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59 Stories To Learn About Tensorflow

by Learn RepoJanuary 11th, 2024

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Learn everything you need to know about Tensorflow via these 59 free HackerNoon stories.

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Let's learn about Tensorflow via these 59 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology.

1. Quantum Machine Learning Using TensorFlow Quantum

INTRODUCTION

2. What Does Facebook Hydra Mean For The Future of Python

Ever since its inception in the year 1089 by Guido Van Rossum, the programming language Python has along far away. Sheldon did its creator knew that Python would in today's world be utilized for a variety of purposes such as research, development, scripting, among many others. Built as a successor in the ABC language, Python does not just find its applications in software development but also in research.

3. YouTube's Recommendation Engine: Explained

Every successful tech product, by the very definition, is a result of some technological marvels working with impeccable user experience to solve a key problem for the users. One such marvel is the recommendation engine by YouTube.

4. NSFW Filter Introduction: Building a Safer Internet Using AI

Filtering out NSFW images with a web extension built using TensorFlow JS.

5. Why ML in Production is (still) Broken and Ways we Can Fix it

Machine Learning, Deep Learning development in production was still broken. ZenML, an extensible, open-source MLOps framework for production-ready ML pipelines.

6. Deploy First TensorFlow Model in Android App

Simple linear regression is useful for finding the relationship between two continuous variables. One is a predictor or independent variable and the other is a response or dependent variable. It looks for a statistical relationship but not a deterministic relationship. Relationship between two variables is said to be deterministic if one variable can be accurately expressed by the other. For example, using temperature in degrees Celsius it is possible to accurately predict Fahrenheit.

7. How to Run Machine-Learning Models in the Browser using ONNX

Learn how to use ONNX Runtime Web to deploy machine-learning models natively to the browser.

8. Training Your Models on Cloud TPUs in 4 Easy Steps on Google Colab

You have a plain old TensorFlow model that’s too computationally expensive to train on your standard-issue work laptop. I get it. I’ve been there too, and if I’m being honest, seeing my laptop crash twice in a row after trying to train a model on it is painful to watch.

9. 10 Must-Try Open Source Tools for Machine Learning

Machine learning is the future. But will machines ever extinct humans?

10. Efficient Implementation of MobileNet and YOLO Object Detection Algorithms for Image Annotation

The objective of the problem is to implement classification and localization algorithms to achieve high object classification and labelling accuracies, and train models readily with as least data and time as possible. The solution to the problem is considered in the following blog.

11. Cocktail Alchemy: Creating New Recipes With Transformers

Build a transformer model with natural language processing to create new cocktail recipes from a cocktail database.

12. Auto-Generating Lyrics With TensorFlow and Machine Learning: A How-To Guide

Creating a bot that, given a starting phrase, would generate its own lyrics, powered by a machine learning model that would have learned from existing songs.

13. How I Transfer an Artistic Style to Any Image

Machine learning and artificial intelligence have been on my radar for years now, but more as a concept and “thing I should know about.” I didn’t feel that I had the free time or skills to dig into it. However, my attitude about machine learning has changed in the past few months. I have seen new and easier tools become accessible to the public. In this post I will walk you through how to transfer an art style to any image using some of these tools.

14. Style Transferring with TensorFlow

Style transfer is a computer vision-based technique combined with image processing. Learn about style transfer with Tensorflow, a prominent framework in AI & ML

15. Time Series Forecasting with TensorFlow.js

Pull stock prices from online API and perform predictions using Recurrent Neural Network & Long Short Term Memory (LSTM) with TensorFlow.js framework

16. Manipulación de tensores en PyTorch. ¡El primer paso para el deep learning!

*Nota: Contactar a Omar Espejel ([email protected]) para cualquier observación. Cualquier error es responsabilidad del autor.

17. TLDR Newsletter Week of August 5th Highlights

18. Flax: Google’s Open Source Approach To Flexibility In Machine Learning

Thinking of Machine Learning, the first frameworks that come to mind are Tensorflow and PyTorch, which are currently the state-of-the-art frameworks if you want to work with Deep Neural Networks. Technology is changing rapidly and more flexibility is needed, so Google researchers are developing a new high performance framework for the open source community: Flax.

19. Top 3 Face Datasets and How to Work with Them

An image dataset contains specially selected digital images intended to help train, test, and evaluate an artificial intelligence (AI) or machine learning (ML)

20. PyTorch vs TensorFlow: Who has More Pre-trained Deep Learning Models?

Given the importance of pre-trained Deep Learning models, which Deep Learning framework - PyTorch or TensorFlow - has more of these models available to users is

21. Rare Datasets for Computer Vision Every Machine Learning Expert Must Work With

Have you ever being in a situation to guess another person’s age? Well May be YES!! How about playing games like finding things in minimum time? or about finding the written character where your doctor wrote in the prescription when you are sick?

