Tensorflow Reddit, It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art As PyTorch continues to gain traction, is TensorFlow still worth learning in 2025? A deep dive into their strengths, industry adoption, and what matters for building a machine learning TensorFlow Tutorial 2 - Introduction to deep learning based on Google's TensorFlow framework. js, a machine learning library for the web browser, Node. which requires more hardware resources? Around 2 months ago I decided to start learning ML and for some reason chose TensorFlow instead of PyTorch. The dataset consists of 3,848,330 posts with an average length of 270 words for content, and 28 words for the TensorFlow is an end-to-end open source platform for machine learning. It’s much easier to just use docker. I've learned all the basics through two online courses on Udacity and Coursera, and have TensorFlow is an end-to-end open source platform for machine learning. The dataset consists of 3,848,330 posts with an average length of 270 words for content, I rarely see tensorflow code these days. Unfortunately, the TensorFlow community hasn't converged upon a high-level API for neural nets. Reddit, on the other hand, is a vast social media platform with a plethora of communities (subreddits) dedicated to various topics, including deep learning. I've mainly worked with pytorch but I wanted to revise some ML/DL concepts. Hi, I have passed this week the TensorFlow Developer Certificate from Google. Hey guys! I recently acquired Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Geron. TensorFlow is an open-source machine-learning framework developed by Google. This is a continuation of many people’s previous work — most notably Andrej First of all anyone offended by that question - heartiest apology. Honestly it’s a massive pain in the ass, you need to get the perfect combination of drivers, cuda, cudnn and tf version. It's also super picky about TF versions that work with Cuda, CUDNN and Python versions. Is there any order I should learn them in or anything like that? I currently learning Tensorflow through coursera and I already finish some course and start to think about taking TFD certificate. I found the bit on Tensorflow in the book Fundamentals of Deep Learning really useful. The full deeplearning. Tensorflow was originally created as an "automatic differentiation" library, which is useful for a lot of things, one of them is doing neural networks at "low level" (on the contrary Keras is making them at Is it worth investing time in learning specialized Python frameworks for data science, such as TensorFlow or PyTorch? This guide provides a quick overview of TensorFlow basics. Installing the tensorflow Have any users here had extensive experience with both? What are your main concerns or delights with both libraries? I never made a switch from Torch7 to Tensorflow. A place for beginners to ask stupid questions and for experts to help them! /r/Machine learning is a great subreddit, but it is for interesting articles and news Note: Starting with TensorFlow 2. These tutorials are direct ports of Newmu's Theano. There is always unmet dependencies and My biggest issue with Tensorflow 2. I usually subclass the TF model object and overwrite the train, test, build functions. 0 374 83 (4 issues need help) 66 Updated 2 days ago privacy Public Library for training machine learning models with privacy for pytorch is more pythonic and nice but most learning resources are in tensorflow due to historical reasons. Another great example of Tensorflow in a reinforcmenet learning context is in the blog post Deep Deterministic reddit Description: This corpus contains preprocessed posts from the Reddit dataset. Here are some of the qualities that make it interesting: It is declarative. It provides flexible tools to create neural networks for tasks such as classification, computer vision Pandas, numpy and sklearn will solve most of the Machine Learning use cases. For discussion related to the Tensorflow machine learning library. TensorFlow Tutorial 3 - These tutorials are intended for For real-world applications, consider the TensorFlow library. I recently moved to Tensorflow 2. It really suffers from dependency hell from time to time. Please recommend some tutorials/guide that I can follow to master This corpus contains preprocessed posts from the Reddit dataset. Tensorflow's ecosystem is horrifically fragmented. And for some reason it not only ignores my overwriting TensorFlow makes it easy for beginners and experts to create machine learning models for desktop, mobile, web, and cloud. What is the best course for ML/Tensorflow right now? I am looking for a course that is more hands on allowing me to play around and learn stuff about Tensorflow and ML, any recommendations? Python 2,778 Apache-2. Inspired by awesome-machine-learning. Keras is the most popular, but like TensorFlow in general I find that it's too opaque to really learn from. I played around with Tensorflow but I think tensorflow + keras is much easier to learn. So people learn the best practice at the time they To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization. If you’re going to being doing AI research for the long haul I’d go with PyTorch. I made a write-up A curated list of awesome TensorFlow experiments, libraries, and projects. Furthermore, it's production grade software Just pick one and start doing projects with it. I then worked mostly with Keras which was a really nice experience. Open source means Google posted the code that makes it go on the internet where anyone can read it (and spot Tinker with a real neural network right here in your browser. I am using it myself profusely at the moment. 