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Deep Learning With Python

Way back in the year 1991 when the great Guido van Rossum had released Python as his side assignment, he had not expected that python would become the world’s fastest developing computer language of the near future. If the trends are to be believed, Python turns out to be a go-to language for the fast prototyping.

If an individual dwells deep at the philosophy with which the Python language is created, one can say that the language had been built for the purpose of its readability and its less complex nature. One can easily understand the language as well as make someone else also understand the same very fast.

The language Python is successful at winning the hearts of its users. As per the Hackerrank 2018 developer survey, it is believed that the JavaScript might be the top most in-demand Programming language by the employers, but the Python language has won the heart of the developers across all the age groups, as per the Love-Hate index.

Why deep learning in python?

It is essential to understand that why would someone wish to use only the Python language in designing any kind of deep learning project. Deep learning in layman terms, is the usage of the data in order to help a machine make intelligent decisions.

For instance — one can build a spam detection algorithm in which the rules may be learned from a data or an anomaly of detection of the rare events by observing at the previous data or by arranging the email based on the tags that one had assigned by viewing the email history and so on. The main task of deep learning is to simply recognize the patterns in a given data set.

One of the critical tasks of a deep learning engineer in his/her career life is to extract, refine, define, clear, arrange and understand the data that is given, in order to develop a set of intelligent algorithms. Thus for a deep learning engineer or a Computer Vision Engineer or a budding Data Scientist or a deep learning or an Algorithm Engineer or a Deep learning engineer one would definitely recommend Python, as it’s easy to understand.

Many times the concepts of topics such as Linear Algebra, Calculus are so complex, that they take a significant amount of effort. A simple implementation in the Python language helps the engineer to validate an idea. There are simple python deep learning tutorials available which offer the best possible assistance to language usage.

Data is the primary factor

Thus it entirely depends on the kind of the task where one wants to use deep learning. Let us take a view at a few instances and examples. For a computer vision projects, the input data is the image or the video. For a statistical review, it may be a series of points across time or a collection of language documents that are spread across the various domains or the audio files that are given or simply some numbers.

Try to imagine that everything which exists around is in the form of data. And the data is raw, inadequate, incomplete, unstructured, and large. Python can be a guide for deep learning to tackle all of the problems.

Python has a collection as well as code stack of the various open source repositories that is developed by the people (and still in process) for the purpose of continuously improving upon the existing methods.

That are very helpful for deep learning for beginner’s category of people. The following are some of the guide for deep learning in python:

  • In order to work with images — opencv, scikit and numpy
  • In order to work with text — nltk, numpy, scikit
  • In order to work with audio — librosa
  • In order to resolve the deep learning problem — scikit, pandas
  • In order to view the data clearly —  seaborn, scikit, matplotlib
  • In order to utilize the deep learning — pytorch, tensorflow
  • In order to perform scientific computing — scipy
  • In order to integrate any kind of web applications — Django

Deep learning in Python: the implementation matters

The total implementation of the clustering algorithm will open up insights towards the problem then simply reading the algorithm. In python, when a user implements the things, it is going to perform much faster in order to prototype code and then test it.

Key Takeaways

Thus it can be seen that if the focus is on the overall task that is needed to train, validate as well as test the models — so far as they satisfy the aim of a problem, any tool/language/framework may be used. Be it for the purpose of extracting the raw data from an API, or analyzing it, or performing an in-depth visualization and creating a classifier for a given task. But the primary reason for using deep learning in Python would mainly be its readability, versatility, and ease of understanding. You can cater your requirement to deep learning python experts to build an awesome application.

Written By : Technostacks

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Written By : Technostacks

Why You Should Use React JS

React JS was developed by Jordan Walke, who was an employee at Facebook. It is a Java framework. It is an open source JavaScript library which is used for building user interfaces for single page applications. It mains view layer for different mobile apps as well and also creates reusable UI components. It was applied on Facebook in 2011 and on Instagram in 2012.

We can create specific changes in data of web application without reloading the web pages. The purpose or say features of this is to be simple, fast efficient in creating a user interface for applications.

We can use it with another framework of JavaScript such as angular for this purpose. It has an active community and substantial foundation behind it and is a front-end library developed by Facebook.

