible”. I think this is a reasonable question because, in part, there are so many good tools out there! This is undeniably a good thing and quite a contrast to just 10 years ago when there were comparatively few choices. The question of toolset though is not a question worth focusing on too much because it’s the wrong question to ask. Of course, you should choose a tool/software package that is reasonably usable by a large percentage of your audience. But the toolset you use will not determine whether your analysis is reproducible in the long-run. I think of the choice of toolset as kind of like asking “Should I use wood or concrete to build my house?” Regardless of what you choose, once the house is built, it will degrade over time without any deliberate maintenance. Just ask any homeowner! Sure, some materials will degrade slower than others, but the slope is definitely down. Discussions about tooling around reproducibility often sound a lot like “What material should I use to build my house so that it never degrades*?” Such materials do not exist and similarly, toolsets do not exist to make your analysis permanently reproducible. I’ve been reading some of the old web sites from Jon Claerbout’s group at Stanford (thanks to the Internet Archive), the home of some of the original writings about reproducible research. At the time (early 90s), the work was distributed on CD-ROMs, which totally makes sense given that CDs could store lots of data, were relatively compact and durable, and could be mailed or given to other people without much concern about compatibility. The internet was not quite a thing yet, but it was clearly on the horizon. But ask yourself this: If you held one of those CD-ROMs in your hand right now, would you consider that work reproducible? Technically, yes, but I don’t even have a CD-ROM reader in my house, so I couldn’t actually read the data. And a larger problem is that a CD from the 90s probably degraded to the point where it is likely unreadable anyway. Claerbout’s group obviously knew about the web and were transitioning in that direction, but such a transition costs money. As does keeping a keen eye on emerging trends and technology usage. Hilary Parker and I recently discussed the how the economics of academic research are not well-suited to support the reproducibility of scientific results. The traditional model is that a research grant pays for the conduct of research over a 3-5 year period, after which the grant is finished and there is no more funding. During (or after) that time, scientific results are published. While the funding can be used to prepare materials (data, software, and code) to make the published findings reproducible at the instant of publication, there is no funding afterwards for dealing with two key tasks:
These two activities (maintenance and support) can continue to be necessary in perpetuity for every study that an investigator publishes. The mismatch between how the grant funding system works and the requirements of reproducible research is depicted in the diagram below. ![]() When I say “value” in the drawing above, what I really mean is the “reproducibility value”. In the old model of publishing science, there was no reproducibility value because the work was generally not reproducible in the sense that data and code were available. Hence, this whole discussion would be moot. Traditional paper publications held their value because the text on the page did not generally degrade much over time and copies could easily be made. Scientists did have to field the occasional question about the results but it was not the same as maintaining access to software and datasets and answering technical questions therein. As a result, the traditional economic model for funding academic research really did match the manner in which research was conducted and then published. Once the results were published, the maintenance and support costs were nominal and did not really need to be paid for explicitly. Fast forward to today and the economic model has not changed but the “business” of academic research has. Now, every publication has data and code/software attached to it which come with maintenance and support costs that can extend for a substantial period into the future. While any given publication may not require significant maintenance and support, the costs for an investigator’s publications in aggregate can add up very quickly. Even a single paper that turns out to be popular can take up a lot of time and energy. If you play this movie to the end, it becomes soberingly clear that reproducible research, from an economic stand point, is not really sustainable. To see this, it might help to use an analogy from the business world. Most businesses have capital costs, where they buy large expensive things – machinery, buildings, etc. These things have a long life, but are thought to degrade over time (accountants call it depreciation). As a result, most businesses have “maintenance capital expenditure” costs that they report to show how much money they are investing every quarter to keep their equipment/buildings/etc. up to shape. In this context, the capital expenditure is worth it because every new building or machine that is purchased is designed to ultimately produce more revenue. As long as the revenue generated exceeds the cost of maintenance, the capital costs are worth it (not to oversimplify or anything!). In academia, each new publications incurs some maintenance and support costs to ensure reproducibility (the “capital expenditure” here) but