#nlp libraries for java script | Explore Tumblr posts and blogs

edujournalblogs · 2 years ago

Text

Full Stack Python Developer

Python has become one of the most popular programming language ever since the growth of Data Science, Machine Learning and Artificial Intelligence etc., over the years. In the web application development, python has a large number of modules, libraries, framework like Django, Flask etc to make web development easier. You need to follow a roadmap to become a Full Stack Python Developer.

Some major roles of Full Stack Developers are as follows:

Designing User Interface (UI)

Developing Backend Business Logic

Handling Server and Database connectivity and operations

Developing API to interact with external applications

Integrating third party widgets into your application

Unit Testing and Debugging

Testing and Hosting on Server

Collaborating with cross-functional teams to deliver innovative solutions that meet customer requirement and expectations.

To become a Full Stack Python Web Developer, you need to be proficient in HTML, CSS and Java Script, have a good knowledge of database management, good understanding of the libraries, framework like Django, Flask, CherryPy etc which will help in streamlining the process of writing web applications. Frameworks are collections of modules or packages that provide a default application structure facilitating quick and easy development of web applications. These frameworks ease the development of both the front-end (HTML, CSS, JavaScript, PyScript, Brython etc) , back-end (Django, Flask, CherryPy, Pyramid ) and Database Management tasks to design, manipulate, and decipher queries (MySql, MongoDB, SQLite), GIT etc for web application development.

if you wish to make a career in Data Science, you need to have a good understanding of mathematics and statistics, good understanding of the database concepts, understanding of various libraries and tools associated with Python, various Algorithms used for Machine Learning, Artificial Intelligence, NLP, Manipulating data, Data Analytics and Visualization etc. if you are performing tasks like Automation, Data Analytics, machine learning, robotics, chatbot with AI etc python does make your life easier.

Conclusion: Python is a versatile and in-demand programming language, and it is easier for anyone to pivot from one field to another easily. If you have a passion and dedication to learn new technologies and develop your skills and knowledge, you can become a full stack developer by joining our Edujournal Bootcamp, which will enable you to learn job-relevant skills. With opportunities in Data Science and Web Application Development growing by the day for Python Developers, do not miss this wonderful opportunity to make a great career out of it. You will get an opportunity to work with our experienced professionals along with their overwhelming support. Happy coding !

URL : www.edujournal.com

#user_interface #front_end #back_end #business_logic #api #web_development #Python #technology #skill #knowledge #Edujournal_Bootcamp #full_stack_developer #Django

