#dnn web development

[softwarefrim]

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The Impact of Python on Data Science and Machine Learning

Data science and machine learning have become increasingly important in a variety of industries, from finance to healthcare to marketing. With the rise of large data sets and the need for sophisticated algorithms to analyze it, companies hire professionals with expertise in these areas. 

Programming languages are crucial in data science and machine learning as they create models, manipulate data, and automate processes.

 Python has emerged as one of the premier data science and machine learning programming languages. It is known for its readability, simplicity, and versatility – making it an appealing choice for both novice and experienced developers.

Python's extensive libraries, such as NumPy, Pandas Matplotlib, etc., enable efficient manipulation & visualization of large datasets, making it a go-to choice among Data Scientists worldwide.

Python: An Overview

Python is a dynamic, versatile, ever-growing programming language that has taken the tech industry by storm. It was created in 1991 with an emphasis on simplicity & ease-of-use making it one of the most beginner-friendly languages.

One of Python's main strengths is its readability which makes it accessible even for non-technical stakeholders while still providing developers with powerful abstractions required for building complex systems. Additionally, Python's emphasis on code readability makes it easy to maintain and modify existing codebases.

 It also boasts a rich library and framework ecosystem, enabling a Python app development agency to build robust applications quickly. These include NumPy & Pandas (for Data Analysis), Django & Flask (for Web Development), and TensorFlow & PyTorch(for Artificial Intelligence/Machine Learning), which simplify the creation of complex systems.

 In addition to being used extensively in web application development services & data analysis, python has emerged as one of the primary languages utilized within AI/ML due to its capability to handle large amounts of data efficiently.

Python for Data Science

Python has revolutionized the field of data science with its powerful libraries and frameworks. NumPy, Pandas, and Matplotlib are some of the key components that make Python an excellent tool for data scientists.

 Pandas is a game-changing library that simplifies data manipulation and analysis tasks. With Pandas, you can easily load datasets from various sources, perform complex queries using DataFrame objects, handle missing values efficiently, and much more.

NumPy is another essential library for numerical computations in Python. It provides fast array operations for large-scale scientific computing applications such as linear algebra or Fourier transforms.

Data visualization is crucial to understand trends within your dataset quickly. Matplotlib offers a wide range of charts/graphs/histograms/diagrams to display your information interactively, providing valuable insights into your dataset.

With these tools under their belt, Data Scientists can explore complex datasets without worrying about implementation details & instead focus on extracting meaningful insights from raw data.

Python for Machine Learning

Machine learning is the practice of teaching machines to learn from data, enabling them to make predictions or decisions without being explicitly programmed. Its applications range from natural language processing and image recognition to fraud detection and autonomous vehicles.

Python has emerged as a leading language for machine learning due to its powerful libraries like scikit-learn & TensorFlow. 

Scikit-Learn provides an extensive array of supervised and unsupervised algorithms that enable users to build models with minimal coding effort. kNN (K-nearest neighbors) is a supervised learning algorithm used to solve classification and regression tasks.

TensorFlow offers an approachable way to create complex Neural Networks(DNN/CNN/RNN) capable of handling large-scale datasets. 

Keras is another popular library built on top of Tensorflow, which simplifies building deep learning models by abstracting away some implementation details.

With these tools, Python developers can leverage machine learning techniques across industries/domains regardless of domain expertise, making it easier than ever for anyone interested in exploring this exciting field.

Advantages of Python in Data Science and Machine Learning

Python has emerged as the language of choice for data science and machine learning because of its many advantages over other languages. Some of these benefits include:

Simplicity & Readability

Python is known for its convenience and readability, making it easy for newcomers to learn. Its straightforward syntax ensures that even complex models can be implemented with ease.

Vast community support and active development: 

The Python community is incredibly supportive, providing users access to vast libraries/forums/blogs, and tutorials. Active development ensures that new tools/features are continually added while existing ones are improved upon.