it’s unclear how much each new publication brings in more “revenue” to offset those costs. Sure, more publications allow one to expand the lab or get more grant funding or hire more students/postdocs, but I wouldn’t say that’s universally true. Some fields are just constrained by how much total funding there is and so the available funding cannot really be increased by “reaching more customers”. Given that the budgets for funding agencies (at least in the U.S.) have barely kept up with inflation and the number of publications increases every year, it seems the goal of making all research reproducible is simply not economically supportable. I think we have to concede that at any given moment in time, there will always be some fraction of published research for which there is no maintenance or support for reproducibility. Note that this doesn’t mean that people don’t publish their data and code (they should still do that!), it just means they don’t support or maintain it. The only question is which fraction should *no*t be supported or maintained? Most likely, it will be older results where the investigators simply cannot keep up with maintenance and support. However, it might be worth coming up with a more systematic approach to determining which publications need to maintain their reproducibility and which don’t. For example, it might be more important to maintain the reproducibility of results from huge studies that cannot be easily replicated independently. However, for a small study conducted a decade ago that has subsequently been replicated many times, we can probably let that one go. But this isn’t the only approach. We might want to preserve the reproducibility of studies that collect unique datasets that are difficult to re-collect. Or we might want to consider term-limits on reproducibility, so an investigator commits to maintaining and supporting the reproducibility of a finding for say, 5 years, after which either the maintenance and support is dropped or longer-term funding is obtained. This doesn’t necessarily mean that the data and code suddenly disappear from the world; it just means the investigator is no longer committed to supporting the effort. Reproducibility of scientific research is of critical importance, perhaps now more than ever. However, we need to think harder about how we can support it in both the short- and long-term. Just assuming that the maintenance and support costs of reproducibility for every study are merely nominal is not realistic and simply leads to investigators not supporting reproducibility as a default. Source: SimplyStatistics The post Asymptotics of Reproducibility in 2020 appeared first on Data Science PR. Via https://datasciencepr.com/asymptotics-of-reproducibility-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=asymptotics-of-reproducibility-in-2020 IDE stands for Integrated Development Environment.IDE is basically a software pack that consist of equipment’s which are used for developing and testing the software. A developer throughout SDLC uses many tools like editors, libraries, compiling and testing platforms. IDE helps to automate the task of a developer by reducing manual efforts and combines all the equipment’s in a common framework. If IDE is not present, then the developer has to manually do the selections, integrations and deployment process. IDE was basically developed to simplify the SDLC process, by reducing coding and avoiding typing errors. In contrast to the IDE, some developers also prefer Code editors. Code Editor is basically a text editor where a developer can write the code for developing any software. Code editor also allows the developer to save small text files for the code. In comparison to IDE, code editors are fast in operating and have a small size. In fact code editors possess the capability of executing and debugging code. The post What is Integrated Development Environment (IDE)? appeared first on Data Science PR. Via https://datasciencepr.com/what-is-integrated-development-environment-ide/?utm_source=rss&utm_medium=rss&utm_campaign=what-is-integrated-development-environment-ide Books provide you the ability to learn at your on time even if you are on the go and they go really in detail. We bring you a list of the best Python books for advanced programmers.Introduction to Machine Learning with Python: A Guide for Data ScientistsMany commercial applications and projects have employed machine learning as an integral ingredient, and the number of applications doing so has only risen over the years. This book by Sarah Guido and Andreas C. Muller teaches you how to use Python programming language to build your machine learning solutions. As the amount of data usage increases with the second, the limitation to machine learning applications is only our imagination. Throughout this book, you learn about the steps required to create a rich machine-learning application using Python and sci-kit-learn library. The book introduces you to the fundamental concepts and uses of machine learning before moving on to the pros and cons of popular machine learning algorithms. You also learn about the advanced methods for model evaluation and the concept of pipelines, which is for encapsulating your workflow and chaining models. In conclusion, the book provides suggestions to help you improve your data science skills. Fluent Python: Clear, Concise, and Effective Programming‘Fluent Python’ by Luciano Ramalho is your hands-on-guide that helps you learn how to write useful Python code by using the most neglected yet best features