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This article investigates the design methods used in distributed machine learning platforms and proposes future research directions. I collaborated with my students Kuo Zhang and Salem Alqahtani to complete this in the fall of 2016, and it will be presented at the ICCCN’17 conference in Vancouver. Machine learning (especially deep learning) has now achieved transformative … Read more
Source: Authorized by AI Technology Camp (ID: rgznai100) This article is approximately 3000 words long and is recommended to be read in 7 minutes. This article will introduce you to a comprehensive manual detailing TensorFlow 2.0. [ Guide ]After the official release of TensorFlow 2.0, it immediately received widespread attention and praise from the academic … Read more
This article was first published on WeChat public account: “The Beauty of Algorithms and Programming”, welcome to follow for more timely updates on this series of articles. Problem Description Here is an example of recognizing the mnist handwritten dataset. This dataset is a classic dataset in machine learning, consisting of 60k training samples and 10k … Read more
Deep learning frameworks have played a crucial role in the rapid development in recent years, among which PyTorch and TensorFlow are the two most popular frameworks. They each have unique features and advantages, but there are also some similarities. This article will delve into PyTorch and TensorFlow, providing a detailed introduction from principles, code implementation, … Read more
https://iamarookie.blog.csdn.net/article/details/117755839 Basic Operations in TensorFlow2 – Part 3 Merge and Split tf.concat tf.concat helps us perform concatenation operations. Format: tf.concat( values, axis, name='concat' ) Parameters: values: a tensor or tensor list – axis: the dimension to operate on – name: the name of the operation, defaults to “concat” Example: part_1 = tf.zeros([5, 3]) print(part_1) part_2 … Read more
Original content from Machinery Industry Education, please share to your circle of friends. Unauthorized reproduction by other public accounts is prohibited. TensorFlow Deep Learning Tutorial Click on the cover View details “TensorFlow Deep Learning Tutorial” (ISBN: 978-7-111-70365-5), Chief Editors: Ping Zhenyu and Kuang Liang,This book comes with micro-course videos, electronic courseware, source code, course standards, … Read more
Source: Deephub Imba This article is approximately 2100 words long and is suggested to be read in 9 minutes. This article will introduce two methods to apply augmentation to datasets in TensorFlow. For image-related tasks, common data augmentation methods include rotating, blurring, or resizing images. This is because the inherent properties of images make data … Read more
Author: Arjun Sarkar Translator: Chen Zhiyan Proofreader: Ouyang Jin This article is about 1900 words, and is recommended for an 8minute read. This article will teach you how to write custom loss functions using wrapper functions and OOP in Python. Tags: TensorFlow 2, Loss Function Figure 1: Gradient Descent Algorithm (Source: Public Domain, https://commons.wikimedia.org/w/index.php?curid=521422) Neural … Read more
Click the above “Mechanical and Electronic Engineering Technology” to follow us The MNIST (Modified National Institute of Standards and Technology) dataset is a classic dataset for handwritten digit recognition, widely used in the fields of machine learning and deep learning. It was created by the National Institute of Standards and Technology (NIST) in the United … Read more
LearnExcelTechnology, Follow WeChat Official Account: excelperfect Introduction: Here I record the issues encountered while installing TensorFlow and their solutions, so that I can refer back to them later and for the reference of friends facing similar problems. Recently, I have been focusing on studying Deep Learning Illustrated. When I reached Chapter 8, I tried to … Read more