Clock Train in JavaScript | Cool UI JavaScript Clock Train

In this video we Build a clock train in JavaScript, cool UI JavaScript clock train you can get the source code of JavaScript clock in ourcodesolution.com

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UI Designer Vs UI Developer

Comparing UI Designers to UI Developers
User interface (UI) designers and developers are directly responsible for the consumer base’s experience using an application or software program. Designers specifically deal with the visual aspects of the program, while developers deal with the overall performance and functionality of the software.
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Responsibilities of UI Designers vs. UI Developers
UI designers and developers work in tandem to create a program or application that is easy to understand and operate by their customers or clients. Though there may be some occasional overlap in the duties within the workplace, their designated duties are quite clear and are dependent on the other. UI developers are responsible for the coding and programming in the conception of an application, specifically with regard to how the software operates at the hands of the user. UI designers are in charge of applying their understanding of the program operations to create a visual experience that is most compatible to the program’s functionality.

UI Designers
User interface designers are tasked with understanding the programming language of the application in creation so that they can conceptualize and craft visual aspects that will facilitate usage of the program. They are expected to understand computer programming as well as graphic design due to the demands of their work, since they are in charge of incorporating their designs into the program correctly. Their designs are implemented into the layout, which is typically drafted by the developers, while the style of their designs is contingent on the guidelines given by the directors. Once these designs are finished, they must implement them into the program and run a demo of it for the developers and directors to ensure they met the needs and expectations of the project while ensuring there aren’t any bugs caused from their designs. Get more skills from UI Training

Other responsibilities of UI designers are as follows:

• Make drafts in graphic design and editing software
• Select fonts and determine color schemes, for consistency
• Proficiency in programming codes such as Java or CSS
• Create storyboards and test runs of animated, visual concepts

UI Developers
User interface developers are responsible for the functional aspects of a software application, coding and programming throughout all stages of development with the clients and potential users of the application in mind. They usually begin the process by incorporating the clients’ expressed needs into a layout that is modified as progress is made. Once they get the general functions working, the designers will incorporate their visual conceptions into the layout to ensure that the first draft is operational. If there are any bugs or malfunctions to fix, the developers must troubleshoot and patch the application. While doing these tasks, they must take detailed notes of all the progress made to streamline any future updates made to the program, functionally or aesthetically. Learn more from ui design course

UI developers will also be responsible for:

• Utilizing research data to improve or build onto the design of the application
• Suggesting any software updates to improve functionality
• Constructing diagrams that will aide other developers and programmers on the project
• Performing test runs of the application

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PyTorch Unsupervised Sentiment Discovery

** DEPRECATED **

This repo has been deprecated. Please visit Megatron-LM for our up to date Large-scale unsupervised pretraining and finetuning code.

If you would still like to use this codebase, see our tagged releases and install required software/dependencies that was available publicly at that date.

PyTorch Unsupervised Sentiment Discovery

This codebase contains pretrained binary sentiment and multimodel emotion classification models as well as code to reproduce results from our series of large scale pretraining + transfer NLP papers: Large Scale Language Modeling: Converging on 40GB of Text in Four Hours and Practical Text Classification With Large Pre-Trained Language Models. This effort was born out of a desire to reproduce, analyze, and scale the Generating Reviews and Discovering Sentiment paper from OpenAI.

The techniques used in this repository are general purpose and our easy to use command line interface can be used to train state of the art classification models on your own difficult classification datasets.

This codebase supports mixed precision training as well as distributed, multi-gpu, multi-node training for language models (support is provided based on the NVIDIA APEx project). In addition to training language models, this codebase can be used to easily transfer and finetune trained models on custom text classification datasets.

For example, a Transformer language model for unsupervised modeling of large text datasets, such as the amazon-review dataset, is implemented in PyTorch. We also support other tokenization methods, such as character or sentencepiece tokenization, and language models using various recurrent architectures.

The learned language model can be transferred to other natural language processing (NLP) tasks where it is used to featurize text samples. The featurizations provide a strong initialization point for discriminative language tasks, and allow for competitive task performance given only a few labeled samples. For example, we consider finetuning our models on the difficult task of multimodal emotion classification based on a subset of the plutchik wheel of emotions.