of the language. The author takes you through the features and libraries of the language and helps you make the code shorter, faster, and readable. The book covers various concepts, including python data model, data structures, functions as objects, object-oriented idioms, control flow, and metaprogramming. Using this book, advanced Python programmers learn about Python 3 and how to become proficient in this version of the language. The author is Luciano Ramalho, a Web Developer who has worked with some of the most significant news portals in Brazil using Python and has his own Python training company. Python Cookbook: Recipes for Mastering Python 3‘Python Cookbook’ by David Beazley and Brian K. Jones helps you master your programming skills in Python 3 or help you update older Python 2 code. This cookbook is filled with recipes tried and tested with Python 3.3 is the ticket for experienced Python programmers who wish to take the approach to modern tools and idioms rather than just standard coding. The book has complete recipes for a variety of topics, covering Python language and its uses, along with tasks common to a large number of application domains. Some of the topics covered in the book are but not limited to strings, data structures, iterators, functions, classes, modules, packages, concurrency, testing, debugging, and exceptions. Throughout the book, the recipes mentioned above presuppose that you have the necessary knowledge to understand the topics in the book. Each recipe contains a sample code the reader can use in their projects. The code follows a discussion about the working of the code and why the solution works. Programming Python: Powerful Object-Oriented Programming‘Programming Python’ by Mark Lutz is ideal for programmers who have understood the fundamentals of Python programming and ready to learn how to use their skills to get real work done. This book includes in-depth tutorials on various application domains of Python, such as GUIs, the Web, and system administration. The book also discusses how the databases uses the language, text processing, front-end scripting layers, networking, and much more. The book explains the commonly used tools, language syntax, and programming techniques through a brief yet precise approach. The book has many examples that show the correct usage and common idioms. The book also digs into the language as a software development tool, along with multiple examples illustrated particularly for that purpose. The post The Best Python Books for Advanced Programmers in 2020 appeared first on Data Science PR. Via https://datasciencepr.com/the-best-python-books-for-advanced-programmers-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=the-best-python-books-for-advanced-programmers-in-2020 Machine Learning (ML) and Artificial Intelligence (AI) are spreading across various industries, and most enterprises have started actively investing in these technologies. With the expansion of volume as well as the complexity of data, ML and AI are widely recommended for its analysis and processing. AI offers more accurate insights, and predictions to enhance business efficiency, increase productivity and lower production cost.AI and ML projects differ from conventional software projects. It varies based on the technology stack, the skills for ML-based projects and the demand for in-depth research. For building ML and AI outline, you have to choose a programming language, which should be flexible, stable and include predefined libraries & frameworks. Python is one of such languages wherein you can see many Python machine learning and Artificial Intelligence projects developing today. Here we have listed the top 8 best Python libraries that could be used for machine learning. Why Python is preferred for Machine Learning and AI?Python supports developers during the entire software development These features add value to the overall popularity of the programming language. The extensive collections of Python libraries for machine learning simplify the development overhead and reduce the development time. Its simple syntax as well as readability supports rapid testing of complex process and makes the language simple to understand for non-programmers. PHP is considered a competitor of Python in terms of web Best Python Libraries for Machine Learning and AIImplementing ML and AI algorithms require a well-structured & well-tested environment to empower developers to come up with the best quality coding solutions. To reduce development time, there are countless Python libraries for machine learning. Python library or framework is a pre-written program that is ready to use on common coding tasks. Let us become familiar with the best Python machine learning libraries: 1. Tensor Flow PythonTensorFlow is an end-to-end python machine learning library for performing high-end numerical computations. TensorFlow can handle deep neural networks for image recognition, handwritten digit classification, recurrent neural networks, NLP (Natural Language 2. Keras PythonKeras is a leading open-source Python library written for constructing neural networks and machine learning projects. It can run on Deeplearning4j, MXNet, Microsoft Cognitive Toolkit (CNTK), Theano or TensorFlow. It offers almost all standalone modules including optimizers, neural layers, activation functions, initialization schemes, cost functions, and regularization schemes. It makes it easy to add new modules just like adding new functions and classes. As the model is already defined in the code, you don’t need to have a separate model config files.Keras makes it simple for machine learning beginners to design and develop a neural network. Keras Python also deals with convolution neural networks. It includes algorithms for normalization, optimizer, and activation layers. Instead of being an end-to-end Python machine learning library, Keras functions as a user-friendly, extensible interface that enhances modularity & total expressiveness. 