This allows TensorFlow. PyTorch vs TensorFlow. This library is an open-source neural-network library framework. if I'm not mistaken, tensorflow utilizes WebGL to operate on hardware (mainly GPU) so the performance hit by js is not aggressive. python documentation: PyDotPlus. When you write TensorFlow code it gets "compiled" into a graph by Python and then run by the TensorFlow execution engine. Similarly, python has been widely used in the field of data analytics, artificial intelligence, deep learning, and web development. To avoid this problem, TF 2. Computer Graphics. Theano and Tensorflow are primarily deep learning libraries but also allow for key linear algebra to be performed on a. Generally speaking, Python is indeed slower. Definitely a software to fall in love with. "TensorFlow performance and advance topics" Mar 7, 2017. js version 1. Currently Tensorflow has limited support for dynamic inputs via Tensorflow Fold. PyTorch has it by-default. wemake-python-styleguide - The strictest and most opinionated python linter ever. TensorFlow Package: Performance of model using TensorFlow This website uses cookies to ensure you get the best experience on our website. Why The Best Python IDE? visual studio code supports syntax highlighting and auto-complete with IntelliSense. Understanding the Input/Output (I/O) model of your application can mean the difference between an application that deals with the load it is subjected to, and one. It is also assumed that model weights can be accessed from relative paths described by the paths fields in weights manifest. js: What are the differences? PyTorch: A deep learning framework that puts Python first. In addition to. Data Science Training in Chennai at Credo Systemz provided by experienced Data Scientists. The results for the elapsed time to run the algorithm are summarized in the table below:. In this course, Deep Learning Using TensorFlow and Apache MXNet on AWS SageMaker, you'll be shown how to use the built-in algorithms, such as the linear learner and PCA, hosted on SageMaker containers. TensorFlow allows you to create large-scale neural networks with many layers like CNN, RNN etc. NET detailed comparison as of 2019 and their Pros/Cons When comparing TensorFlow vs Infer. Guru99 is totally new kind of learning experience. Based on some reviews, PyTorch also shows a better performance on a lot of models compared to TensorFlow. Motivation. Both of them are among the top programming languages according to the TOIBE index. Similarly, python has been widely used in the field of data analytics, artificial intelligence, deep learning, and web development. js (JavaScript). PyTorch and TensorFlow libraries are two of the most commonly used Python libraries for deep learning. In this post, we will build an image object detection system with Tensorflow-js with the pre-trained model. js is designed as a WebGL accelerated library, I expected the best performance on a powerful GPU [4]. Which language runs faster? There's no two ways to say this: Go runs faster than Python, in some use cases even 30. This is because Node. TensorFlow allows you to create large-scale neural networks with many layers like CNN, RNN etc. With almost two decades of business software development, Brad's led web teams, been a Linux sysadmin, and developed a storefront in Go. Many binaries depend on numpy-1. Introduction to TensorFlow Keras is available for Python and works not only for TensorFlow but also for CNTK and Theano. You should learn both, and not just for tensorflow, learning a language for one specific library is a shallow motive. Summary: There's a three way technology race to bring faster, easier, cheaper, and smarter AI. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Python has high-quality library support for a majority of their applications. openCV ML vs Tensorflow? Depth- och IR-data from RealSense camera (SR300)? Odd issue with OpenCV. Earlier this year, a Google software engineer wrote a blog post on. This comparison on Keras vs TensorFlow vs PyTorch will provide you with a crisp knowledge about the top Deep Learning Frameworks and help. Performance. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. I'm trying to run inference using tensorflow. My Raspberry Pi was running Python 3. Image recognition is a start up problem when comes to tensorflow. 