This is part of the CNN Architectures series by Dimitris Katsios. Find all CNN Architectures online: Notebooks: MLT GitHubVideo tutorials: YouTubeSupport MLT on Patreon DenseNet We will use the tensorflow.keras Functional API to build DenseNet from the original paper: “Densely Connected Convolutional Networks” by Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger. In the paper we can read: [i] “Note that … Continue reading CNN ARCHITECTURES: DENSENET
This is part of the CNN Architectures series by Dimitris Katsios. Find all CNN Architectures online: Notebooks: MLT GitHubVideo tutorials: YouTubeSupport MLT on Patreon SqueezeNet We will use the tensorflow.keras Functional API to build SqueezeNet from the original paper: “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size” by Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, … Continue reading CNN ARCHITECTURES: SQUEEZENET
Extract the most important information from a CNN paper and learn how to code up the architecture. This time: "Xception: Deep Learning with Depthwise Separable Convolutions"
By Alisher Abdulkhaev and Suzana Ilić Issue #9: March 2020 Keras turned 5Facebook releases SynSinTuring Award 2019 winners: Patrick M. Hanrahan and Edwin E. CatmullNeRF: Representing Scenes as Neural Radiance Fields for View SynthesisStanford NLP Group releases StanzaGoogle announced TensorFlow Developer CertificateICLR 2020 as a fully virtual conference due to the coronavirus spreadingGoogle announced TensorFlow Quantum: … Continue reading AI DIGEST #MARCH20
What's the most important implementation information in a Deep Learning paper and how do we code it up? MLT Director Dimitris Katsios shows you exactly that with a series on CNN Architectures including notebooks, visualizations and videos. To kick off the series, Dimitris picked some of the earliest Convolutional Neural Network papers. This series will … Continue reading CNN ARCHITECTURES (1-5)
Amidst the coronavirus pandemic we want to share some wonderful news. We are extremely happy that Machine Learning Tokyo doubled in size in this past year from 2,400 members in March 2019 to more than 5,000 members in March 2020. Going online and opening up to the global AI community made MLT more diverse, bigger … Continue reading 5,000 MLT MEMBERS