Joel Grus - Software Engineer, Data Scientist & Best-Selling Author. Available for consulting on data science, machine learning, and software engineering projects.| joelgrus.com
This is just a fun experiment to answer the question: how can I share a memory-mapped tensor from PyTorch to Numpy, Jax and TensorFlow in The post Memory-mapped CPU tensor between Torch, Numpy, Jax and TensorFlow first appeared on Terra Incognita.| Terra Incognita
Tensorflow is a machine learning library that lets you create all kinds of neural networks....| DEV Community
Training computer vision (CV) or natural language processing (NLP) models can be expensive and requires large datasets. If labeling is done manually, the process will take a longer training time and requires expensive hardware. For instance, the Generative Pre-trained Transformer 2 (GPT-2), a benchmark-setting language model created by Open AI| Machine learning nuggets
Recurrent Neural Networks (RNNs) are a class of neural networks that form associations between sequential data points. For example, the average sales made per month over a certain period. The data has a natural progression from month to month, meaning that the sales for the first month are the only| Machine learning nuggets
The Keras Functional API provides a way to build flexible and complex neural networks in TensorFlow. The Functional API is used to design networks that are not linear. In this article, you will discover that the Keras Functional API is used to create networks that: * Are non-linear. * Share layers. * Have| Machine learning nuggets
Building object detection and image segmentation models is slightly different from other models. Majorly because you have to use specialized models and prepare the data in a particular way. This article will examine how to perform object detection and image segmentation on a custom dataset using the TensorFlow 2 Object| Machine learning nuggets
Training models in Keras is usually done using the fit method. However, you may want more control over the training process. To do that, you'll need to create a custom training loop. This involves setting up a custom function to compute the loss and gradient. This article will walk you| Machine learning nuggets
In the Implementing Fully Convolutional Networks (FCNs) from scratch in Keras and TensorFlow article, you saw how to build an image segmentation model with FCNs. However, due to the model's limitations, it did not perform very well in the segmenting task. In this post, you will see how to improve| Machine learning nuggets
Building artificial neural networks with TensorFlow and Keras requires understanding some key concepts. After learning these concepts, you'll install TensorFlow and start designing neural networks. This article will cover the concepts you need to comprehend to build neural networks in TensorFlow and Keras. Without further ado, let's get the ball| Machine learning nuggets
In the artificial neural networks with TensorFlow article, we saw how to build deep learning models with TensorFlow and Keras. We covered various concepts that are foundational in training neural networks with TensorFlow. In that article, we used a Pandas DataFrame to build a classification model in Keras. This article| Machine learning nuggets
Musings on extensions to einsum notation for more readable machine learning code.| machine learning musings
IoT is a passion of mine for quite some time, so imagine how happy I was to receive a gift from Semix, the all new ESP-EYE v2.1!| Dror Gluska
Enabling the use of the GPU on your Mac M1 with the tensorflow-metal plugin can be challenging because there is a lot of conflicting documentation and older forum questions and replies. I’ve written this article for a Mac M1 running on macOS Sequoia 15.1.1. As of December 2024, you should pair Python 3.11 with TensorFlow ... Mac: tensorflow-metal pip module on M1 chip for GPU support| Fabian Lee : Software Engineer
Transfer learning is used in machine learning and is a method in which already-trained or pre-trained neural networks are present and these pre-trained neural| GeekPython - Python Programming Tutorials
Read about the evolution of Catwalk, Grab's model serving platform, from its inception to its current state. Discover how it has evolved to meet the needs of Grab's growing machine learning model serving requirements.| Grab Tech
Hello everybody, I hope you’ve been enjoying this summer after two years of Covid and lockdowns :D In this post I’m going to describe how| evilsocket
In this post we’ll talk about two topics I love and that have been central elements of my (private) research for the last ~7 years: machi| evilsocket
It’s been a while that i’ve been quite intensively playing with Deep Learning both for work related research and personal projects. More specifically, I’ve been using the Keras framework on top of a TensorFlow backend for all sorts of stuff. From big and complex projects for malware detection, to smaller and simpler experiments about ideas i just wanted to quickly implement and test - it didn’t really matter the scope of the project, I always found myself struggling with the same issu...| evilsocket
