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Compare deep learning frameworks - IBM Developer
Review Classification using Active Learning
Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition: Kapoor, Amita, Gulli, Antonio, Pal, Sujit, Chollet, Francois: 9781803232911: Amazon.com: Books
Frontiers | A Deep Active Learning Approach to the Automatic Classification of Volcano-Seismic Events
Active learning: deduplication example | by Roman Kazinnik | Medium
GitHub - google/active-learning
Active Learning on MNIST — Saving on Labeling | by Andy Bosyi | Towards Data Science
Introducing Keras 2
Review Classification using Active Learning
Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python: Moolayil, Jojo: 9781484242391: Amazon.com: Books
Active Learning Tensorflow Implementation | by Moklesur Rahman | Medium
AI Starter-Everything you need to know about Keras to build your first deep learning model | by Pallawi | Medium
Ppt model & strategi pembelajaran active learning membaca keras keras
Master Keras for Deep Learning Projects - A Beginner's Guide
Deep active learning for constitutive modelling of granular materials: From representative volume elements to implicit finite element modelling - ScienceDirect
Comprehensive introduction to active learning with Tensorflow and Keras - Show and Tell - TensorFlow Forum
Keras Tutorial: Deep Learning in Python | DataCamp
Active Learning and Human Involvement in Machine Learning | by Darshan Deshpande | Analytics Vidhya | Medium
cleanlab 2.3 adds support for Active Learning, Tensorflow/Keras models made sklearn-compatible, and highly scalable Label Error Detection
PyRelationAL: A Library for Active Learning Research and Development | Semantic Scholar
Deep Bayesian Active Learning on MNIST - Damien Lancry
Using Active Learning to Improve your Machine Learning Models – CV-Tricks.com
Active Learning and Semi-supervised Learning turn your unlabeled… – Towards AI
cleanlab 2.3 adds support for Active Learning, Tensorflow/Keras models made sklearn-compatible, and highly scalable Label Error Detection