Created by Robert Plutchik, this wheel is used to illustrate different emotions in a compelling and nuanced way. He suggested that there are 8 primary bipolar emotions (joy versus sadness, anger versus fear, trust versus disgust, and surprise versus anticipation) with different levels of emotional intensity. For our classification task we utilize tweets from the SemEval2018 Task 1E-c emotion classification dataset to perform multilabel classification of anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. This is a difficult task that suffers from real world classification problems such as class imbalance and labeler disagreement.

On the full SemEval emotion classification dataset we find that finetuning our model on the data achieves competitive state of the art performance with no additional domain-specific feature engineering.

Setup

Install

Install the sentiment_discovery package with python3 setup.py install in order to run the modules/scripts within this repo.

Python Requirements

At this time we only support python3.

• numpy
• pytorch (>= 0.4.1)
• pandas
• scikit-learn
• matplotlib
• unidecode
• sentencepiece
• seaborn
• emoji

Pretrained models

We've included our sentencepiece tokenizer model and vocab as a zip file:

• sentencepiece tokenizer [1MB]

We've included a transformer language model base as well as a 4096-d mlstm language model base. For examples on how to use these models please see our finetuning and transfer sections. Even though these models were trained with FP16 they can be used in FP32 training/inference.

• FP16 Transformer LM [311MB]
• FP16 mLSTM LM [169MB]

We've also included classifiers trained on a subset of SemEval emotions corresponding to the 8 plutchik emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust):

• Finetuned Plutchik Transformer [673MN]
• Finetuned Plutchik mLSTM [433MB]

Lastly, we've also included already trained classification models for SST and IMDB binary sentiment classification:

• Finetuned SST Transformer [621MB]
• Transferred SST mLSTM [325MB]
• Transferred IMDB mLSTM [325MB]

To use classification models that reproduce results from our original large batch language modeling paper please use the following commit hash and set of models.

We did not include pretrained models leveraging ELMo. To reproduce our papers' results with ELMo, please see our available resources.

Each file has a dictionary containing a PyTorch state_dict consisting of a language model (lm_encoder keys) trained on Amazon reviews and a classifier (classifier key) as well as accompanying args necessary to run a model with that state_dict.

Data Downloads

In the ./data folder we've provided processed copies of the Binary Stanford Sentiment Treebank (Binary SST), IMDB Movie Review, and the SemEval2018 Tweet Emotion datasets as part of this repository. In order to train on the amazon dataset please download the "aggressively deduplicated data" version from Julian McAuley's original site. Access requests to the dataset should be approved instantly. While using the dataset make sure to load it with the --loose-json flag.

Usage

In addition to providing easily reusable code of the core functionalities (models, distributed, fp16, etc.) of this work, we also provide scripts to perform the high-level functionalities of the original paper:

• sentiment classification of input text
• unsupervised reconstruction/language modeling of a corpus of text (+ script for launching distributed workers)
• transfer of learned language model to perform sentiment analysis on a specified corpus
• sampling from language model to generate text (possibly of fixed sentiment) + heatmap visualization of sentiment in text

Classifying text

Classify an input csv/json using one of our pretrained models or your own. Performs classification on Binary SST by default. Output classification probabilities are saved to a .npy file

python3 run_classifier.py --load_model ama_sst.pt                               # classify Binary SST
python3 run_classifier.py --load_model ama_sst_16.pt --fp16                     # run classification in fp16
python3 run_classifier.py --load_model ama_sst.pt --text-key <text-column> --data <path.csv>     # classify your own dataset

See here for more documentation.