Easy integration with other tools/languages: 

Python's ability to interface seamlessly with other languages/tools makes it highly versatile enabling developers to use their favorite libraries or leverage specialized hardware like GPUs/Tensor Processing Units (TPUs) without worrying about compatibility issues.

Availability of pre-trained models/Open-source code repositories: 

With numerous open-source libraries such as TensorFlow/Keras/scikit-learn amongst others, Developers can leverage pre-trained models or ready-made solutions rather than building from scratch saving time & effort in implementation.

 These benefits make it clear why Python is becoming increasingly popular among data scientists worldwide

Case Studies and Real-World Applications

Python has proven to be a game-changer in data science and machine learning, as evidenced by numerous case studies showcasing its impact in diverse industries. From healthcare to finance and marketing, it has played a significant role in driving innovation and enabling data-driven decision-making.

 In the healthcare industry, Python is used to analyze medical records and identify patients at risk of developing certain diseases. This enables early intervention and personalized treatment plans based on individual patient needs.

In finance, Python is used to develop models that can predict stock prices or identify fraudulent activities. These models are trained using vast amounts of historical data enabling accurate predictions resulting in better trading decisions while minimizing risks.

Furthermore, it has revolutionized marketing by giving companies access to advanced analytics and machine learning algorithms. 

Real-world success stories also highlight Python's impact. For instance, Netflix relies on Python's recommendation system to provide personalized content suggestions, while Airbnb optimizes pricing algorithms using Python to ensure the best rates for hosts and guests.

These examples highlight how Python is reshaping industries worldwide providing valuable insights into complex datasets leading innovation across domains while offering flexible solutions at every stage.

Conclusion

Python has emerged as a driving force in the fields of data science and machine learning, leaving an indelible impact on the way we approach and leverage data. Its significance cannot be overstated, as it continues to shape industries, drive innovation, and fuel breakthroughs.

In this age of data-driven transformation, the significance of data science and machine learning is undeniable. With an ever-growing demand for insights, these fields promise endless possibilities. Thanks to supportive communities like Finoit, led by visionary CEO Yogesh Choudhary, aspiring data enthusiasts have abundant resources and powerful tools to shape the future. 

So, embrace the power of Python and unlock the doors to a world of unlimited possibilities in data science and machine learning. 

[our13belowconsulting-blog]

13 Below provides you the best DNN web development.

[ariaportal-blog]

Hello community tumblr

Hi This is my first post to get started In the coming days, I will write to you about DNN platform programming technologies Including template design and a variety of modules Thanks to the friends who will be with us in the future

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[codedwebmaster]

Top 8 Advantages Of Using Dnn Cms For Web Development

Top 8 Advantages Of Using Dnn Cms For Web Development

Content marketing is used the number of ways by marketers. And WordPress positions as the most famous content management system, or CMS. However, B2B and mid to more prominent associations regularly require a more significant number of abilities and security than an open source WordPress system can deliver. Dnn Services, which developed from Microsoft, offers exceptional altering capacities,…

[lucywilson540]

DNN(Dotnetnuke) platform secure your business web development

DNN is the most widely used .NET CMS platform for building application and rich website in Microsoft DotNet. The DotNetNuke provides stability, security and support for business-critical applications and is available at a substantially lower cost than alternative proprietary CMS or application development platform solutions. One of the Best  Dotnetnuke Development Company India

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[thegodnnpro-blog]

Software industry in Pakistan: what 2017 holds

Pakistan’s Software industry churns out tons of impressive work every year in terms of freelance programs, software codes and designs. Over the years Pakistan has created a niche for itself and is now considered as the most preferred outsourcing destination across the globe. The selling point of Pakistani Software freelancers is their quality of work, being competitive with top freelancing countries with a bonus of economical digital labor, thereby attracting contractors from around the globe. It is pertinent to mention here that internet proliferation has proved to be the main game changer for all freelance jobs, especially IT related in our region.