0 Google's TensorFlow 2. In this tutorial, we'll cover the theory behind text generation using a Recurrent Neural Networks, specifically a Long Short-Term Memory Network, implement this network in Python, and use it to generate. TensorFlow release 1. Golang is fast, and since the language is compiled, the developers can interpret it faster. Finally, a word about Plotly. This concept will sound familiar if you are a fan of HBO's Silicon Valley. Python supports a wide variety of platforms in the Industry from Play station, Solaris, Macintosh, Linux and MS Windows. I did some research and from what i understand Lua is a scripting language very alike Javascript but i dont see JS being used in the AI branch even tho all the support like JQuery JS has. Python and C++ frontends sit atop the execution engine (the Layers API module offers a simpler interface for commonly used layers in deep learning models) and on top of that, higher. $ pip install --ignore-installed --upgrade tensorflow # Use pip for Python 2. Develop programs that work either in the browser or with Node JS. js or python,you are actually comparing JavaScript with Python. Note, however, that Keras does allow you to get access to the TensorFlow session. Instead, it uses another library to do. Now, it's time to see which language, Go or Python, is better specifically for AI programming. ☞ Become a JavaScript developer - Learn (React, Node,Angular) ☞ JavaScript: Understanding the Weird Parts ☞ Vue JS 2 - The Complete Guide (incl. …So why are we going through the extra step of using Keras…instead of just using TensorFlow on its own. It is used to develop web applications (an application i. …TensorFlow is a popular tool for building…and training deep neural networks. Performance Input pipeline optimization. I'm not sure if this is helpful however, given its so niche I imagine a support ticket to AMD may yield faster information than the forum. TensorFlow provides stable Python and C APIs as well as non-guaranteed backwards compatible API's for C++, Go, Java, JavaScript, and Swift. js means you're using the same language on the frontend and the backend. Additionally, even with powerful GPU, I noticed significant discrepancies in measured performance, depending on whether the browser runs on my main laptop screen or on an external screen attached to the HDMI port. We are going to. Trying to decide among the API's to use: python, c++, or java. If you want to compare speed you must choose specific implementations to compare with each other. Vue Router & Vuex) ☞ Complete Guide to TensorFlow for Deep Learning with Python ☞ Machine Learning with TensorFlow + Real-Life Business Case ☞ Machine Learning & Tensorflow - Google Cloud. As a company owner or JavaScript developer learn how you can use the power of Node. You can use the new AutoGraph feature of tf. The Nuts and Bolts Behind OctoLingua. Python codes are easier to maintain and more robust than R. Take Python; the libraries for ML (scikit-learn, NumPy, Theano, etc) provide more than just a wider support of ML algorithms than Node (which is important!); but also per. 12 with XLA. Models and examples built with TensorFlow. You can also find TensorFlow APIs in multiple languages (Python, C++, Java, and Go); and the community has developed other bindings, including C#, Haskell, Julia, Ruby, Rust, and Scala. …TensorFlow comes with a great web-based tool called…TensorBoard that lets us visualize our model's structure…and monitor its training. I answered with /usr/local/bin/python3 because I want to use Python 3. TensorFlow is an open-source machine learning software built by Google to train neural networks. js provides flexible building blocks for neural network programming in JavaScript. Selecting the runtime. 4 is now available using standard pip installation. Keep up to date with release announcements and security updates by subscribing to [email protected] This article is part of a more complete series of articles about TensorFlow. However, there are different performance. js uses the graphics card (GPU) of the computer via the browser API WebGL. Currently Tensorflow has limited support for dynamic inputs via Tensorflow Fold. …TensorFlow comes with a great web-based tool called…TensorBoard that lets us visualize our model's structure…and monitor its training. Python 1, R 1. js and browser-based applications. These are a great way to get started with a project, or to help share the load of working on open source projects. I did not use Tensorflow. 16+mkl and the Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019 for Python 3, or the Microsoft Visual C++ 2008 Redistributable Package x64, x86, and SP1 for Python 2. js is a Javascript library created by Google to embed Machine Learning capabilities to any form of web application. Keras - more deployment options (directly and through the TensorFlow backend), easier model export. Python Developer - Job Description and Ad Template Company Introduction {{Write a short and catchy paragraph about your company. Feather (Fast reading and writing of data to disk) Fast, lightweight, easy-to-use binary format for filetypes; Makes pushing data frames in and out of memory as simply as possible; Language agnostic (works across Python and R) High read and write performance (600 MB/s vs 70 MB/s of. This tutorial shows how to use TensorFlow with Horovod on a Deep Learning AMI with Conda. What is the best way to find out which versions of CUDA and cuDNN Tensorflow uses? I made a mess trying to get tensorflow-gpu_2. We make tons of efforts to take boredom out of learning and make education a fun experience. Python