Applications of Machine Learning methods have significantly contributed to the development of biomedical research and drug discovery. In this talk Nazim Medzhidov and Romeo Cozac focus on 1) understanding the types of biological/chemical data available and challenges in biomedical research, 2) providing examples on how ML approaches using biological/chemical data are used to address complex … Continue reading MACHINE LEARNING IN LIFE SCIENCE RESEARCH & DRUG DISCOVERY
AI, Machine Learning and Deep Learning this month, at a glance.
The most important happenings in Machine Learning, Deep Learning and AI this month, at a glance.
We are excited to announce our Fastbook Sessions with Sanyam Bhutani.This will be a series of 2-hour weekly study sessions dedicated to going through the book, "Deep Learning for Coders with fastai and PyTorch: AI applications Without a PhD" written by the creators of the fast.ai course. The authors of the course were kind enough … Continue reading THE FASTBOOK SESSIONS
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