0 notes

globaljobalert-blog · 2 years ago

Text

Software Engineer, Research - Remote

Company: AssemblyAI AssemblyAI is a remote-first AI company building powerful deep learning models for developers, startups, and enterprises to transcribe and understand their audio data. Our Automated Speech Recognition (ASR) models already outperform companies like Google, AWS, and Microsoft - which is why hundreds of companies and thousands of developers are using our APIs to transcribe and understand millions of videos, podcasts, phone calls, and zoom meetings every day. Our APIs power innovative products like conversational intelligence platforms, zoom meeting summarizers, content moderation, and automatic closed captioning. AssemblyAI’s Speech-to-Text APIs are already trusted by Fortune 500s, startups, and thousands of developers around the world, with well-known customers including Spotify, Algolia, Dow Jones, Happy Scribe, BBC, The Wall Street Journal, and NBCUniversal. As part of a huge and emerging market, AssemblyAI is well on its way to becoming the leader in speech recognition and NLP. We're growing at breakneck speed, and recently announced our Series B round. We've raised $63M in total funding, and are backed by leading investors including Insight Partners, Accel, Y Combinator, Patrick and John Collision (Founders of Stripe), Nat Friedman (Former CEO of GitHub), and Daniel Gross (Entrepreneur & Investor in companies including GitHub, Uber & SpaceX)! Our ambition is to build an iconic AI company, making advanced deep learning technology accessible to everyday developers through a simple API, good docs, and a great developer experience. Join our world-class, remote team and help us build an iconic deep learning company! The Role AssemblyAI is growing quickly, and we’re searching for a mid-level software engineer to help create and own our Deep Learning research framework. You'll need strong software and cloud engineering skills and experience building maintainable systems. Collaboration skills will be important, as you will collaborate closely with the adjacent Research team and help direct a small team to complete larger projects. Some of your responsibilities will include: - Help to design our new experiment framework and integrate it with an open source management platform - Enable researchers to launch many experiments in the cloud across 100s of accelerators by running a single shell script - Design, implement, and maintain the experiment framework, databases, and documentation that all our researcher depend on everyday to perform research - Ensure that model code is hermetically packaged so that it can be easily deployed to production - Ensure that the platform is well tested and resilient to failures, capacity issues, etc. You'll love this job if you.... - Enjoy solving complex technical problems, even when there is no perfect solution - Enjoy building platforms, that evolve over time and scale other teams - Enjoy having ownership of a mission critical software - Enjoy working on a system that enables large scale deep learning research - Thrive in small, cross-functional teams. We like to wear many hats here! Requirements - 3+ years of engineering backend applications using Python and/or other backend language(s) such as Java, C#, JavaScript, Go, C/C++ - 2+ years of working with SQL and NoSQL databases - 2+ years working with common AWS or GCP services, or a similar platform - 2+ years of being a maintainer of a heavily used library or framework Nice to have - 2+ years of working with accelerator backed compute (GPU or TPU) - Experience with bazel as a build system At AssemblyAI, our goal is to attract and retain outstanding talent from diverse backgrounds, while ensuring fair pay among our team members. Our salary ranges are determined by competitive market rates that align with our company's size, stage, and industry. It's important to note that salary is just one aspect of the comprehensive compensation package we offer. When determining salaries, we consider various factors such as relevant experience, skill level, and qualifications evaluated during the interview process. We also strive to maintain internal equity by comparing salaries with those of peers on the team. While the salary range provided below serves as a general expectation for the posted position, we are open to considering candidates who possess more or less experience than specified in the job description. Should any updates arise regarding the expected salary range, we will communicate them accordingly. Please note that the provided range represents the anticipated base salary for candidates in the United States. For candidates outside of this region, there may be variations in the range, which we will communicate directly to applicants. Salary range: $140,000-$170,000 USD Our Team Our team is made up of problem solvers, innovators and top AI researchers with over 20+ years of experience in Machine Learning, NLP, and Speech Recognition from companies like DeepMind, Google Brain, Meta, Apple and Amazon. They conduct cutting edge deep learning research and develop novel algorithms & techniques to continually push the state of the art in speech recognition & NLP! Our team is fully remote, and our culture is super collaborative, low-ego, transparent, and fast-paced. We want to win - and have a flat organization where everyone can openly share ideas (regardless of their title or position) in order to get the best idea. As a remote company, our team members are given a lot of trust and autonomy to work where and how they want. We look for people to join our team who are ambitious, curious, and self-motivated, and we put a lot of trust and autonomy into everyone on our team. We want to empower everyone to do their best work with whatever tools, structures, or resources they need to perform at their highest potential. Benefits (US) - Competitive Salary + Bonus - Equity - 401k - 100% Remote team - Unlimited PTO - Premium Healthcare (100% Covered for you + dependents) - Vision & Dental Care - $1K budget for your home office setup - New Macbook Pro (or PC if you prefer) - 2x/year company paid team retreat APPLY ON THE COMPANY WEBSITE To get free remote job alerts, please join our telegram channel “Global Job Alerts” or follow us on Twitter for latest job updates. Disclaimer: - This job opening is available on the respective company website as of 3rdJuly 2023. The job openings may get expired by the time you check the post. - Candidates are requested to study and verify all the job details before applying and contact the respective company representative in case they have any queries. - The owner of this site has provided all the available information regarding the location of the job i.e. work from anywhere, work from home, fully remote, remote, etc. However, if you would like to have any clarification regarding the location of the job or have any further queries or doubts; please contact the respective company representative. Viewers are advised to do full requisite enquiries regarding job location before applying for each job. - Authentic companies never ask for payments for any job-related processes. Please carry out financial transactions (if any) at your own risk. - All the information and logos are taken from the respective company website. Read the full article

0 notes

boardinfinityblogs · 3 years ago

Text

How To Become A Machine Learning Engineer

A Machine Learning Engineer works closely with artificial intelligence. A Machine learning engineer is responsible for developing programmes and algorithms that empowers machines to take actions in a particular direction. Machine Learning Engineers are often mistaken to be a Data Scientist since they both are relatively new, there is little awareness about the difference.

If we go by the terminologies used, we'd see that there is the term Scientist used for Data Scientists. A scientist is someone who explores the science behind the working mechanism of something while a machine learning engineer purely focuses on model building. A data scientist's work begins once the model has been entirely built. However, this model building part only concerns 5-10% of the job a machine learning engineer does.