To work with it efficiently, you must have vast knowledge about HTML5, CSS, and JavaScript. React JS doesn’t use HTML, but JSX is very similar to it. So being familiar it may help you to learn more. ES6 is a recent version of JavaScript which is used in this which makes it advance and more efficient.

Key Reasons to choose React JS

We have lots of framework platform so it’s a genuine question as to why we should use React JS. But it has some typical features which would make life easy for you. Let us look at some key reasons to choose React JS:

  • Simple and easy to learn: It is straightforward and sophisticated as compared to any other javascript frameworks and is neither difficult to understand and use. You can use plain JavaScript to create a web application and then handle it using this. You can mix HTML with it via some of its syntaxes. JSX is also much easier to use with it.
  • Code reusability and data binding: It supports code reusability and can create an Android web application. It uses one side data binding and also flux which is an application architecture which controls the flow of the data from one point. It is a useful feature regarding web application development and can help us a lot. Data binding and code reusability are essential factors.
  • Performance and testing: We can use to browse, ecmascript6 modules which define dependencies and can use it with reacts-di, babel, etc. They are easy to test can be treated as a function of the present state and can be checked from the output, triggered actions, events, etc. It is imperative to test before using and React JS makes it too easy to do it.

As discussed above the purpose of using React JS is to create user interfaces for web application with much ease and sophistication. It is the best framework when compared with others. It allows the user to perform the task with JSX rather than pure JavaScript, but you can use it too in case. It has native libraries developed by Facebook, and it gives reach architecture to android, UPD and IOS.

The benefits of React JS are as follows:

  • JSX is used which makes it more updated and quite simple to use. It uses HTML tags and syntax to render subcomponents .html tags are converted into react framework, and then the work goes on. Also can be done using simple JavaScript if JSX isn’t available.
  • Single Way Data Flow: It allows unique way data flow in which sets of values are passed as components rendered as properties in HTML tags. It cannot directly access or modify components but passes a call back which does this task. The property is then known as “Properties flow down, and actions flow up.
  • Virtual Document Objects Model: React JS creates components of memory data structures, which computes the changes and then updates the browser. So a unique feature is enabled which allows the user to code and it renders the components, elements, and data which can ultimately be processed and used.
  • Render method takes input and returns what to display. JSX is a XML like syntax. Components can be used to render () via these properties.
  • A state-full component: A component can maintain internal state data in addition to taking input data. When a components state changes, it is re-invoked by calling render (). Although event handlers seem to be rendered inline but will be collected and implemented using event delegation.
  • Comparison between Angular and React-JS: Subscription of HTML while React JS is a complete pure JavaScript based library. It is more advanced, simple, dependable and intensive programming than angular. Hence it is much better than angular when compared concerning the framework.
  • Can be applied using Babel: It is a compiler that converts markup language into JavaScript. You can use the newest features of JavaScript with this and also is available for different conversions. For example, our React JS uses this to convert JSX into JavaScript. JSX is an XML syntax extension to JavaScript which comes with full features of ECMAScript.
  • The JSX expressions can be used by rapping them in curly braces. They are immutable hence cannot be changed, you can just use render () to replace them in case of every time if you want modification anyhow.
  • React components are JavaScript functions. React uses ES6 classes to create components and can be created using the render method.

Key Takeaways

React JS is flexible and provides hooks that allow you to interface with other libraries and framework. It uses markdown libraries to do so. The declarative aspects make it more comfortable to debug as well. Overall react is the best framework for creating the user interface in a web application. When a website is complex to code and can’t define the understanding of a user, then one can go for React JS.

React JS is a better framework platform indeed to create a user interface for iOS, Android type web application. It is user-friendly, convenient, and efficient and why not it should be preferred over any other framework. It is applied in Facebook and Instagram. So if you are thinking of creating or modifying data on the web page then you must learn and use React JS.

If you have any question or planning to develop a react web application then you can hire us. We have experienced team of React JS programmers who are able to full fill your requirements.