22. How I Built an AI to Detect License Plate Number Registration (ANPR)

This Car Mod Is A Privacy Nightmare! (AI Number Plate Reader with Python, Tensorflow, OpenCV, OpenALPR)

23. How to Fine Tune a 🤗 (Hugging Face) Transformer Model

How to fine-tune a Hugging Face Transformer model for Sequence Classification

24. Top 10 Data Science Libraries in Python

Data Science Libraries that will shine this year.

25. What Neural Networks Teach Us About Schizophrenia

Pretrained Artificial Neural Networks used to work like a Blackbox: You hand them an input and they predict an output with a certain probability — but without us knowing the internal processes of how they came up with their prediction. A Neural Network to recognize images usually consists of around 20 neuron layers, trained with millions of images to tweak the network parameters to give high quality classifications.

26. Classify Handwritten Digits using Deep learning with Tensorflow

Deep learning is a subpart of machine learning and artificial intelligence which is also known as deep neural network this networks capable of learning unsupervised from provided data which is unorganized or unlabeled. today, we will implement a neural network in 6 easy steps using TensorFlow to classify handwritten digits.

27. Artificial Intelligence Vs Machine Learning: What's the difference?

AI and Machine Learning are predominant terms that are creating a lot of buzz in the technology world. The terms can often be used interchangeably but that’s not the case, AI and ML are way more different from each other in their approach, algorithms and logical thinking.

28. How To Build Links Detector That Making Links in Your Book Clickable

How I built a link detector for your smart phone to browse links printed in books.

29. Computer Vision Could Improve Health and Workplace Safety

Recent developments in the field of training Neural Networks (Deep Learning) and advanced algorithm training platforms like Google’s TensorFlow and hardware accelerators from Intel (OpenVino), Nvidia (TensorRT) etc., have empowered developers to train and optimize complex Neural Networks in small edge devices like Smart Phones or Single Board Computers.

30. From TF to TFLite: Deploying ML Models on Mobile [Part 2]

This is part 2 of the two-part article on deploying ML models on mobile. We saw how to convert our ML models to TfLite format here. For those of you who came here first, I recommend you click on the above link to get the whole picture. If you just want the android part ,the demo app we are building has a GAN model generating handwritten digits and a classifier model predicting the generated digit.

31. From TF to TFLite: Deploying ML Models on Mobile [Part 1]

tl;dr - Link to code: TensorFlow GAN model.

So the other day I was talking to my rubber ducky about how G-Board predicts my next word, even when those words are entirely made up by me, in that how it actually learns on-device. How amazingly Netflix, Amazon, Google Maps make use of machine learning in their apps. How does machine learning on apps even work? Does the model learn even after being deployed? Can I deploy a GAN model on mobile?

32. How to Use TensorFlow in Python: Google‘s Open-Source Library For Deep Learning

You might not always know it, but Deep Learning is everywhere. We explain how to use TensorFlow, Google's Library For Deep Learning, in Python.

33. Build Your Own Voice Recognition Model with Tensorflow

While I'm usually a JavaScript person, there are plenty of things that Python makes easier to do. Doing voice recognition with machine learning is one of those.

34. C++ to WebAssembly using Bazel and Emscripten

How to get Bazel and Emscripten to compile C++ to WebAssembly or JavaScript

35. Approach Pre-Trained Deep Learning Models With Caution

Pre-trained models are easy to use, but are you glossing over details that could impact your model performance?

36. Tensorflow Vs. Keras: Comparison by building a model for image classification

Yes, as the title says, it has been very usual talk among data-scientists (even you!) where a few say, TensorFlow is better and some say Keras is way good! Let’s see how this thing actually works out in practice in the case of image classification.

37. Human Detection System Using RaspberryPi, Thermal Camera and Machine Learning

Triggering reliable events based on the presence of people has been the dream of many geeks and DIY automators for a while. Having your house to turn the lights on or off when you enter or exit your living room is an interesting application, for instance. Most of the solutions out there to solve these kinds of problems, even more high-end solutions like the Philips Hue sensors, detect motion, not actual people presence — which means that the lights will switch off once you lay on your couch like a sloth.

38. How Blockchain & AI Integration is Changing Business Landscape?

The potential of Blockchain is no lesser than Artificial intelligence. If you have taken a look at them, you must already know the impacts of these technologies on various industries.