Learn how to get started with TensorFlow on various systems. From basic setup to advanced options, start your deep learning journey with PyImageSearch today!| PyImageSearch
Announcing the release of TensorFlow GNN 1.0, a production-tested library for building GNNs at Google scale, supporting both modeling and training.| blog.tensorflow.org
Dans cet article, nous allons voir ce qu’est la quantization et comment l’utiliser avec TensorFlow pour améliorer et accélérer tes modèles. Depuis la sortie de ChatGPT, des chercheurs s’attellent à diminuer l’espace de stockage utilisé par des réseaux de neurones – de plus en plus grand. De nombreux techniques d’optimisation ont été évoquées et expérimentées […] L’article Qu’est-ce que la Quantization et Comment l’Utiliser avec TensorFlow est apparu en premier...| Inside Machine Learning
Dans cet article, je te montre comment utiliser une Random Seed avec TensorFlow pour obtenir des résultats reproductibles avec ton modèle. À l’inverse du Machine Learning traditionnel, l’entraînement d’un modèle de Deep Learning est un processus hautement complexe. Dans ce processus est utilisé l’aléatoire, entre autres, pour initialiser les poids des neurones. Cette aspect aléatoire […] L’article Random Seed TensorFlow – Comment obtenir des résultats stables avec un mo...| Inside Machine Learning
Cet article est un guide à l’attention des débutants en Intelligence Artificielle qui souhaite commencer à apprendre le Deep Learning. Comment ne pas avoir entendu parler de l’Intelligence Artificielle à notre époque? De ChatGPT, en passant par AlphaFold et les voitures autonomes, pour arriver à Midjourney, il est aujourd’hui clair que l’IA n’est plus de […] L’article Commencer le Deep Learning en 2024 – Meilleur Guide Simple est apparu en premier sur Inside Machine Lear...| Inside Machine Learning
With the plethora of open-source language models, it's incredibly difficult to determine if a piece of text is AI generated. However, with a good dataset, you can train a model in TensorFlow to detect if a large language model generated text. It's such an interesting problem that there is even| Machine learning nuggets
Last week, GKE announced GPU support for Autopilot. Here's a fun way to try it out: a TensorFlow-enabled Jupyter Notebook with GPU-acceleration! We can even add state, so you can save your work between sessions. Autopilot makes all this really, really easy, as you can configure everything as a Kuber| William Denniss
The recent wave of generative language models is the culmination of years of research starting with the seminal "Attention is All You Need" paper. The paper introduced the Transformer architecture that would later be used as the backbone for numerous language models. These text generation language models are autoregressive, meaning| Machine learning nuggets
BERT is a popular Masked Language Model. Some words are hidden from the model and trained to predict them. The model is bidirectional, meaning it has access to the words to the left and right, making it a good choice for tasks such as text classification. Training BERT can quickly| Machine learning nuggets
Training computer vision models with little data can lead to poor model performance. This problem can be solved by generating new data samples from the existing images. For example, you can create new images by flipping and rotating the existing ones. Generating new image samples from existing ones is known| Machine learning nuggets
Image generation models are causing a sensation worldwide, particularly the powerful Stable Diffusion technique. With Stable Diffusion, you can generate images with your laptop, which was previously impossible. Here's how diffusion models work in plain English: 1. Generating images involves two processes. Diffusion adds noise gradually to the image until| Machine learning nuggets
An autoencoder takes an input image and creates a low-dimensional representation, i.e., a latent vector. This vector is then used to reconstruct the original image. Regular autoencoders get an image as input and output the same image. However, Variational AutoEncoders (VAE) generate new images with the same distribution as| Machine learning nuggets
Training computer vision models requires a lot of time because of the size of the models and image data. Therefore, training these models can take prolonged periods of time, especially when training on a single GPU. You can reduce the training time by distributing the training across several GPUs. This| Machine learning nuggets
TensorBoard is a visualization library that enables data science practitioners to visualize various aspects of their machine learning modeling. For instance, you can use TensorBoard to: * Visualize the performance of the model. * Tuning model parameters. * Profile the executions of the program. For example, check the utilization of GPUs. * Debug machine| Machine learning nuggets
Build a smart camera that tracks your pets (and whatever else) using privacy-preserving, on-device ML.| daleonai.com
Learn how to Sentence Transformers model with TensorFlow and Keras for creating document embeddings| www.philschmid.de