Training Language Models (+ Distributed/FP16 Training)

Train a language model on a csv/json corpus. By default we train a weight-normalized, 4096-d mLSTM, with a 64-d character embedding. This is the first step of a 2-step process to training your own sentiment classifier. Saves model to lang_model.pt by default.

python3 pretrain.py                                                               #train a large model on imdb
python3 pretrain.py --model LSTM --nhid 512                                       #train a small LSTM instead
python3 pretrain.py --fp16 --dynamic-loss-scale                                   #train a model with fp16
python3 -m multiproc pretrain.py                                                  #distributed model training
python3 pretrain.py --data ./data/amazon/reviews.json --lazy --loose-json \       #train a model on amazon data
  --text-key reviewText --label-key overall --optim Adam --split 1000,1,1 
python3 pretrain.py --tokenizer-type SentencePieceTokenizer --vocab-size 32000 \  #train a model with our sentencepiece tokenization
  --tokenizer-type bpe --tokenizer-path ama_32k_tokenizer.model 
python3 pretrain.py --tokenizer-type SentencePieceTokenizer --vocab-size 32000 \  #train a transformer model with our sentencepiece tokenization
  --tokenizer-type bpe --tokenizer-path ama_32k_tokenizer.model --model transformer \
  --decoder-layers 12 --decoder-embed-dim 768 --decoder-ffn-embed-dim 3072 \
  --decoder-learned-pos --decoder-attention-heads 8
bash ./experiments/train_mlstm_singlenode.sh                                      #run our mLSTM training script on 1 DGX-1V
bash ./experiments/train_transformer_singlenode.sh                                #run our transformer training script on 1 DGX-1V 

For more documentation of our language modeling functionality look here

In order to learn about our language modeling experiments and reproduce results see the training reproduction section in analysis.

For information about how we achieve numerical stability with FP16 training see our fp16 training analysis.

Sentiment Transfer

Given a trained language model, this script will featurize text from train, val, and test csv/json's. It then uses sklearn logistic regression to fit a classifier to predict sentiment from these features. Lastly it performs feature selection to try and fit a regression model to the top n most relevant neurons (features). By default only one neuron is used for this second regression.

python3 transfer.py --load mlstm.pt                                 #performs transfer to SST, saves results to `<model>_transfer/` directory
python3 transfer.py --load mlstm.pt --neurons 5                     #use 5 neurons for the second regression
python3 transfer.py --load mlstm.pt --fp16                          #run model in fp16 for featurization step
bash ./experiments/run_sk_sst.sh                                    #run transfer learning with mlstm on imdb dataset
bash ./experiments/run_sk_imdb.sh                                   #run transfer learning with mlstm on sst dataset

Additional documentation of the command line arguments available for transfer can be found here

Classifier Finetuning

Given a trained language model and classification dataset, this script will build a classifier that leverages the trained language model as a text feature encoder. The difference between this script and transfer.py is that the model training is performed end to end: the loss from the classifier is backpropagated into the language model encoder as well. This script allows one to build more complex classification models, metrics, and loss functions than transfer.py. This script supports building arbitrary multilable, multilayer, and multihead perceptron classifiers. Additionally it allows using language modeling as an auxiliary task loss during training and multihead variance as an auxiliary loss during training. Lastly this script supports automatically selecting classification thresholds from validation performance. To measure validation performance this script includes more complex metrics including: f1-score, mathew correlation coefficient, jaccard index, recall, precision, and accuracy.

python3 finetune_classifier.py --load mlstm.pt --lr 2e-5 --aux-lm-loss --aux-lm-loss-weight .02   #finetune mLSTM model on sst (default dataset) with auxiliary loss
python3 finetune_classifier.py --load mlstm.pt --automatic-thresholding --threshold-metric f1     #finetune mLSTM model on sst and automatically select classification thresholds based on the validation f1 score
python3 finetune_classifier.py --tokenizer-type SentencePieceTokenizer --vocab-size 32000 \       #finetune transformer with sentencepiece on SST
  --tokenizer-type bpe tokenizer-path ama_32k_tokenizer.model --model transformer --lr 2e-5 \
  --decoder-layers 12 --decoder-embed-dim 768 --decoder-ffn-embed-dim 3072 \
  --decoder-learned-pos --decoder-attention-heads 8 --load transformer.pt --use-final-embed
python3 finetune_classifier.py --automatic-thresholding --non-binary-cols l1 l2 l3 --lr 2e-5\     #finetune multilayer classifier with 3 classes and 4 heads per class on some custom dataset and automatically select classfication thresholds
  --classifier-hidden-layers 2048 1024 3 --heads-per-class 4 --aux-head-variance-loss-weight 1.   #`aux-head-variance-loss-weight` is an auxiliary loss to increase the variance between each of the 4 head's weights
  --data <custom_train>.csv --val <custom_val>.csv --test <custom_test>.csv --load mlstm.pt
bash ./experiments/se_transformer_multihead.sh                                                    #finetune a multihead transformer on 8 semeval categories

See how to reproduce our finetuning experiments in the finetuning reproduction section of analysis.