Written By : Technostacks

Deep Learning In Healthcare

Deep learning is a part of the machine learning family. It is based on data learning representations and methodologies. In deep learning, there are three major classifications. They are supervised deep learning, unsupervised deep learning and semi-supervised deep learning. Deep learning is mainly used in recognizing speech, natural language processing, computer visions, social media networks, medical analysis, the design of the drug, bioinformatics, machine translations, game programs on board, and inspection of materials.

Data learning gets its inspiration from the processing of information and communication in medical nervous systems. The properties of data learning differ in structures and functionalities that make them incomplete. For example, the human brain. Each human brain is unique and differs in structure and functioning.

Deep learning in health care

To find out how deep learning can be used in healthcare, we must first look into the health care treatments offered by deep learning. So, Deep learning in health care is used to assist professionals in the field of medical sciences, lab technicians and researchers that belong to the health care industry.

Deep learning in health care helps to provide the doctors, the analysis of disease and guide them in treating a particular disease in a better way. So, the medical decisions made by the doctors can be made more wisely and are improving in standards.

Deep learning in various spheres of health care

  • Genomics – Deep learning technique is used to understand a genome and helps the patients undergoing treatment to get an idea about the disease that might affect them in the future. Genomics is a field which is steadily growing, and deep learning runs to have an excellent future with the help of insurance industries. Deep learning techniques are used to make medical field researchers and doctors fast and more accurate.
  • Cell scope – A Cell scope uses deep learning technology and it also helps the parents to keep monitoring the health conditions or the health status of their children. This technology can be seen on any devices and decreases the visits of parents to hospitals or health care centers to see their children or consult the doctor.
  • Insurance fraud – Deep learning is used in the analysis of medical insurance claim fraud. Deep learning uses a technique known as predictive analysis which can predict the fraud claims that are likely to happen in future. Deep learning helps the insurance industry to send offers and discounts to the target patients.
  • Medical imaging – There are a few techniques involved in the medical industry such as CT scan, ECG, MRI etc. that are used to diagnose harmful diseases. The harmful diseases include brain tumor, heart attacks, cancer, and many others. Hence, deep learning can be used to consult doctors who analyze the patient’s disease and provide them with good treatment.
  • Discovery of drug – Data learning helps in medicine discovery and is also used in developing them. A patient’s medical history is analyzed, and treatment is given according to the analysis. By the deep learning in health care, we can gain insight from the patient’s tests and reports and disease-related information regarding the symptoms.
  • Alzheimer’s disease – It is one of the most crucial challenges that medical industry clients are facing currently. Deep learning is used here to detect Alzheimer’s disease at its initial stage itself, making it more convenient for doctors to treat.

Deep learning solutions in health care and other industries

In deep learning, there are various hidden patterns and chances in helping doctors to treat their patients well. Machine learning, artificial intelligence has gained a lot of attention over the past few years. Now even deep learning has created its own path in this field and is steading growing in the market. Industries like health care, travel, and tourism, finance, retail and textiles, manufacture, health etc are relying on deep learning directly or indirectly. The first priority of any individual in today’s modern world is his or her health.

So, the health industry is one such platform that implements some technologies like deep learning for fulfilling their needs. Medical experts try to find various ways of implementing new technologies. These technologies must provide an impactful result for a better future. Deep learning puts together a bulk of data that included records of the patients, medical reports, personal data, insurance reports etc. for providing them with better treatment for a good outcome.

Current and future directions in health care and medical domains

Insights into deep learning in health care current and future applications are evident and can be clearly observed. Some of them are:

    • On gaining knowledge from complex, heterogeneous and high-dimensional bio-medical information, remains a challenge in transforming the health care industry. Different types of data are emerging in the modern world of medical sciences. Issues faced in imaging, sensor text, sensor data, electronic records etc. are solved by using deep learning algorithms. Deep learning makes the un-structured and complex examples into successful representations. The latest technology of deep learning provides new and efficient paradigms in obtaining the end to end learning models for complex data types.
    • Usage of electronic health records (EHR) promises to advance clinical research and better inform the decision-making skills clinically. Modern electronic health records can prevent the practice of predictive modeling by summarizing and representation. Patients who achieved results based on electronic health record data and an alternative feature learning strategy are being successful. From various research’s we can straight away say that deep learning framework arguments decision systems in the clinical environment.
    • Food and drug administration have finally approved the plasminogen activator streptokinase and urokinase and therapy can no longer be an approach for treating the thrombosis disease. Deep learning is used in treating acute peripheral arterial thrombosis and embolism along with acute coronary thrombosis.
    • Deep learning is a family of many computational methods that allow any algorithm that programs itself by using a learning method from a large set of examples that demonstrate the desired behavior. As an application of this method, there is a further assessment and validation to medical imaging.
    • Deep learning trained algorithms have some outcomes and measures. The specificity and the sensitive nature of the algorithm in detecting reference diabetic retinopathy (RDRs) can be defined as moderate and also as the worst ones. So deep learning trained algorithm was evaluated at two operating points which are selected from a development set out of which one handles high specificity and another one handles high sensitivity thereby providing better results.
    • Electronic health recorder or EHR with predictive modeling Dara is anticipated to solve personalized health quality in medicine. So, for constructing predictive statistical method, we require extraction of curated predictor variables from EHE data that is normalized and a process that discards the majority of information in each patient’s record.Deep learning after using this representation can be capable of predicting multiple medical events from various medical centers without any site-specific data harmonization. Data learning approaches can be used to create an accurate and scalable prediction for a variety of sceneries regarding clinical resources. In deep learning, neural networks can be used for identifying relevant information from the patient’s chart and records.
  • Data learning algorithms are convolutional networks that have become a methodology by choice. They are being used to analyze medical images. Deep learning can further be used in medical classification, segmentation, registration, and various other tasks.Deep learning is used in areas of medicine like retinal, digital pathology, pulmonary, neural etc. Deep learning is a steadily developing trend in the field of data analysis and has also been named one of the 10 breakthrough technologies in the year 2013. Deep learning is an advancement of artificial neural networks which consist of more layers at higher levels of abstraction.

    There is an improvement in the predictions from the data developed using deep learning algorithms. Deep learning is emerging as a very important machine learning tool in imaging, convolutional neural networks, computer domains vision etc.

  • Health informatics is emerging as a domain of interest among researchers all over the world. Therese researchers owe the majority of the implications on society. Right from the prediction of a disease to providing personalized services to patients, applications in deep learning range. Biomedical data in the health care industry has gained knowledge about many applications based on techniques followed by deep learning.
  • The health care field of modern era comprises various strategies that are of national importance owing to their spectrums of reach to individuals or society. We have earlier witnessed the advancements in machine learning and artificial intelligence technologies applied in various domains. Among all the domains, health care is in the primary focus of deep learning and machine learning researchers and experts of the industries owing to the veracity of data availability and high volumes.
  • The increase of health care sectors is being characterized by large data sets from clinical management systems. This provides a choice for any application of deep learning approaches on health care data sets, which may be sparse.

Benefits of deep learning

There are various benefits of deep learning in the health care industry. Some of them are:

  • Deep learning learns the important relationships in your data and records the information about past clients which can be used a future reference for the patients with similar symptoms or diseases.
  • Deep learning allows us to create a model-based on whatever source of data available when you require a risk score upon administration other than discharge.
  • Deep learning provides accurate and timely risk scores which enable the confidence and approximate allocation of resources.
  • Deep learning approaches lead to lower costs and provide improved outcomes.
  • When the deep learning algorithms interact with the training data, they become more precise and accurate allowing individuals to gain unprecedented insights into care processes, variability, and diagnostics.
  • Graphics processing units or GPU’s are getting more efficient to energy and are becoming faster.
  • Innovation is exploding as we now use deep learning algorithms at a fraction of the past costs and so algorithms are getting sophisticated.
  • Electronic health records or EHR and other digitization efforts are giving health care data the access to use trained algorithms than ever before.
  • Diagnostics are being more accurate and faster through deep learning which identifies patterns by connecting the tools.
  • Deep learning can determine whether the skin lesions are cancerous just like any other board certificated dermatologist.

Conclusion

Based on all the analysis done to deep learning by the different researchers, it is clear that deep learning can be an element for translating biomedical data into improved human health care. On the other hand, the latest advancements in deep learning technologies provide new useful and effective paradigms to prove the end to end learning models for uncertain and complex data structures.