According to an estimate the rate at which freelancing is progressing in Pakistan, by 2020 one in every three workers will be an online freelancer. The tech market holds further potential to grow as more than 10,000 IT graduates are added to the existing pool each year and return of the foreign qualified Pakistani graduates further fosters the industry. As of now the number of registered I.T. companies exceeds 1,500 providing a strong foothold to the upcoming generation.

Numerous Pakistani entrepreneurs and startups in the IT industry have proved their mettle time after time. Their commitment to innovative software solutions is making a significant contribution to this vibrant sector of Pakistan’s economy and their commitment to nurture the software industry is substantiating Pakistan as the land of opportunities if you are in anyway linked to the technology industry.

Noticeable trends in the IT industry include fragmentation into highly specialized segments and technologies, noticeable increase in startup activity, self-taught individuals who availed of all of the freely available course materials to educate themselves.

Over the years the IT industry in Pakistan has evolved and has now made its mark globally. The public and the private sector have played a congruent role in its success. The industry however has further room for growth which is now being fulfilled by supportive role played by incubators, accelerators and co-working spaces. About the author: Farah Asim is a Marketing and Communication Strategist over 17 years of corporate exposure and has been associated with Godnnpro in the same capacity. Asim Skype: senecakhan Email: senecakhan@gmail.com

[bestdigitalmarketinginbangalore]

Conversational AI: What is it & how does it work

One of the most common applications of conversational intelligence software is chatbots, which use NLP to interpret user inputs and carry on a conversation. Other applications include virtual assistants, customer service chatbots, and voice assistants. Savvy consumers expect to communicate via mobile apps, the web, interactive voice response (IVR), chat, or messaging channels. They look for a consistent and enjoyable experience that’s fast, easy, and personalised. 

For businesses, the key to meeting and exceeding these expectations across channels and at scale is intelligent automation. Conversational artificial intelligence (AI) powers interactions that are near humans, improving CX, boosting satisfaction, driving loyalty, and increasing customer lifetime value (LTV). Driven by underlying machine learning and deep neural networks (DNN), a typical conversational AI flow includes:

An interface that allows the user to input text into the system or Automatic Speech Recognition (ASR), a user interface that converts speech into text. 

Natural language processing (NLP) extracts the user's intent from the text or audio input and translates the text into structured data.

Natural Language Understanding (NLU) to process the data based on grammar, meaning, and context; to comprehend intent and entity; and to act as a dialogue management unit for building appropriate responses.

An AI model that predicts the best response for the user based on the user's intent and the AI model's training data. Natural Language Generation (NLG) infers from the above processes and forms an appropriate response to interact with humans.

Conversational AI brings together a range of advanced capabilities for an omnichannel UI, contextual awareness, language processing, response generation, intent management, exception/escalation management, advanced analytics, and integration. Cerina Studio is one such intelligent conversational platform where the goal is to incorporate and seamlessly integrate the unique experiences that have been acquired while perfecting Artificial Intelligence (AI), Machine learning (ML), Natural Language Processing (NLP), and live agent support. Powered by 180+ language technology, the studio kit provides a complete conversational experience. The android keyboard and intent technology platform were developed over many years of learning and development. In many cases, the user interface, NLP, and AI model are all provided by the same provider, often conversational intelligent software. However, it is also possible to use different providers for each of these components

[its-annanguyen]

Final Thoughts on Experience

INTRODUCTION

As a Bachelor of Applied Science candidate, I am more focused on the interface design aspect, mainly dealing with web + mobile design & development. I enjoy creating digital arts with a combination of computer programming in HTML/CSS. My hope is to become a UI/UX developer at a high-tech company like Google, Amazon, or even Yelp. That is why I am thankful to have interned at One Wave Designs during the summer of 2018 from June 1st to August 31st as a Web Design & Development Intern.