A Machine learning engineer makes sure that all production tasks are working properly in terms of execution and scheduling. Apart from this, they keep on adding new functionalities to the machine learning library. They keep a close eye on data science code - if the codes are maintainable, scalable and debuggable. They automate and abstract repeatable routines present in machine learning tasks and finally bring the best software development practices to the table and speed up the entire process of data processing.

A Machine learning engineer hence plays the role of an architect that lays road for the Data Science Team.

What should an ML engineer's background look like?

Machine Learning Engineers mostly have a master's degree or a PhD in Computer Science, Artificial Intelligence, Data Science or Software Engineering. Interestingly, this profession also has many newly graduates, with 1-3 years of experience. ML Engineer's also have another group of experienced developers who have transitioned into the role from fields like Software Engineering or Data Engineering, and of course Data Science.

However, a beginner-level Machine Learning Engineer is not an inexperienced one. This profession requires some set of specialized skills and experience to set foot in the field of Machine Learning. The list of these skills are non-exhaustive, to already sum it up this field requires you to be proficient with every programming language, tool and concept out there! Some of the basic skills to be an ML Engineer have been listed down below -

An ML Engineer has to have a good knowledge of Python, Statistics, Model Optimization, Model Validation, ML Frameworks( TensorFlow and PyTorch), ML applications (NLP, computer vision and time series analysis), Mathematics (algebra and calculus. In Machine Learning, Python is preferred over R because of its production aspect. Also codes are productionalized faster in Python.

This profession is a mix of software engineering and data science. From Software Engineering, it requires experience in a second programming language also other than python, such as Java, C or Java Script. An ML Engineer should also know Functional programming concepts, design patterns, software architecture, data structures and algorithms. A good knowledge of databases and query languages is a must. API Development, Testing, Version Control, Project Management also makes up a huge part of Machine Learning.

If you come from a similar field of software or data science or IT and looking for a career transition into Machine Learning, there are various courses available online on Machine Learning to give you a hand-on experience with the same. Before you start on a Machine Learning project, it is always wise to learn these skills and apply your knowledge with some prior training.

Learning is easy since there is a whole universe out there on the internet to make you skilled, in fact highly skilled in the Machine Learning field. However, in order to take up a job at a good company, certifications do help a lot! Certifications act as a proof of your skills that can match a certain good standard. However, skills back them up! If you have no skills, your certification will hold absolutely no value.

If you want to become a good ML Engineer, focus on the desired skills, start building models on training and put your knowledge to use for some experience and then join a good organization on the basis of your knowledge, skills and finally certification!

There isn't much needed to become a Machine Learning Engineer. But there is definitely a lot of effort and hard work that goes behind becoming a GOOD Machine Learning Engineer! Choose your pick!

Our Online Artificial Intelligence & Machine Learning Course contains the perfect mix of theory, case studies, and extensive hands-on assignments to even turn a beginner into a pro by the end. Our ML and artificial intelligence certification courses are perfect for students and working professionals to get mentored directly from industry experts, build your practical knowledge, receive complete career coaching, be a certified AI and ML Engineer.

#artificial intelligence #machine learning

1 note · View note

nishiagrawal · 4 years ago

Text

Which Type of Apps can you Build in Python?

For many years, technology has been going through complete makeovers and this has been changing our lives. As a result, smartphones, computer systems, Artificial Intelligence and other technologies have made our lives easier. To utilize these technologies, we have access to different programs and applications that are developed using different programming languages. Python is one such powerful programming language.

Python is a pretty popular programming language among developers. It is considered as one of the top programming languages that can beat the original coding language JAVA. This language has facilitated the whole mobile application development process to a great extent and has become popular in the developer community.

Let’s take the journey of knowledge and learn why Python app development is popular and what type of applications can be developed using this programming language. But first, let us first learn about Python.

What Exactly Is Python?

So what is Python you ask? Python is an object-oriented high-level programming language with an incredible built-in data structure. These data structures are combined with dynamic typing and binding features to render hassle-free app development. Python acts as a glue language that fuses several components.

It is a well-known programming language and is quite popular among developers for its easy-to-learn syntax that reduces the expenses incurred in the program maintenance. It also offers several modules and packages that promote code reusability. On top of that, it completely favors cross-platform app development, making it ideal for any mobile app development.

Saying that Python is quite popular would not suffice. Let us have a look at the features that play a vital role in skyrocketing its popularity.

Why Is Python So Popular?

IDLE stands for Integrated Development and Learning Environment. This environment allows developers to create Python code. It is used to create a single statement that can be used to execute Python scripts.

Django

Django is the open-source Python framework that streamlines web app development activities by providing access to different features. This feature helps the developers to create complex codes effectively.