is the right language when it comes to automatic memory management as it is dynamic in nature. The TensorFlow Playground is a web application which is written in d3. PyTorch is much more dynamic with computational graphs than Tensorflow. Although neither Java nor Python is especially suited to high-performance computing, when performance matters, Java has the edge by platform and by design. js vs Spring Boot Flyway vs Liquibase AWS CodeCommit vs Bitbucket vs GitHub. Both R Programming vs Python are popular choices in the market; let us discuss the Top key Differences Between R Programming vs Python to know which is the best: R was created by Ross Ihaka and Robert Gentleman in the year 1995 whereas Python was created by Guido Van Rossum in the year 1991. PyTorch has it by-default. It seems as TensorFlow's functionality defines in Python and then moved to C++. Python Tools for Visual Studio is a completely free extension, developed and supported by Microsoft with contributions from the community. 'weightsManifest': A TensorFlow. Read more. This "new" approach needs some time to get used to, but I think it is very intuitive if you have worked with Python outside of Deep Learning before. I'm not sure if this is helpful however, given its so niche I imagine a support ticket to AMD may yield faster information than the forum. Models and examples built with TensorFlow. Note, however, that Keras does allow you to get access to the TensorFlow session. Advanced memory profiling to enhance the performance of your algorithms. Neural Engineering Object (NENGO) – A graphical and scripting software for simulating large-scale neural systems; Numenta Platform for Intelligent Computing – Numenta's open source implementation of their hierarchical temporal memory model. You lose a bit of performance because WebGL can only be tricked into executing the matrix multiplication required by TensorFlow. Now let us get to each of the above factors and see how Python and PHP compare with each other. Python speed, you'll find that the former is faster. …TensorFlow comes with a great web-based tool called…TensorBoard that lets us visualize our model's structure…and monitor its training. TensorFlow. Google's TensorFlow 2. Python, as such is a full fledged programming language and many organisations use it in their production systems. NET and Java. js to perform visual recognition on images using JavaScript from Node. js and Custom Classifiers. Only after that can we understand the total concept of Deep Learning with TensorFlow. Theoretically, yes. ]TensorFlow is an open-source software library for dataflow programming across a range of tasks. Type errors (such as passing a String as an argument to a method which expects an Integer) are to be expected from time-to-time. " "No servers for applications. js which is now called TensorFlow. Python2 vs Python3 | Syntax and performance Comparison Python 2. Python is a general-purpose programming language, and it can be picked up very quickly. Let’s start from NumPy (you’ll see why a bit later). To add a new package, please, check the contribute section. The setup is as follows. Net awesomeSites Blogging BOOK Bootstrap Browser c# chrome COM Comp Hardware CSS CUSTOMIZATIONS DarkNet Database Debugging Design Patterns Eclipse Editors&IDEs Excel GameDev Git GitHub Google Graphics H2 HTML hybridApp IIS iOS javascript Jive JQuery Linux Mainframe Memory Misc. (A few of these are described below). Python is the right language when it comes to automatic memory management as it is dynamic in nature. Leveraging the GPU results in a 17x performance increase! It's worth mentioning that we're running this is on a powerful 8 core Intel Xeon processor—the GPU speedup will often exceed these results. 7 is scheduled to be the last major version in the 2. I know that mxnet was the fastest, highly scalable, highly distributed framework out there. TensorFlow will standardize on SavedModel as an interchange format for TensorFlow Serving, TensorFlow Lite, TensorFlow. All but Torch support Python integration while only TensorFlow and mxnet support R directly. For instructions on how to prepare your local machine for Python development, see Setting Up a Python Development Environment. benchmarks by tensorflow - Benchmark code. This is, unfortunately, to the extent of the resemblance. But on the other, having a Tensorflow API doesn't suddenly give it a bunch of libraries for statistics, comp. Jun 8, 2016. Xray intends to improve and create a cross-platform text editor with high performance, easy collaborating, convenient extensibility, and seamless compatibility. For TensorFlow people, this will be a quick intro on how to add WebRTC to your project. js ML framework, and showed with demo on how to perform the complete machine-learning workflow, including the training, client-side deployment, and transfer learning. Some frameworks, such as Django, give you everything you. CNN (fp32, fp16) and Big LSTM job run batch sizes