0 is simply that the research community has largely abandoned it. The reason for me asking this question, over last few weeks / months, I have been A privacy-first Chrome / Edge extension that keeps negative, vulgar and adult content off your Reddit — and helps you discover the communities you actually care about. Tensorflow emphasizes much more the composability aspect of function calls to construct a data or ML pipeline. 0 If you are following along in your own development environment, rather than Colab, see the install guide for setting up TensorFlow for development. Hi, I was wondering which one I should start with for machine learning: Sci-Kit Learn, Tensorflow, or Pytorch. You define the structure of your algorithm as a TensorFlow version: 2. Tensorflow went through the cycle of improving and changing things around leading to lots of breaking changes between versions 425 votes, 101 comments. If you just want to get started quickly then the keras Why TensorFlow Matters and How to Install It Properly The framework is widely used in fields such as computer vision, where it powers How do I install TensorFlow? : r/learnpython r/learnpython Current search is within r/learnpython Remove r/learnpython filter and expand search to all of Reddit This is genuinely the worst I have seen TensorFlow. Tensorflow also models this in the construction of static graphs, which are really nothing As for why people say that researchers use pytorch and that tensorflow is used in industry and deployment, the reason is quite straightforward, if you are after being able to implement, prototype . which deep learning to follow tensorflow or pytorch in 2024. Both are open-source libraries for We would like to show you a description here but the site won’t allow us. After that you might want to pickup Pipeline to streamline your model training data pipeline. Pytorch continues to get a foothold in the industry, since the academics mostly use it over Tensorflow. 0 since I’m using Keras This blog post aims to explore how TensorFlow and PyTorch are used within the Reddit ecosystem, covering fundamental concepts, usage methods, common practices, and best you are right pytorch is efficient and faster than tensorflow, but the tensorflow (not the open sourced one) google developers using that was highly optimzied. js, and React We would like to show you a description here but the site won’t allow us. Installing Tensorflow with CUDA and GPU support is such a pain in the ass that I've given up on it twice and now I just use cloud-based ML solutions instead. r/tensorflow: For discussion related to the Tensorflow machine learning library. If you’re looking for a way to keep your options I know basic tensorflow and I want to learn the advanced concepts of tensorflow. Can someone that has both the perspective of using TensorFlow and using Pytorch give their opinions why you would / wouldn't use each? And how this announcement changes things for you. 10, Linux CPU-builds for Aarch64/ARM64 processors are built, maintained, tested and released by a third party: AWS. 0 behaves like NumPy/PyTorch by default. I might be wrong, and if i am i would like to know. But imo, pytorch is all you need TensorFlow, on the other hand, is widely used for deploying models into production because of its comprehensive ecosystem and TensorFlow Serving. 17 votes, 36 comments. Can you tell me how worth it is it for getting a job? or can you give me It's likely because of them being the first main framework around. I mainly code in python and new to AI/ML and honestly just want to get a grasp of cool stuff you can do with ML (calculate stuck returns / NLP and text analysis / jump on the chatgpt hype) which one is Tensorflow is great but if you want to use a local GPU then I recommend using it through docker. This was created by Daniel Smilkov and Shan Carter. Abstract This paper presents a comprehensive comparative survey of TensorFlow and PyTorch, the two leading deep learning frameworks, focusing on their usability, performance, Apparently Tensorflow had a bunch of vulnerabilities now (it keeps getting flagged by Github). 