Understandable & Readable Code

One of the most important features of Python is syntax. The rules of the syntax enable the developers to express the concepts without writing any additional code. It makes complex things look simple and this is why Python is deemed suitable for beginners to learn and understand.

Apart from that, Python is the only language that focuses on code readability which allows the developers to use English words instead of punctuations. All these factors make Python the perfect programming suite for mobile app development. The codebase constantly helps the developers update the software without any effort.

It Is Faster

In the Python environment, programs are added to the interpreter that runs them directly. It is extremely easy to code and get your hands on the feedback. With Python, you can easily finish and execute your programs quickly. This improves the time-to-market as well.

High Compatibility

Python supports almost every operating system including Windows, iOS, and Android. You can use Python to use and run the code across different platforms. With Python, you can also run the same code without the need for recompilation.

Test-Driven Development

With Python app development, you can easily create prototypes of software applications. In a Python environment, coding and testing can go hand in hand, thanks to the ‘Test-Driven Development’.

Powerful Library

Python has a robust suite of libraries that gives it an edge over the other languages. The standard library allows you to select the modules from a wide range of requirements. With each module, you can easily add multiple functionalities to the process. All this without any extra coding.

Supports Big Data

Big data is one emerging technology Python supports. Python houses several libraries to work on Big Data. Additionally, it is easier to code with Python for Big data projects as compared to other programming languages.

Supportive Community

Another important element that increases the popularity of Python is its community support. Python has a very active community of developers and SMEs that provides guides, videos, and learning material for a better understanding of the language.

What Type of Apps Can You Build Using Python?

Blockchain Applications

Blockchain is one of the paramount features of technological advancements. It has revolutionized the functionalities of apps and how they perform. Fusing the essentials of blockchain app development was a challenging task in the past. However, since the inception of Python, things have become simpler in this regard.

The methodology of Blockchain app development is tricky but the simplicity of Python makes it less complex. Python houses a suite of frameworks to help the developers with HTTP requests. Developers can now easily employ these complex requests to establish an online connection with Blockchain and create endpoints. Apart from that, and different frameworks of Python also play an important role in creating a decentralized network of scripts on multiple machines.

Command-Line Applications

Command-line apps are control programs that operate from a command line. Both console and command-line apps work without a GUI. The correct evaluation of language helps the developers to discover new ways to use the command-line language.

Python offers the ‘Read Eval-Print-Loop’ feature popularly known as Python’s REPL feature. This particular feature helps the developers with the proper evaluation of command-line languages so they can create new possibilities in app development. Furthermore, Python is fully loaded with libraries that help in performing other tasks effectively.

Audio & Video Applications

Python app development helps in creating music and other types of audio and video apps. Python libraries like PyDub and OpenCV help the developers to successfully create an audio-video application. The video search engine, Youtube, was created using Python. This language is quite effective when it comes to delivering powerful applications with high performance.

Game Development

HD games such as Battlefield 2, EVE Online and a few other popular titles have been created using Python. Python offers the developers the facilities and environment to rapidly create a game prototype that can be tested in real-time. On top of that, this powerful programming language is also used to create game designing tools that assist in several tasks of the game development process.

System Administration Applications

Python is deemed fit for creating complex system administration applications as it allows the developers to communicate with the operating system through the OS. It also enables the developers to connect with the operating system through an interface the language is running on. This powerful language makes all the operating system operations accessible.

Machine Learning Applications

After Blockchain, and Big Data, the inspiring trend of this decade is Machine Learning and the developers can create ML apps using Python libraries. The libraries like Pandas and Scikit for machine learning are available in the marketplace for free. The commercial version can be obtained under a GNU license.

Natural Language Programming is a branch of machine learning that enables a system to analyze, manipulate and understand the human language. Developers can create NLP apps using Python libraries.

Business Applications

Python is quite versatile when it comes to app development. It also assists with ERP, eCommerce and other enterprise-grade app solutions. Odoo is a management software that is written in Python and offers a huge range of business apps. Enterprises have adopted Python for their business solutions.

Python offers a variety of development environments to create business apps. PyCharm is one of the best IDEs for Python that quickly installs on Windows, Mac OSX and other platforms. Developers can easily use this IDE to create avant-garde business apps.

Final Words

Down the road, we can say that Python app development is really fast and super flexible. Developers love this programming language because of the premium suite of features it offers. It is possible to create various types of apps, thanks to the versatile nature of Python app development. If you have a business idea and are looking for Python experts, we at DRC Systems would love to lend a helping hand. Hire dedicated Python developers and get in touch with us for all your Python needs!