INTERNSHIP PROCESS

Learning Experience

As a Yelp Elitist, I searched up the “Best Web Design Company” on Yelp, in which One Wave Designs popped up as the first search with a 5-star rating! I gave it a shot and emailed the President/CEO/Owner of the company, Paul. He responded back asking for my resume and portfolio where I finally got a response a month later that I got the internship position!

During my time as an intern, I was responsible for mainly 3 things: web design & development, layout concepts, and SEOs. I mainly worked on 8 projects in 10 weeks. I learned that SEOs (Search Engine Optimization) is very important in web development because websites with good SEOs will always appear at the top of the search list depending on keywords that you use. For instance, if I were to type “Hawaii Web Design” in Google Search, One Wave Designs will be the first to pop up under all the other Google ads search. I learned how to hyperlink emails (mailto:) and phone numbers (tel:), which are also important factors in SEOs. Hierarchy, or the way you order the sizes of the header and texts, also matters too. When you’re adding images or links, it’s good to add a title or alt texts to increase SEO keyword searches. Lastly, saving/uploading images that are 200K or less is great for websites because it loads a lot faster.

I learned how to use 2 types of content management system (CMS), DNN Software and WordPress. I am very familiar with WordPress, it was my first time hearing about DNN. Unfortunately, though, DNN is not used as often anymore and non-developers are shifting to easy CMS like WordPress.I really liked using DNN because of how much coding is involved, whereas WordPress is almost dragging-and-dropping... this is more ideal for non-coders.

I learned how to use an FTP (File Transfer Protocol) software called FileZilla. This allows me to be flexible and customize a website through HTML/CSS coding. Basically, I can manipulate a style of the website by changing up the codes - which can only be done by FTPing and coding. It’s very confusing to explain and understand... I never heard anything like it before until I interned here! After taking web design & development course here at UHWO, I learned that it’s always a smart idea to make copies of the original files that I’ll be editing incase I mess up the codings. Don’t want to repeat that mistake again because there was a time where I had to reset the entire website and build it from scratch. :(

Layout concepts were the MOST STRESSFUL projects I had to do when I interned here. I honestly kind of dreaded it. Paul hated doing layout concepts too! Which explains why I always worked on them instead of him. These 4 software helped me a great deal when I had to make layout concepts/drafts for potential clients:

WhatTheFont.com

Google Fonts

Pantone Color Picker

iStockPhoto.com

WhatTheFont.com allowed me to upload a screenshot of a word so that it can identify the font types for me. Once it generates a few options of fonts, I’d download them (for free) using Google Fonts. Fonts that I find on Google Fonts are great for websites because it doesn’t have to be embedded. Another thing with the web is determining the color, so that’s why I always use the color picker on the Pantone website. Lastly, copyrights and permissions on images/videos/etc. are always questionable. That’s why I always look up stock photos on iStock since we have a subscription with them anyways. 

Discoveries

I feel like I’ve grown as a person over the years. I used to be so shy and quiet, never being the first person to speak or raise my hand. Through this internship among other things, I learned how to speak up and ask questions when I needed to. If this is an unpaid internship that I am devoting much of my time to, I EXPECT to learn quite a few things. It never hurts to ask questions because that’s how you learn -- this is my motto. I learned that I am not that great at criticisms or taking in constructive feedback. I want to learn how to be more patient because there were times I’d get super annoyed when my supervisors would tell me what to do when I’m already doing it or will do it. Also, seeing how much projects I’ve done in such a short time span, I discovered that I am a very quick self-learner. As Paul mentioned, every client will have different expectations when it comes to building their website, which is why he couldn’t help/guide me as much as he should’ve. But in a field like web design/development, everything to customizable and flexible, so there’s never just “one way” to work on every single project.

Sample Work

Here’s a GIF image I found that totally speaks to me when it comes to designing layout concepts: the struggle with making the sizes exact. I was able to learn what the difference is between changing an “image size” and a “canvas size” on Photoshop because of this!