39. Lessons for Improving Training Performance — Part 1

Part 1: Lower precision & larger batch size are standard now

40. [Hacking Tinder] Train an AI to Auto-Swipe for You 🖖

Auto-tinder was created to train an AI using Tensorflow and Python3 that learns your interests in the other sex and automatically plays the tinder swiping-game for you.

41. Fabio Manganiello on Home-Made Computer Vision, IoT, Automation, AI

Fabio Manganiello writes about solutions he's discovered while building a platform, library of plugins and an API to connect/manage any device and service through any backend, allowing users to easily set up any kind of automation. Fabio is based in Amsterdam, the Netherlands, and has been nominated for a 2020 #Noonie for exceptional contributions to the IoT tag category on Hacker Noon.

42. Efficient Model Training in the Cloud with Kubernetes, TensorFlow, and Alluxio Open Source

This article presents the collaboration of Alibaba, Alluxio, and Nanjing University in tackling the problem of Deep Learning model training in the cloud. Various performance bottlenecks are analyzed with detailed optimizations of each component in the architecture. This content was previously published on Alluxio's Engineering Blog, featuring Alibaba Cloud Container Service Team's case study (White Paper here). Our goal was to reduce the cost and complexity of data access for Deep Learning training in a hybrid environment, which resulted in over 40% reduction in training time and cost.

43. The Full Story behind Convolutional Neural Networks and the Math Behind it

Convolutional Neural Networks became really popular after 2010 because they outperformed any other network architecture on visual data, but the concept behind CNN is not new. In fact, it is very much inspired by the human visual system. In this article, I aim to explain in very details how researchers came up with the idea of CNN, how they are structured, how the math behind them works and what techniques are applied to improve their performance.

[44. Differential Privacy with Tensorflow 2.0 : Multi class Text Classification

Privacy](//gzht888.com/differential-privacy-with-tensorflow-20-multi-class-text-classification-privacy-yk7a37uh) Introduction

45. What Is Edge AI?

Edge AI—also referred to as on-device AI—commonly refers to the components required to run an AI algorithm locally on a hardware device.

46. Mueller Report for Nerds! Spark meets NLP with TensorFlow and BERT (Part 1)

Photo by Michael on Unsplash

47. [Tutorial] Build a Gender Classifier for Live Webcam Stream using Tensorflow and OpenCV

Training a Neural Network from scratch suffers two main problems. First, a very large, classified input dataset is needed so that the Neural Network can learn the different features it needs for the classification.

48. Objects Classification Using CNN-based Model

— All the images (plots) are generated and modified by the Author.

49. Can I Grade Loans Better Than LendingClub?

In case you missed it, I built a neural network to predict loan risk using a public dataset from LendingClub. Then I built a public API to serve the model’s predictions. That’s nice and all, but… how good is my model?

50. C++ to WebAssembly using Bazel and Emscripten

How to get Bazel and Emscripten to compile C++ to WebAssembly or JavaScript

51. Training Your Own Text Classification Model From Scratch With Tensorflow Is As Easy As ABC

Hello ML Newb! In this article, you will learn to train your own text classification model from scratch using Tensorflow in just a few lines of code.

52. RethNet Model: Object-by-Object Learning for Detecting Facial Skin Problems

In August 2019, a group of researchers from lululab Inc propose the state-of-the-art concept using a semantic segmentation method to detect the most common facial skin problems accurately. The work is accepted to ICCV 2019 Workshop.

53. Uber AI Labs Senior Research Scientist Talks TensorFlow 2.0 [Interview]

There’s no doubt that TensorFlow is one of the most popular machine learning libraries right now. However, newbie developers who want to experiment with TensorFlow often face difficulties in learning TensorFlow; the framework has a not unjustified reputation for having a steep learning curve that can make it hard for developers to get to grips with quickly.

54. Training Machine Learning Models Using TensorFlow or PyTorch

I will show you how gradient descent works, which is in the deepest deep of machine learning.

55. Building Machine Learning Models With TensorFlow

In this article, I will share with you some useful tips and guidelines that you can use to better build better deep learning models.

56. Build A Smart Baby Monitor Using a RaspberryPi and Tensorflow

Some of you may have noticed that it’s been a while since my last article, despite winning this year's IoT Noonies award (btw thanks to all of you who voted, that means a lot to me!).

57. #Switch2Swift for Deep Learning

If you are interested, what the recent fast.ai advanced and closed Deep Learning Class had to say about Google’s Swift for Tensorflow project, you might find this post interesting. Even if you attended the class, you should find here hopefully a good overview (with links into the class, presentations, and additional material), what Swift for Tensorflow is and why it might be relevant.