0 notes

jobsaggregation2 · 5 years ago

Text

Senior Data Engineer

Position: Senior Data Engineer with R Location:Sydney At least 12 years? experience working as a data engineer with R, with an emphasis on designing, building, and running Skills in SQL, Python, ETL (Extract, transform, load), Apache Spark and Data Analysis 5 years? experience programming with R, Python Superior coding skills using common data science tools, including Python (strongly preferred), R, Linux/Unix command line and shell scripting. Experience with Notebooks such as Jupyter or Apache Zeppelin preferred. Experience with R Studio is required. Experience with NLP pretrained libraries Experience in leading large Big Data Development Program on Cloudera Platform and having Hands on Experience in different tech stacks in big data including Spark (on Scala and Java), Hive, and Impala etc. Should have strong experience in Relational and No-SQL databases Strong in PHP and SQL. Knowledge of JavaScript would be useful Experience with Airflow is an advantage Experience with AWS Technologies Reference : Senior Data Engineer jobs from Latest listings added - JobsAggregation http://jobsaggregation.com/jobs/technology/senior-data-engineer_i10425

0 notes

nox-lathiaen · 5 years ago

Text

Senior Data Engineer

Position: Senior Data Engineer with R Location:Sydney At least 12 years? experience working as a data engineer with R, with an emphasis on designing, building, and running Skills in SQL, Python, ETL (Extract, transform, load), Apache Spark and Data Analysis 5 years? experience programming with R, Python Superior coding skills using common data science tools, including Python (strongly preferred), R, Linux/Unix command line and shell scripting. Experience with Notebooks such as Jupyter or Apache Zeppelin preferred. Experience with R Studio is required. Experience with NLP pretrained libraries Experience in leading large Big Data Development Program on Cloudera Platform and having Hands on Experience in different tech stacks in big data including Spark (on Scala and Java), Hive, and Impala etc. Should have strong experience in Relational and No-SQL databases Strong in PHP and SQL. Knowledge of JavaScript would be useful Experience with Airflow is an advantage Experience with AWS Technologies Reference : Senior Data Engineer jobs Source: http://jobrealtime.com/jobs/technology/senior-data-engineer_i11139

0 notes

analyticsindiam · 6 years ago

Text

11 Alternatives To Keras For Deep Learning Enthusiasts

Keras, one of the most popular frameworks in deep learning, is a high-level neural network library which runs on top of TensorFlow, CNTK and Theano. Written in Python, this framework allows for easy and fast prototyping as well as running seamlessly on CPU as well as GPU. In this article, we are listing down the top 11 alternatives to Keras, the popular deep learning library: (The list is in alphabetical order) 1| CUDA CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by NVIDIA. It includes GPU-accelerated libraries, debugging and optimisation tools, a C/C++ compiler and a runtime library to deploy your application. With the help of the CUDA Toolkit, one can develop, optimise and deploy applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centres, cloud-based platforms, and HPC supercomputers. 2| Deeplearning4j Deeplearning4j is an open-sourced deep learning programming library which is written for Java and is compatible with any JVM language, such as Scala, Clojure or Kotlin. The underlying computations of this library are written in C, C++, and Cuda 3| DeepPy DeepPy is an MIT-licensed deep learning framework for designing models with complex architectures. Techniques like LSTM and Batch Normalisation are implemented inside this framework and it maintains a clean high-level interface. This framework allows for Pythonic programming based on NumPy, runs on CPU or Nvidia GPUs, implements various network architectures like Feedforward, Covnets, Autoencoders, among others. 4| Infer.NET Infer.NET is a machine learning framework for running Bayesian inference in graphical models. It provides state-of-the-art message-passing algorithms and statistical routines needed to perform inference for a wide variety of applications. There are various intuitive features in this framework such as rich modelling language, multiple inference algorithms, designed for large scale inference as well as user-extendable. With the help of this framework, various Bayesian models such as Bayes Point Machine classifiers, TrueSkill matchmaking, hidden Markov models, and Bayesian networks can be implemented with ease. 5| ML Kit ML Kit is a mobile software development kit which provides convenient APIs that help to use custom TensorFlow Lite models in mobile apps. The ready to use APIs for common mobile use cases include recognizing text, detecting faces, identifying landmarks, scanning barcodes, labelling images, and identifying the language of the text. With just a few lines of codes, this framework can enable cloud-based processing, the real-time capabilities of mobile-optimised on-device models and much more. 6| NLTK Natural Language Toolkit (NLTK) is a platform for building Python programs to work with human language data. This toolkit is one of the most powerful NLP libraries which contains packages for machine learning. It provides easy-to-use interfaces along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning. 7| PredictionIO PredictionIO is an open-source machine learning server which is built on top of state-of-the-art open source stack, Spark, MLlib, HDFS, and Elasticsearch. This framework includes a number of useful features such as it can respond to dynamic queries in real-time, choose from a wide variety of templates implementing important machine learning algorithms, deploy multiple engines to support multiple application features, efficiently use Huge Data or small data with flexible scaling and much more. 8| ScikitLearn One of the most popular libraries of machine learning, ScikitLearn is a Python module for machine learning which is built on top of SciPy, NumPy and Matplotlib. The library features a number of classification, regression and clustering algorithms such as Support Vector Machines (SVM), Random Forest, Gradient Boosting, k-means clustering, among others. 9| TensorFlow Originally developed by the researchers at Google Brain, TensorFlow is one of the popular machine learning libraries. Often times, it is being compared with Keras. This is an end-to-end open-source platform for machine learning which has a comprehensive, flexible ecosystem of tools, libraries, and community resources in order to build and deploy machine learning applications. 10| Theano Theano is a popular Python library which allows a developer to define, optimise, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. There are various intuitive features in this library such as tight integration with NymPy, transparent use of a GPU, efficient symbolic differentiation, speed and stability optimisations, extensive unit-testing and self-verification, among others. 11| Torch Torch is an opensource machine learning library which supports a wide range of machine learning algorithms that puts GPUs first. This scientific computing framework includes an easy and fast scripting language known as LuaJIT, and an underlying C/CUDA implementation. Torch supports linear algebra routines, numeric optimization routines, neural network, energy-based models and much more. Read the full article