CONCLUSION

I wouldn’t mind working in a place similar to my internship~ I mean, I accepted a job position with One Wave Designs after finishing up my internship hours so that says something :D until this day (December) I am still with them. After 6 months, I learned a lot from working at a small yet successful company. There’s sooOoOoOoo much stress that comes with it. There were several times when Paul would dump a handful of projects on me despite my limited schedule and time constraint. I would lose my cool with him at times, and that’s where I reached my boiling point and told him I had enough. That’s when I discovered how much courage I had. Just a few days ago, I turned in a 30-day resignation letter to him, planning to resign by the end of this year since my last semester of college will be a stressful one yet. After Paul received my letter, he decided to give me a freelance position and allowed me to work whenever I can and work from home instead of in the office (lolol). This is what I’ve always wanted!

To conclude, it was a great experience interning here, but it was even better when I actually got paid. Sometimes I would question whether the amount of work I’m doing would even equate to how much I was getting paid by the hour. Ultimately, I was in it for the long run to build my experience and resume. I finally learned when/where to draw the line, which I should’ve done a lot sooner. 

In the end, I learned that it’s good to build relationships with others and never be afraid to ask questions. Since I showed a lot of dedication and commitment when working here, I was able to earn Paul’s trust and was able to get things my way most of the time. I take internship experience very seriously. I’m here to learn so I’m not afraid to ask questions when I need to.

Whoops, forgot to add my presentation slides here: CM 390 Presentation

[craigbrownphd]

If you did not already know

Progressive Web Application (PWA) Progressive web applications (PWAs) are web applications that load like regular web pages or websites but can offer the user functionality such as working offline, push notifications, and device hardware access traditionally available only to native applications. PWAs combine the flexibility of the web with the experience of a native application. … PyOD PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for use by both practitioners and researchers. With robustness and scalability in mind, best practices such as unit testing, continuous integration, code coverage, maintainability checks, interactive examples and parallelization are emphasized as core components in the toolbox’s development. PyOD is compatible with both Python 2 and 3 and can be installed through Python Package Index (PyPI) or https://…/pyod. … Antisymmetrical Initialization (ASI) How different initializations and loss functions affect the learning of a deep neural network (DNN), specifically its generalization error, is an important problem in practice. In this work, focusing on regression problems, we develop a kernel-norm minimization framework for the analysis of DNNs in the kernel regime in which the number of neurons in each hidden layer is sufficiently large (Jacot et al. 2018, Lee et al. 2019). We find that, in the kernel regime, for any loss in a general class of functions, e.g., any Lp loss for $1 < p < \infty$, the DNN finds the same global minima-the one that is nearest to the initial value in the parameter space, or equivalently, the one that is closest to the initial DNN output in the corresponding reproducing kernel Hilbert space. With this framework, we prove that a non-zero initial output increases the generalization error of DNN. We further propose an antisymmetrical initialization (ASI) trick that eliminates this type of error and accelerates the training. We also demonstrate experimentally that even for DNNs in the non-kernel regime, our theoretical analysis and the ASI trick remain effective. Overall, our work provides insight into how initialization and loss function quantitatively affect the generalization of DNNs, and also provides guidance for the training of DNNs. … Spline-Based Probability Calibration (SplineCalib) In many classification problems it is desirable to output well-calibrated probabilities on the different classes. We propose a robust, non-parametric method of calibrating probabilities called SplineCalib that utilizes smoothing splines to determine a calibration function. We demonstrate how applying certain transformations as part of the calibration process can improve performance on problems in deep learning and other domains where the scores tend to be ‘overconfident’. We adapt the approach to multi-class problems and find that better calibration can improve accuracy as well as log-loss by better resolving uncertain cases. Finally, we present a cross-validated approach to calibration which conserves data. Significant improvements to log-loss and accuracy are shown on several different problems. We also introduce the ml-insights python package which contains an implementation of the SplineCalib algorithm. … https://analytixon.com/2022/09/14/if-you-did-not-already-know-1828/?utm_source=dlvr.it&utm_medium=tumblr