for the GPU's. Here's a link to TensorFlow. js! Here is the GitHub repository for reference. js is to d3. js uses the graphics card (GPU) of the computer via the browser API WebGL. Languages don't have speed, they have only semantics. The comparison is with TensorFlow running a ResNet-50 and Big-LSTM benchmark. PyQt contains over 620 classes that cover graphical user interfaces, XML handling, network communication, SQL databases, Web browsing and other technologies available in Qt. Join us for this informative workshop where we’ll show performance benchmarks that illustrate Node. About •5+ Years of Experience in Analysis, Design, and Development of various stand-alone, Client-Server and Web Based Software applications using Python 3. js deploys Performance Security A YAML file with the training configuration and a python file with. Although some Python implementations, such as PyPy, are fine-tuned for performance, raw portable performance is not where Python shines. 'weightsManifest': A TensorFlow. The article will help us to understand the need for optimization and the various ways of doing it. Tensorboard seems like a great tool to compare performance of different models but I'm not sure how it helps measure the performance of my complete setup. We recently announced the open source release of nGraph™, a C++ library, compiler and runtime suite for running Deep Neural Networks on a variety of devices. This comparison on Keras vs TensorFlow vs PyTorch will provide you with a crisp knowledge about the top Deep Learning Frameworks and help you find out which one is suitable for you. Top Key Differences Between R Programming vs Python. Take Python; the libraries for ML (scikit-learn, NumPy, Theano, etc) provide more than just a wider support of ML algorithms than Node (which is important!); but also per. A typical workflow using TensorFlow Lite would consist of: Creating and training a Machine Learning model in Python using TensorFlow. Keras vs TensorFlow vs scikit-learn PyTorch vs TensorFlow vs scikit-learn H2O vs TensorFlow vs scikit-learn Keras vs PyTorch vs TensorFlow Swift AI vs TensorFlow Trending Comparisons Django vs Laravel vs Node. Python is a tool to deploy and implement machine learning at a large-scale. All but Torch support Python integration while only TensorFlow and mxnet support R directly. WebRTC people will need to get used to Python. pb --output_node_names output/Conv2D Then generated the. Install TensorFlow for Python 2. Python certification training by Credo Systemz is an instructor-led training conducted in Chennai premises. Which are relatively recent. The TensorFlow Playground is a web application which is written in d3. I did some research and from what i understand Lua is a scripting language very alike Javascript but i dont see JS being used in the AI branch even tho all the support like JQuery JS has. js, we will immediately see that Node is significantly faster. Motivation. - [Voiceover] In this course we'll be…using Keras with the TensorFlow backend. If you want to compare speed you must choose specific implementations to compare with each other. Build apps powered by the powerful Tensorflow JS library. Creating large-scale data products. Python is not the first choice one can think of when designing a real-time solution. See the complete profile on LinkedIn and discover Lukasz’s connections and jobs at similar companies. js! Here is the GitHub repository for reference. retrain -h Run the training. 7 and Python 3. WebRTC people will need to get used to Python. When comparing Python vs. 0 (compiled from scratch) and Keras 2. js is not a programming language like Python, but rather a runtime environment for JavaScript. PyCharm is a Python IDE that provides code completion, refactoring , and in-built support for running Jupyter notebooks. If there are no performance gain per iterations, the application bottleneck is in the input pipeline in reading and preprocess the data. python -m scripts. Written in Python, which is regarded as a really pleasant language to read and develop in TensorFlow is written in Python, with the parts that are crucial for performance implemented in C++. When you talk about Node. But all of the high-level abstractions and development is done in Python. TFlearn is a modular and transparent deep learning library built on top of Tensorflow. multi-platform python binaries and download any needed source files on the fly. js: Machine Learning in. This enables more reliable performance in any environment. TensorFlow is an open-source machine learning software built by Google to train neural networks. Changing the shape of a Tensor. Stay on top of important topics and build connections by joining Wolfram Community groups relevant to your interests. It's important to remember that Node. 5 on the same machine (Ubuntu 16. We’d like to tell you about a new TensorFlow feature called “AutoGraph”. Machine learning is the new buzz word all over the world across the industries. You