#alternativestokeras #Keras #predictionIO #Scikitlearn #Tensorflow #tensorflowtheanodifference #Theano

0 notes

linkhello1 · 5 years ago

Text

Senior Data Engineer

Position: Senior Data Engineer with R Location:Sydney At least 12 years? experience working as a data engineer with R, with an emphasis on designing, building, and running Skills in SQL, Python, ETL (Extract, transform, load), Apache Spark and Data Analysis 5 years? experience programming with R, Python Superior coding skills using common data science tools, including Python (strongly preferred), R, Linux/Unix command line and shell scripting. Experience with Notebooks such as Jupyter or Apache Zeppelin preferred. Experience with R Studio is required. Experience with NLP pretrained libraries Experience in leading large Big Data Development Program on Cloudera Platform and having Hands on Experience in different tech stacks in big data including Spark (on Scala and Java), Hive, and Impala etc. Should have strong experience in Relational and No-SQL databases Strong in PHP and SQL. Knowledge of JavaScript would be useful Experience with Airflow is an advantage Experience with AWS Technologies Reference : Senior Data Engineer jobs from Latest listings added - LinkHello http://linkhello.com/jobs/technology/senior-data-engineer_i11243

0 notes

linkhellojobs · 5 years ago

Text

Senior Data Engineer

Position: Senior Data Engineer with R Location:Sydney At least 12 years? experience working as a data engineer with R, with an emphasis on designing, building, and running Skills in SQL, Python, ETL (Extract, transform, load), Apache Spark and Data Analysis 5 years? experience programming with R, Python Superior coding skills using common data science tools, including Python (strongly preferred), R, Linux/Unix command line and shell scripting. Experience with Notebooks such as Jupyter or Apache Zeppelin preferred. Experience with R Studio is required. Experience with NLP pretrained libraries Experience in leading large Big Data Development Program on Cloudera Platform and having Hands on Experience in different tech stacks in big data including Spark (on Scala and Java), Hive, and Impala etc. Should have strong experience in Relational and No-SQL databases Strong in PHP and SQL. Knowledge of JavaScript would be useful Experience with Airflow is an advantage Experience with AWS Technologies Reference : Senior Data Engineer jobs from Latest listings added - LinkHello http://linkhello.com/jobs/technology/senior-data-engineer_i11243