Additional documentation of the command line arguments available for finetune_classifier.py can be found here

Analysis

• Why Unsupervised Language Modeling?
  • Difficulties of Supervised Natural Language
  • Data Robustness
  • Model/Optimization Robustness
• Reproducing Results
  • Training
    • Transformer Training Setup
    • mLSTM Training Setup
  • FP16 Training
  • Large Model Training
  • Sentiment Transfer
  • Finetuning Classifiers
  • ELMo Comparison
• Data Parallel Scalability
  • PyTorch + GIL
• Open Questions

Acknowledgement

A special thanks to our amazing summer intern Neel Kant for all the work he did with transformers, tokenization, and pretraining+finetuning classification models.

A special thanks to @csarofeen and @Michael Carilli for their help developing and documenting our RNN interface, Distributed Data Parallel model, and fp16 optimizer. The latest versions of these utilities can be found at the APEx github page.

Thanks to @guillitte for providing a lightweight pytorch port of openai's sentiment-neuron repo.

This project uses the amazon review dataset collected by J. McAuley

Thanks

Want to help out? Open up an issue with questions/suggestions or pull requests ranging from minor fixes to new functionality.

May your learning be Deep and Unsupervised.

---

Download details:

Author: NVIDIA
Source: https://github.com/NVIDIA/sentiment-discovery

License: View license

#pytorch 

UX designer ? UI designer ? UI Developer ?

The UX designer is someone who thinks about what should the user flow be like, which page should lead to which page, when should a confirm popup appear or not appear, should there be a listing page before or after a create-new page, should there be an address field in the page or geolocation is enough to serve the purpose? After brainstorming through each of these and several other questions, the UX designer comes up with something known as wireframes, which in simple terms is just a blueprint of the website/app.

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The UI designer then takes the wireframes and makes them beautiful, also ensuring that the workflow of the product is communicated well to the user. He will add the pixel level details to the wireframes. What should be the font used, what should be the background image, do we need a background image, what should be the foreground color, how big should be the submit button, does it make more sense to have the menu at the bottom of the screen, what should the logo look like? The job of a UI designer is answering all these and thereafter delivering static mockups, using may be Photoshop, Invision and many other design tools.

The UI developer is the one who puts these static mockups in “real code”. They might need skills like HTML CSS , precompilers(like sass or less) , UI frameworks (like bootstrap or foundation), or xml layouts( in case of android UI) or a combined knowledge of all of them with Javascript (in case of react, react native). The result is a beautiful set of screens/pages which can be actually rendered in a browser or a mobile device.Learn more from ui design course

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In the world of modern technology, new terms appear every day. Though the term ‘UI’ & ‘UX’ has been around for a while, people still get confused between the two. At AMH WebStudio, we believe that both UX & UI complement each other and to be precise User Interface (UI) is a part of User Experience (UX).

Difference between UX & UI

To make it simple, UI is how things look, while UX is how things feel.
The user interface (UI) is the series of visual elements, screens, pages, like buttons and icons, that enable a person to interact with a product or service.

User experience (UX), on the other hand, is the overall experience that a person has while they interact online with every aspect of products and services.

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User Interface Design
User Interface (UI) design is a combination and behavior of content e.g. text, videos, documents, images, form elements and etc. It’s the first thing that grabs a user’s attention when they visit a website.
UI also plays an important role in a user’s decision to stay on a website or leave it immediately.

It takes a lot of practice, a good eye for details, a lot of trails and rectifications to get better and better. The goal of a UI designer should be to create a beautiful and engaging user interface and at the same time, it must also connect with the user’s emotions to let them observe your products/services.

