MLOps with SageMaker, introduction to the YOLO family, multi GPU model training, neural 3D scene reconstruction with the manhattan-world assumption, learning to answer questions from millions of narrated videos, ConvMAE, ARTEMIS, EasyNLP, and more.
Kubeflow MLOps, a comprehensive list of strategies for feature selection, introducing PyScript, open pre-trained transformer language models, DeepNorm, NeurMiPs, Stanford CS224N NLP with Deep Learning, LAION-5B dataset, and more.
We are excited to get back to work, to revive Data Phoenix from the ashes of war. Today you will know about emerging architectures for modern data infrastructure, backpropagation in RNN, masked generative image transformer, scalable large scene neural view synthesis, and more.
Calculating derivatives in PyTorch, automate your ML development pipeline with PyCaret, a video restoration transformer, adapting CV models for time series and automated EEG review, DALL-Eval, EvoJAX, and more ...
![Data Science and Machine Learning from Scratch [And More] Post feature image](/content/images/size/w1200/format/webp/2022/02/data_phoenix_articles_4-min.png)
We believe that any data scientist and ML professional should never stop learning. In this post, we provide an overview of the best resources and courses you can use to start digging in.
Kubeflow pipeline from scratch, financial text classification using FinBERT, a single model for many visual modalities, a gentle introduction to supervised learning, GAN-based facial editing of real videos, NN-SVG, GreaseLM, Data2vec, videos, and more ...