Written By : Technostacks

Xcode 10

The newly released Xcode build the system with Xcode 9 by Apple is in the preview mode. The advanced features were not active during that time. The activated Xcode 10 features by default have some issues in the iOS projects since Apple is aware of these, they have separately issued new build system. They have also mentioned possible solutions to tackle those issues.

We are here to highlight the top 5 issues iOS developers might face and they might not be covered in the recent release notes, e.g. Xcode 10 system requirements and the system behaviour of new build with third-party tools.

Xcode 10 Features: The Newly Build System

You can now activate new build settings from Xcode Files-> Project/Workspace Settings with this toggling between legacy and new build system becomes easy. Additionally, if you are building an iOS project right from the command line using the Xcode build then it is required to pass additional parameter such as UseModernBuildSystem=YES.
The latest build system is called as the xcbuild. The new build system elevates the overall swift build by running the targets and its build phases side by side. Once it is activated you will face both its benefits and issues in regard to the new build system in your project. Here we will try to identify the issue and get solutions immediately.

1. The Info.Plist

When an iOS project is built using the new build system you will face several issues regarding the info.plist files. Here are the few things you need to keep in mind regarding New Build System and info-plist files.

  • Make sure there is no duplicate plist file in the copy bundle resources in the build phase of any target. Otherwise you will not be able to create an app with the new build system. Additionally, the files copied multiple times will hamper the functioning.
  • The new build system functions on various precedence of running info-plist step within the clean and incremental builds. In the clean build, you will find that the info.plist steps after processing assets, whereas the incremental build runs before signing.
  • If there only info.plist value and does not have the Xcode reference folders then you will face an Xcode build system failure.

2. The CocoaPods

CocoaPods bring some issues for the iOS projects.

  • With CocoaPods development pods will not be updated unless a clean build is performed. The embedded pods are not executed successfully.
  • Some of the Cocoapods build phase script doesn’t run reliably as you may see disbursement in its behaviour and you might not be able to archive the app.

Hence, this makes it clear that cocoapods and the new build system don’t get along well together.

3. Running the Script Phase

The new build system is full of flaws with the Run Script Phase started giving false results. But this too comes with a good reason.
The new Xcode 10 features have a lot of improvement in the Run Script Phase. However, it requires you to help build processes with the help of feeding files for the run script phase. You will have to specify the input files to the run script phase, this is important to determine whether the script needs to be run or not.
If Xcode build system runs parallel commands then the input for the run script phase will not be generated, this fails the build system as it gets confused. However, providing the input files to run scripts is advisable as when the inputs grow in number it gives a way to specify all the input files in the .xcfilelist format. Hence it is recommended to add the files to avoid running this phase for all the other incremental builds whenever it is not required.

4. Clean Build Folder Action

With the new build system, a clean build folder has been introduced. This introduction eliminates all the derived data of the iOS application causing the cleaner builds right from scratch. This step means if you are using cocoapods all the frameworks will be rebuilt from the scratch and you will face a delay in developing an iOS project. There may be Xcode indexing issues as well.
The new build system by Apple is introduced to improve the performance, reliability, and stability of the Swift build. This system is designed to capture the configuration errors early in the application development phase. In Xcode 10 mascos version so you don’t have a choice and you will have to update the build process to adapt to the new build system. This scenario demands a lot of configuration enhancements in the app.

5. XCCONFIG Files

There are a number of iOS developers might be using .xcconfig files to keep the Xcode build settings at one place for the appropriate goals. There are some queries that conditional variable assignment in the xcconfig files might not work as wanted, because of the build failures. To ensure your xcconfig files, Apple suggested running following command.

defaults write com.apple.dt.XCBuild EnableCompatibilityWarningsForXCBuildTransition -bool YES

If this command displays any errors or warnings, we must have to solve it to get stable builds.

Moving Forward

The recent Xcode 10 release date was September 17, 2018. Let us know have you tried the Xcode code dark mode yet or migrated to the new build system? If yes, what are your experiences? If you wish to share your views or get an issue resolved, get in touch with us without any hesitation.

Written By : Technostacks

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