Always consider your app or website as a journey for your users that must be beautiful, easy and engaging at the same time. A UI designer is the tour guide that takes the user on a wonderful journey through your app or website. To make any journey delightful, one should always remember to guide the users step-by-step with more engagement and attention.

Designing isn’t just about using tools for visual graphics. It’s also about explaining your idea to your users via graphic. You need to brainstorm, experiment, test, and understand your users and their journey throughout using your products/services. The benefits of having a well-designed product are that you’ll have a higher user retention rate.

Things to remember while creating a delightful UI.

Big, Bold & Bright:
We humans have nature to always get attracted towards the biggest, the boldest and the brightest first. Then our attention moves to smaller, less bold, and less bright things, isn’t it?
As a designer, you can use this information to curate the experience of your user by displaying the most important part of your presentation in big, bold or bright format.

Importance Of Alignment:
Alignment is a fundamental aspect of UI Design. It improves readability and makes the design more pleasing to the eye. An important design principle is to minimize the number of alignment lines.
There are two fundamental types of alignment: ‘Edge’ and ‘Center’ alignment. Depending on the use-case, you’ll choose one or the other. Usually, edge alignment is considered better. It’s quite easy to align elements in design software like Photoshop. Most design software will usually provide a ruler/guide to edge align all the elements.Learn more from ui design course

Become an attention architect:
To become a great designer you need to grab the user’s attention with your design. To create a great design you need to pay attention to every little thing in your designs. The latter lets you achieve the former.
UI Design is about tailoring the experience for your users by guiding their attention towards different important things.

Best ways to grab user’s attention:
• Make its size larger or smaller.
• Italicize words. Capitalise or lowercase some words.
• Bolder or brighter in color.
• Choose the correct typeface with correct weight.
• Be sure there is enough breathing space in between two lines.

Experiment with different designs:
The most important thing to consider while designing is Testing! Try out different colors, fonts, tones, angles, alignment, layout, etc. Experimenting with different designs to architect a user journey that is beautiful, easy and engaging.
User Experience Design
User Experience (UX) design is all about constructing hassle-free and enjoyable experiences for your users to land on their desired goals. Learn more from ui design course

“User experience” refers to a person’s interactions with a product, application, or operating system. It defines what and where things should be present on the screen.
User experience design is a broad field and becoming more popular by the day. Not only companies but many others that develop products or provide services are catching on to the value of understanding their users and validating their hypotheses before they build.
To put it simply, ‘research’ is at the heart of all UX work. Without spending time researching the customer experience with a product there is no UX.

1. Usability
Brainstorm on what is the user using my app or website for?
What is the core functionality of my app or website?
What is it that I need to do in order to make my product useful?
How can I minimize the number of clicks or steps that it takes for the user to achieve their goals?
What’s the main thing my users want to achieve with my app or website?
How can I make my user’s experience smooth, quick, and enjoyable?

2. User Persona and Profile:
Identify what your user types are and what they want to achieve via your app. The best possible way to do this is by profiling your users. Understand the market with a few thinking exercises. Filter down your target user/audience.
Keep repeating the question to yourself that what is the core functionality of your website/app.

3. Asking for permissions:
If your website/app has push notification, needs location services, integration with social media, email, etc., make sure you take the permission of the users when they’re using your app/website. Make sure your app/website sends the permission notification only when the user is about to use that feature and not when they just launch the app/website.

4. Form Elements vs Form Functionality:
Always prefer the function over form as Designs are not always about the forms such as beautiful color scheme, the fonts, the layout, and such. It more about functionality.

5. Consistency:
Are you being consistent throughout your app? Inconsistency in design creates confusion for the users and they might opt for your competitors in that case, as they are unhappy with your presentation. Thinking of consistency in terms of appearance is good but better in terms of functionality.

6. Simplicity:
Can I make it more simple than before? Ask yourself this question to make your app/website use by older people efficiently. Believe me, a tightly packed user interface, lots of different colors, number of buttons to click is one such example of a bad User Experience (UX) Design.

7. Don’t make users think:
Am I making things difficult for my user? We as a human don’t like to be confused or engage with something that is much complex. When programming, make sure to use lightweight codes to make it work as efficiently as possible.
When designing, make sure to keep the interface as clean as possible to avoid any confusion within the user.