will need to perform two operations to have a ready to use model. Note: A bugfix release, 2. It is also assumed that model weights can be accessed from relative paths described by the paths fields in weights manifest. js Bootstrap vs Foundation vs Material-UI Node. High Performance Computing is available today but so are new commercial versions of actual Quantum computers and Neuromorphic Spiking Neural Nets. PyQt is available in two editions: PyQt4 which will build against Qt 4. TensorFlow is an open source software library for numerical computation using data flow graphs. CNN (fp32, fp16) and Big LSTM job run batch sizes for the GPU's. Does Python Have a String “contains” Substring Method? How To Safely Create Nested Directories in Python; Using Python To Create a Slack Bot; Power-Up Your Pytho. It is an open source library for numerical computation and large scale machine learning. 1 Python Version: 3. Its recent surge in popularity does support the claim that TensorFlow is better at marketing itself than long-time players of the open-source market like Torch and Theano. Vue Router & Vuex) ☞ Complete Guide to TensorFlow for Deep Learning with Python ☞ Machine Learning with TensorFlow + Real-Life Business Case ☞ Machine Learning & Tensorflow - Google Cloud. Tensorflow is 3. Changing the shape of a Tensor. x and PyQt5 which will only build against 5. Horovod is pre-installed in the Conda environments for TensorFlow. PyTorch vs TensorFlow. Here is the list with May. mapPartitions converts each partition of the source RDD into multiple elements of the result (possibly none). Python, on the other hand, is an interpreted language and not as fast as Golang. So, here we are, ladies and gentlemen, it's fight night. Tensorflow defines a computational graph statically before a model can run. js, TensorFlow. I prefer TFLearn because it seems to offer slightly better performance than Keras. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Per-axis vs per-tensor. Google has released the latest update to it's ultra-popular library TensorFlow - version 1. Python, as such is a full fledged programming language and many organisations use it in their production systems. 7, although I'll be switching to 3 soon. This may be a good thing if you want to be able to switch frameworks at will, but I've never had to do that. Additionally, even with powerful GPU, I noticed significant discrepancies in measured performance, depending on whether the browser runs on my main laptop screen or on an external screen attached to the HDMI port. js is designed as a WebGL accelerated library, I expected the best performance on a powerful GPU [4]. Python provides high-level programming abstractions by directly directing traffic among the pieces and secure them together. Keras is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow. A typical workflow using TensorFlow Lite would consist of: Creating and training a Machine Learning model in Python using TensorFlow. If you want to compare speed you must choose specific implementations to compare with each other. We will go deep in understanding the differences between the two languages. executed at server side and generates dynamic page). PyTorch vs TensorFlow. How about tfjs-node and tfjs-node-gpu? The performance gap should be a lot smaller, but I don't know enough about how tensorflow works (eg how much is done in python/js) or how bindings in node and python work to predict the difference. Check out a basic "Hello, World" program here and a more traditional matrix example here. You can run the script using the python command. Finally, a word about Plotly. Keras vs TensorFlow vs scikit-learn PyTorch vs TensorFlow. This post is a continuation of the NVIDIA RTX GPU testing I've done with TensorFlow in; NVLINK on RTX 2080 TensorFlow and Peer-to-Peer Performance with Linux and NVIDIA RTX 2080 Ti vs 2080 vs 1080 Ti vs Titan V, TensorFlow Performance with CUDA 10. Keep up to date with release announcements and security updates by subscribing to [email protected] Installing TensorFlow. Horovod is pre-installed in the Conda environments for TensorFlow. This is a far more natural style of programming. Creating large-scale data products. Why should JavaScript have a better likelihood than any of the others?. For this purpose, today, we compare two major languages, Scala vs Python for data science and other uses to understand which of python vs Scala for spark is best option for learning. With almost two decades of business software development, Brad's led web teams, been a Linux sysadmin, and developed a storefront in Go. NET detailed comparison as of 2019 and their Pros/Cons When comparing TensorFlow vs Infer. What is PHP. js use an existing model that has wrappers that make it easy to pass an. TensorFlow Multi-GPU performance with 1-4 NVIDIA RTX and GTX GPU's. My Raspberry Pi was running Python 3. It interoperates seamlessly with TensorFlow, PyTorch, scikit-learn, Gensim and the rest of Python's awesome AI ecosystem. Jun 8, 2016. # Note: the following command will overwrite any existing TensorFlow # installation. python -m tensorflow. You lose a bit of performance because WebGL can only be tricked into executing the matrix multiplication required by TensorFlow. js, along with this recent announcement and the exciting performance gains of v1. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. I did some research and from what i understand Lua is a scripting language very alike Javascript but i dont see JS being used in the AI branch even tho all the support like JQuery JS has. Frameworks. js core, which implements several CNNs (Convolutional Neural Networks) to solve face detection, face recognition and face landmark detection, optimized for the web and for mobile devices. Written in Python, which is regarded as a really pleasant language to read and develop in TensorFlow is written in Python, with the parts that are crucial for performance implemented in C++. Ease of Learning. Note, however, that Keras does allow you to get access to the TensorFlow session. js is to d3. One of its applications is to develop deep neural networks. You can, for example, run Python on an alternative interpreter that is called PyPy. While looking at the TensorFlow installation in your system we will also learn how to install Tensorflow with Anaconda. Convert Pretrained Models to TensorFlow. In addition to. Your web. Computer Graphics. TensorFlow is also on this month’s trending repositories list but we should have got used to it being GitHub’s darling. Go: performance. 7, although I'll be switching to 3 soon. Advanced Python Tutorials. While Python is extremely strong in supporting human productivity, it still lacks in computational performance compared to 'traditional' HPC languages such as Fortran or C. It is designed to achieve high scalability and performance and is known to provide high-performance gains when compared to other toolkits like Theano and Tensorflow when running on multiple machines. TensorFlow best practice series. ReportPlus Vs. This is because Node. Awni Hannun, Stanford. I see similar performance differences for real networks where the matrix-multiplication overhead should dominate, still TensorFlow is significantly slower. The following is an example of using an user-defined function to be called multiple(∞) times in a script with ease. autograph is a related tool that increases the surface area of Python code that can be converted to a TensorFlow graph. The open-source Anaconda Distribution is the easiest way to perform Python/R data science and machine learning on Linux, Windows, and Mac OS X. Python, as such is a full fledged programming language and many organisations use it in their production systems. skflow alternatives and similar packages 10. I am having a problem with importing tensorflow GPU on spyder. Convert Pretrained Models to TensorFlow. While Python is a robust general-purpose programming language, its libraries targeted towards numerical computation will win out any day when it comes to large batch operations on arrays. Configuration-driven: no coding, see Kafka Connector Configuration to learn about available configuration settings. NGC is a GPU-Optimized software hub that simplifies high-performance computing, deep learning, and machine learning structure and workflows. The retrain script is from the TensorFlow Hub repo, but it is not installed as part of the pip package. With spaCy, you can easily construct linguistically sophisticated statistical models for a variety of NLP problems. With over 15 million users worldwide, it is the industry standard for developing, testing, and training on a single machine, enabling individual data scientists to:. Scalable and resilient architecture: review Kafka Connector Architecture to learn how Kafka Connector addresses performance, scalability, and fault tolerance requirements and get deeper understanding of the Kafka Connector internals. Install numpy+mkl before other packages that depend on it. JS vs Python: Professional Comparison and Overview Loops in Python - comparison, and performance Detect Toxic Language in Twilio Chat with Tensorflow. I am having a problem with importing tensorflow GPU on spyder. Tensorflow defines a computational graph statically before a model can run. Python TensorFlow Machine Learning Deep Learning Deployment and performance analysis of SSR. …So why are we going through the extra step of using Keras…instead of just using TensorFlow on its own. Why should JavaScript have a better likelihood than any of the others?. C++ offers speed and performance, the core of tensorflow is fully written in C++, so if you want to understand tensorflow library. In this post you will discover the. 5 which we covered at the time of the release here. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. js makes use of a small library called tfjs-vis. We will go deep in understanding the differences between the two languages.