0 notes

cvwing1 · 5 years ago

Text

Senior Data Engineer

Position: Senior Data Engineer with R Location:Sydney At least 12 years? experience working as a data engineer with R, with an emphasis on designing, building, and running Skills in SQL, Python, ETL (Extract, transform, load), Apache Spark and Data Analysis 5 years? experience programming with R, Python Superior coding skills using common data science tools, including Python (strongly preferred), R, Linux/Unix command line and shell scripting. Experience with Notebooks such as Jupyter or Apache Zeppelin preferred. Experience with R Studio is required. Experience with NLP pretrained libraries Experience in leading large Big Data Development Program on Cloudera Platform and having Hands on Experience in different tech stacks in big data including Spark (on Scala and Java), Hive, and Impala etc. Should have strong experience in Relational and No-SQL databases Strong in PHP and SQL. Knowledge of JavaScript would be useful Experience with Airflow is an advantage Experience with AWS Technologies Reference : Senior Data Engineer jobs from Latest listings added - cvwing http://cvwing.com/jobs/technology/senior-data-engineer_i14165

0 notes

mathematicianadda · 6 years ago

Text

Launching Today: Free Wolfram Engine for Developers

Why Aren’t You Using Our Technology?

It happens far too often. I’ll be talking to a software developer, and they’ll be saying how great they think our technology is, and how it helped them so much in school, or in doing R&D. But then I’ll ask them, “So, are you using Wolfram Language and its computational intelligence in your production software system?” Sometimes the answer is yes. But too often, there’s an awkward silence, and then they’ll say, “Well, no. Could I?”

I want to make sure the answer to this can always be: “Yes, it’s easy!” And to help achieve that, we’re releasing today the Free Wolfram Engine for Developers. It’s a full engine for the Wolfram Language, that can be deployed on any system—and called from programs, languages, web servers, or anything.

The Wolfram Engine is the heart of all our products. It’s what implements the Wolfram Language, with all its computational intelligence, algorithms, knowledgebase, and so on. It’s what powers our desktop products (including Mathematica), as well as our cloud platform. It’s what’s inside Wolfram|Alpha—as well as an increasing number of major production systems out in the world. And as of today, we’re making it available for anyone to download, for free, to use in their software development projects.

The Wolfram Language

Many people know the Wolfram Language (often in the form of Mathematica) as a powerful system for interactive computing—and for doing R&D, education, data science and “computational X” for many X. But increasingly it’s also being used “behind the scenes” as a key component in building production software systems. And what the Free Wolfram Engine for Developers now does is to package it so it’s convenient to insert into a whole range of software engineering environments and projects.

It’s worth explaining a bit about how I see the Wolfram Language these days. (By the way, you can run it immediately on the web in the Wolfram Language Sandbox.) The most important thing is to realize that the Wolfram Language as it now exists is really a new kind of thing: a full-scale computational language. Yes, it’s an extremely powerful and productive (symbolic, functional, …) programming language. But it’s much more than that. Because it’s got the unique feature of having a huge amount of computational knowledge built right into it: knowledge about algorithms, knowledge about the real world, knowledge about how to automate things.

We’ve been steadily building up what’s now the Wolfram Language for more than 30 years—and one thing I’m particularly proud of (though it’s hard work; e.g. check out the livestreams!) is how uniform, elegant and stable a design we’ve been able to maintain across the whole language. There are now altogether 5000+ functions in the language, covering everything from visualization to machine learning, numerics, image computation, geometry, higher math and natural language understanding—as well as lots of areas of real-world knowledge (geo, medical, cultural, engineering, scientific, etc.).

In recent years, we’ve also introduced lots of hardcore software engineering capabilities—instant cloud deployment, network programming, web interaction, database connectivity, import/export (200+ formats), process control, unit testing, report generation, cryptography, blockchain, etc. (The symbolic nature of the language makes these particularly clean and powerful.)

The goal of the Wolfram Language is simple, if ambitious: have everything be right there, in the language, and be as automatic as possible. Need to analyze an image? Need geographic data? Audio processing? Solve an optimization problem? Weather information? Generate 3D geometry? Anatomical data? NLP entity identification? Find anomalies in a time series? Send a mail message? Get a digital signature? All these things (and many, many more) are just functions that you can immediately call in any program you write in Wolfram Language. (There are no libraries to hunt down; everything is just integrated into the language.)

Back on the earliest computers, all one had was machine code. But then came simple programming languages. And soon one could also take it for granted that one’s computer would have an operating system. Later also networking, then a user interface, then web connectivity. My goal with the Wolfram Language is to provide a layer of computational intelligence that in effect encapsulates the computational knowledge of our civilization, and lets people take it for granted that their computer will know how to identify objects in an image, or how to solve equations, or what the populations of cities are, or countless other things.

And now, today, what we want to do with the Free Wolfram Engine for Developers is to make this something ubiquitous, and immediately available to any software developer.