3. Theano PythonSince its arrival in 2007, Theano has captured the Python developers and researchers of ML and AI.At the core, it is a well-known scientific computing library that allows you to define, optimize as well as evaluate mathematical expressions, which deals with multidimensional arrays. The fundamental of several ML and AI applications is the repetitive computation of a tricky mathematical expression. Theano allows you to make data-intensive calculation up to a hundred times faster than when executing on your CPU alone. Additionally, it is well optimized for GPUs, which offers effective symbolic differentiation and includes extensive code-testing capabilities.When it comes toperformance, Theano is a great Python machine learning library as it includes the ability to deal with computations in large neural networks. It aims to boost development time and execution time of ML apps, particularity in deep learning algorithms. Only one drawback of Theano in front of TensorFlow is that its syntax is quite hard for the beginners. 4. Scikit-learn PythonScikit-learn is another prominent open-source Python machine learning library with a broad range of clustering, regression and classification algorithms. DBSCAN, gradient boosting, random forests, vector machines, and k-means are a few examples. It can interoperate with numeric and scientific libraries of Python like NumPy and SciPy.It is a commercially usable artificial intelligence library. This Python library supports both supervised as well as unsupervised ML. Here is a list of the premier benefits of Scikit-learn Python that makes it one among the most preferable Python libraries for machine learning:
5. PyTorch PythonHave you ever thought why PyTorch has become one among the popular Python libraries for machine learning in such a short time?PyTorch is a production-ready Python machine-learning library with excellent examples, applications and use cases supported by a strong community. This library absorbs strong GPU acceleration and enables you to apply it from applications like NLP. As it supports GPU and CPU computations, it provides you with performance optimization and scalable distributed training in research as well as production. Deep neural networks and Tensor computation with GPU acceleration are the two high-end features of the PyTorch. It includes a machine learning compiler called Glow that boosts the performance of deep learning frameworks. 6. NumPy PythonNumPy or Numerical Python is linear algebra developed in Python. Why do a large number of developers and experts prefer it to the other Python libraries for machine learning?Almost all Python machine-learning packages like Mat-plotlib, SciPy, Scikit-learn, etc rely on this library to a reasonable extent. It comes with functions for dealing with complex mathematical operations like linear algebra, Fourier transformation, random number and features that work with matrices and n-arrays in Python. NumPy Python package also performs scientific computations. It is widely used in handling sound waves, images, and other binary functions. 7. Python PandasIn machine learning projects, a substantial amount of time is spent on preparing the data as well as analyzing basic trends & patterns. This is where the Python Pandas receives machine learning experts’ attention. Python Pandas is an open-source library that offers a wide range of tools for data manipulation & analysis. With this library, you can read data from a broad range of sources like CSV, SQL databases, JSON files, and Excel.It enables you to manage complex data operation with just one or two commands. Python Pandas comes with several inbuilt methods for combining data, and grouping & filtering time-series functionality. Overall, Pandas is not just limited to handle data-related tasks; it serves as the best starting point to create more focused and powerful data tools. 8. Seaborn PythonFinally, the last library in the list of Python libraries for machine learning and AI is Seaborn – an unparalleled visualization library, based on Matplotlib’s foundations. Both storytelling and data visualization are important for machine learning projects, as they often require exploratory analysis of datasets to decide on the type of machine learning algorithm to apply. Seaborn offers high-level dataset based interface to make amazing statistical graphics.With this Python machine learning library, it is simple to create certain types of plots like time series, heat maps, and violin plots. The functionalities of Seaborn go beyond Python Pandas and matplotlib with the features to perform statistical estimation at the time of combining data across observations, plotting and visualizing the suitability of statistical models to strengthen dataset patterns.Here are the details of Github activities for each of the Python libraries for machine learning discussed above: These libraries are extremely valuable when you’re working on machine learning projects as it saves time and further provides explicit functions that one can build on. Among the outstanding collection of Python libraries for machine learning, these are