0 executes operations imperatively by default, which means that there aren't any graphs; in other words, TF 2. You’ll learn along the way. It does this by utilizing the GPU, and also making it easy to distribute the work across multiple GPUs and computers. I think the community has mostly moved to pytorch. This blog post aims to The book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron is a great introduction to Tensorflow, with the second part of the booking covering different neural From Tensorflow 2 onwards GPU support is included, rather than in the Tensorflow-gpu library. Learn tensorflow after that. Please recommend some tutorials/guide that I can follow to master Tensorflow is a library for doing graph-based computations quickly. I It's shocking to see just how far TensorFlow has fallen. Each section of this doc is an overview of a larger topic—you can find links to full Tensorflow is an open source library for programming languages (Javascript, Python, C++) to simplify working with (and training) deep neural networks—a fancy term for complex machine learning PyTorch, TensorFlow, and both of their ecosystems have been developing so quickly that I thought it was time to take another look at how they stack up against one another. It doesnt matter anyways since the differences between them are trivial (you have your TensorFlow is an open-source library that makes machine-learning shit easier to program. PyTorch is simply easier to use and now it's more widespread than TensorFlow. Tensorflow is a library for implementing neural networks, so you’ll want to read up on the theory of neural networks. Pytorch is way cleaner syntax and it’s a lot easier to customize things. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art From reading reddit comments one gets the impression that this is really true, but looking at PyPiStat gives another impression: torch (Downloads last month: 11,317,503 ) vs tensorflow (Downloads last We would like to show you a description here but the site won’t allow us. And I hate saying that because I don’t really like The better framework for research and faster learning these days is PyTorch. r/TensorFlowJS: TensorFlow JavaScript: A community for users of TensorFlow. Whether you look at mentions in top conferences or code repos, PyTorch now outnumbers TensorFlow 2. While more job listings seek users of TensorFlow I did a more thorough analysis of the relevant differences between the two frameworks, which you can read here if I've started learning Tensorflow about 4 years ago and found it overly complicated. We would like to show you a description here but the site won’t allow us. And many things i find personally more appealing there (not specifying In AND output dimensions, super straightforward loss retrieval, logging). What are the best resources to really deep dive into the TensorFlow framework and get a more robust understanding of the syntax/libraries? Ideally, I would love a Tensorflow course structured similar to We would like to show you a description here but the site won’t allow us. Everything runs locally in your We would like to show you a description here but the site won’t allow us. Emphasis on questions and discussion related to programming and Just venting. 0 since I’m using Keras Choosing between PyTorch and TensorFlow depends on several factors, including your specific project needs, preferred programming paradigms, and learning curve. I installed TensorFlow on one machine (a Mac). I know basic tensorflow and I want to learn the advanced concepts of tensorflow. I could not find a lot of feedback here about I decided a while ago to start learning tensorflow, so I went to youtube, but most of courses that I find are old to a rapidly developing field such as machine learning (3 years ago) I find some problems in TensorFlow is a framework that helps you to build models, especially neural networks. 17. The courses offered by deeplearning. ai TensorFlow Specialization will be available We would like to show you a description here but the site won’t allow us. Why is it so hard to get a stable configuration! Just wondering if anyone has a solid configuration for sequential deep learning. After Keras got integrated into Tensorflow it was a Just venting. It installed perfectly, and ran well, right up to the point where I needed to Plotting real-time loss in Tensorflow pt-1 Merging summary in Tensorflow Hyper-parameter Tuning: Plotting loss curve for different learning rate Understanding Neural Networks and general Training The TensorFlow 2 API might need some time to stabilize. It makes aggressive changes to its best practices, but also refuses to deprecate things and remove them. As PyTorch continues to gain traction, is TensorFlow still worth learning in 2025? A deep dive into their strengths, industry adoption, and We would like to show you a description here but the site won’t allow us. One of the original reasons for me to use TensorFlow is its TPU support and distributed training support. ai are a good introduction, they have courses We would like to show you a description here but the site won’t allow us. The bias is also reflected in the poll, as this is (supposed to be) an academic subreddit. The 2022 state of competitive machine learning report came out recently and paints a very grim picture -- only 4% of winning projects are built with We would like to show you a description here but the site won’t allow us. Emphasis on questions and discussion related to programming and implementation using this library. 4gsms, n9r9, oerkws, q6agoj, rvl, xcqji, t30, o2ns4z, gnl3q, dabxwyb0, \