The Wolfram Engine

The Free Wolfram Engine for Developers implements the full Wolfram Language as a software component that can immediately be plugged into any standard software engineering stack. It runs on any standard platform (Linux, Mac, Windows, RasPi, …; desktop, server, virtualized, distributed, parallelized, embedded). You can use it directly with a script, or from a command line. You can call it from programming languages (Python, Java, .NET, C/C++, …), or from other systems (Excel, Jupyter, Unity, Rhino, …). You can call it through sockets, ZeroMQ, MQTT or its own native WSTP (Wolfram Symbolic Transfer Protocol). It reads and writes hundreds of formats (CSV, JSON, XML, …), and connects to databases (SQL, RDF/SPARQL, Mongo, …), and can call external programs (executables, libraries, …), browsers, mail servers, APIs, devices, and languages (Python, NodeJS, Java, .NET, R, …). Soon it’ll also plug directly into web servers (J2EE, aiohttp, Django, …). And you can edit and manage your Wolfram Language code with standard IDEs, editors and tools (Eclipse, IntelliJ IDEA, Atom, Vim, Visual Studio Code, Git, …).

The Free Wolfram Engine for Developers has access to the whole Wolfram Knowledgebase, through a free Basic subscription to the Wolfram Cloud. (Unless you want real-time data, everything can be cached, so you can run the Wolfram Engine without network connectivity.) The Basic subscription to the Wolfram Cloud also lets you deploy limited APIs in the cloud.

A key feature of the Wolfram Language is that you can run the exact same code anywhere. You can run it interactively using Wolfram Notebooks—on the desktop, in the cloud, and on mobile. You can run it in a cloud API (or scheduled task, etc.), on the public Wolfram Cloud, or in a Wolfram Enterprise Private Cloud. And now, with the Wolfram Engine, you can also easily run it deep inside any standard software engineering stack.

(Of course, if you want to use our whole hyperarchitecture spanning desktop, server, cloud, parallel, embedded, mobile—and interactive, development and production computing—then a good entry point is Wolfram|One, and, yes, there are trial versions available.)

Going into Production

OK, so how does the licensing for Free Wolfram Engine for Developers work? For the past 30+ years, our company has had a very straightforward model: we license our software to generate revenue that allows us to continue our long-term mission of continuous, energetic R&D. We’ve also made many important things available for free—like our main Wolfram|Alpha website, Wolfram Player and basic access to the Wolfram Cloud.

The Free Wolfram Engine for Developers is intended for use in pre-production software development. You can use it to develop a product for yourself or your company. You can use it to conduct personal projects at home, at school or at work. And you can use it to explore the Wolfram Language for future production projects. (Here’s the actual license, if you’re curious.)

When you have a system ready to go into production, then you get a Production License for the Wolfram Engine. Exactly how that works will depend on what kind of system you’ve built. There are options for local individual or enterprise deployment, for distributing the Wolfram Engine with software or hardware, for deploying in cloud computing platforms—and for deploying in the Wolfram Cloud or Wolfram Enterprise Private Cloud.

If you’re making a free, open-source system, you can apply for a Free Production License. Also, if you’re part of a Wolfram Site License (of the type that, for example, most universities have), then you can freely use Free Wolfram Engine for Developers for anything that license permits.

We haven’t worked out all the corners and details of every possible use of the Wolfram Engine. But we are committed to providing predictable and straightforward licensing for the long term (and we’re working to ensure the availability and vitality of the Wolfram Language in perpetuity, independent of our company). We’ve now had consistent pricing for our products for 30+ years, and we want to stay as far away as possible from the many variants of bait-and-switch which have become all too prevalent in modern software licensing.

So Use It!

I’m very proud of what we’ve created with Wolfram Language, and it’s been wonderful to see all the inventions, discoveries and education that have happened with it over decades. But in recent years there’s been a new frontier: the increasingly widespread use of the Wolfram Language inside large-scale software projects. Sometimes the whole project is built in Wolfram Language. Sometimes Wolfram Language is inserted to add some critical computational intelligence, perhaps even just in a corner of the project.

The goal of the Free Wolfram Engine for Developers is to make it easy for anyone to use the Wolfram Language in any software development project—and to build systems that take advantage of its computational intelligence.

We’ve worked hard to make the Free Wolfram Engine for Developers as easy to use and deploy as possible. But if there’s something that doesn’t work for you or your project, please send me mail! Otherwise, please use what we’ve built—and do something great with it!

from Stephen Wolfram Blog http://bit.ly/2VUYtkA from Blogger http://bit.ly/2EoFzrt

#Blogger

0 notes