the best libraries, which are worth considering them. With the help of these Python machine learning libraries, you can introduce high-level analytical functions, even with minimal knowledge of the underlying algorithms you are working with. Source: Hackernoon The post List of the Best Python Libraries for Machine Learning in 2020 appeared first on Data Science PR. Via https://datasciencepr.com/list-of-the-best-python-libraries-for-machine-learning-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=list-of-the-best-python-libraries-for-machine-learning-in-2020 Machine learning and artificial intelligence stand to push algorithmic trading to new levels. Not only can more advanced strategies be employed and adapted in real time but new techniques like Natural Language Processing of news articles can offer even more avenues for getting special insight into market movements.Algorithms can already make complex decisions and make them according to predetermined strategies and data, but with machine learning, these strategies can update themselves based on what is actually working. Instead of just “if/then” logic, an ML algorithm can assess multiple strategies and refine the next trades based upon the highest returns. While they still take work to set up, this means traders can have faith in their bot even as market conditions evolve beyond initial parameters. One popular type of ML strategy is called naive Bayes. In this technique, learning algorithms make trades based on previous statistics and probability. For example, historical market data shows that Bitcoin goes up 70% after having three consecutive days in the red. A naive Bayes algorithm would see that the last three days have all been down and automatically place an order based on the likelihood it will rise today. These systems are highly customizable, and it will be up to every trader to set their own parameters for things like risk and reward ratios, but once you are happy with a balance, you can let it run with minimal interference. Another benefit of ML is the ability for machines to be able to read and interpret news reports. By scanning for keywords and having the appropriate strategies lined up, these types of bots can make trades within seconds when positive or negative news breaks. Obviously, these will only be as accurate as the logic that goes into them — and are thus tricky to implement — but still offer an edge over other traders when properly set up. Note that this is the cutting edge of a new branch in automated trading. So, bots designed to work this way may be harder to find, cost more to access or simply be less predictable than some of the more time-tested techniques. The post What are machine learning strategies? appeared first on Data Science PR. Via https://datasciencepr.com/what-are-machine-learning-strategies/?utm_source=rss&utm_medium=rss&utm_campaign=what-are-machine-learning-strategies Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.“Success in creating effective A.I.,” said the late Stephen Hawking, “could be the biggest event in the history of our civilization. Or the worst. We just don’t know.” Are we creating the instruments of our own destruction or exciting tools for our future survival? Once we teach a machine to learn on its own—as the programmers behind AlphaGo have done, to wondrous results—where do we draw moral and computational lines? In this program, leading specialists in A.I., neuroscience, and philosophy tackle the very questions that may define the future of humanity. PARTICIPANTS: Yann LeCun, Susan Schneider, Max Tegmark, Peter Ulric Tse MODERATOR: Tim Urban TOPICS: – Opening film on the history and future of artificial intelligence. 00:06 – Participant intros. 06:05 – What is machine learning? 07:34 – What are neural networks and how do they learn? 09:30 – Teaching computers to create internal models of the world? 12:00 – What do the next 10 years in AI look like? 13:50 – Artificial narrow intelligence and mental models. 14:35 – How is AI changing the world of art and creativity? 16:01 – Can computers be creative? 19:35 – AI writes a screenplay for a movie, how did it turn out? 23:20 – What is artificial general intelligence? 25:30 – How far away are we from developing artificial general intelligence equivalent to human intelligence? 27:00 – Will advanced AI turn into Terminators and take over the world? 28:30 – What’s so special about human intelligence? 31:10 – What is human consciousness and will machines ever experience consciousness? 31:11 – Separating intelligence from consciousness. 41:34 – Defining morality in AI agents. 44:34 – Will machines ever have emotions? 46:45 – Should we be looking at other forms of non-human intelligence to model in our machines? 50:05 – How do you align the drives of AI with human values? 52:25 – Will artificial general superintelligence be good or bad for humankind? 53:10 – Creating a new ethics of AI. 56:15 – When will we ever have super-AGI? 58:40 PROGRAM CREDITS: – Produced by Christy Wegener – Associate Produced by Ann Tyler Moses – Opening film written / produced by Christy Wegener, edited by Gil Seltzer – Music provided by APM – Additional images and footage provided by: Getty Images, Shutterstock, Videoblocks The post Will Self-Taught, A.I. Powered Robots Be the End of Us? appeared first on Data Science PR. Via https://datasciencepr.com/will-self-taught-a-i-powered-robots-be-the-end-of-us/?utm_source=rss&utm_medium=rss&utm_campaign=will-self-taught-a-i-powered-robots-be-the-end-of-us |
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