Below are some points to note for a great UX design:

Give hints and tips. Try to design an interface where users figure out the app/website within a few seconds. That’s where intuitive design principles come!
Make things as simple as possible. Stick to the standard rule as a pull to refresh, or pinch to zoom. Do not use these actions for some other goals.
Unnecessary pop-ups and alerts interrupt the flow, resulting in bad user experience. Do not make your customers feel stupid by giving a popups or alerts to the user to confirm a frequent action. Ask for confirmation only for harmful actions users might regret, like deleting something or making a purchase, etc.
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DIFFERENCE BETWEEN UI/UX DESIGNER AND UI/UX ENGINEER

UI is a User Interface while UX is a User Experience. First of all, several people recognize it to be the same which is incorrect. They are deeply related but pretty inconsistent. UI is more relevant to the designing phase while UX is more relevant to analysis and scientific perspectives.

UI/UX Designer vs UI/UX Engineer
UI/UX Designer
The most reliable system to know the difference between User Interface/User Experience designer and developer is to learn the skill set they need to own. Coming first to the designer, as declared earlier, they require to study and design. In other words, they require learning and interpreting the needs of the users and express concepts and design concepts that those clients will prefer to use. Hence, they require having a perception of online users’ behaviors, psychology, and important features. One requires walking in the shoes of the online users and obtaining a comprehensive hold of what is cooking inside their heads while they are browsing.

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But that is all non-technical abilities. For a designer, the following skill set is also essential and they are technical. It is similar to graphic designing. A User Interface/User Experience designer requires understanding all the industrial-standard design tools particularly the ones that are generally utilized. Not just that, he must have a comprehensive idea about the professional designing method that comprises the UI UX Development Company as well as the application developers. The wireframes of the application are composed of UI/UX designer which form the groundwork on any application. Hence, it is a combinational skill set of analysis, research, and design.

UI/UX Engineer
As said earlier, the position of UI/UX engineer is more functional and technical. After the analysis and review part is performed by the UI/UX designer, he colludes with the UI/UX designer to design the wireframe and also know the assessment process and the concepts and motivations behind all the designs. He has to spend attention to the point the design seems good and it is useful on all browsers or operating systems or machine configurations. A UI/UX designer may not possess any concept whether what he is designing will operate on all browsers or not and therefore, the collaboration is sufficient to make the ultimate design that works for the UI/UX engineer as well as the application developer.Learn more from ui design course

After the collaboration is made, the engineer must decode the wireframes and all the ideas into the practical output. Hence, apart from having a design skillset, he requires to understand the programming languages utilized to design the front-end of any application. Amongst the front-end programming languages, HTML+JS+CSS is a necessity. Then it relies on the application as well as the technology the firm utilizes for the front-end development. It can also rely on the need of the customers.

Sometimes, the responsibility of converting wireframes into HTML layouts is performed by UI.UX designer and the UI/UX developer fine shape them so that the application developer can begin developing the back-end and combine it with the front-end seamlessly. He has to retain in mind how the designs of the front-end he is planning are going to act in different browsers, how much time it will demand to upload, how many resources it will use, how the clients will react and much more. UI UX Designing Agency in Qatar.

Appearances Approached:
A UI/UX designer is also regarded with the appearance and feel of the interface. He is into client surveying, client analysis, and then doing design analysis of the competitors, and then imagining the design plans. After that, he is regarded with branding and graphics development. On the other side, a UI/UX engineer is regarded with responsiveness and interactivity. The primary thing he pays consideration to is UI prototyping. Once that is out of the way, he looks into the interactivity and animation facets. The adaption to all tools, platforms, and browsers is taken care of and the deceptions of the design perspectives are stroke off. Then he starts the implementation after getting the application developer on board.

In technological terms, the job description of UI UX Design Agency is analysis, data design, and visual design. The job specification of a User Interface/User Experience engineer is visual design, interactive design, and front-end development. Because of the front-end part, the salary of engineers is more than the designer relied on similar years of experience. You can begin as a UI/UX designer and initiate a transition to the UI/UX engineer with adequate experience.

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