* Added initial notes concerning the role of floating point precision

in deep learning applications.

deep-learning/20231115.FP-precision-notes.md

@@ -0,0 +1,28 @@
+Notes on Floating Point Precisions in Deep Learning Computations
+================================================================
+
+ECCV 2020 Tutorial on Accelerating Computer Vision with Mixed Precision
+-----------------------------------------------------------------------
+
+https://nvlabs.github.io/eccv2020-mixed-precision-tutorial/
+
+Topics of the tutorial:
+
+  * Training Neural Networks with Tensor Cores
+  * PyTorch Performance Tuning Guide
+  * Mixed Precision Training for Conditional GANs
+  * Mixed Precision Training for FAZE: Few-shot Adaptive Gaze Estimation
+  * Mixed Precision Training for Video Synthesis
+  * Mixed Precision Training for Convolutional Tensor-Train LSTM
+  * Mixed Precision Training for 3D Medical Image Analysis
+
+Has PDF of the slides and the videos.
+
+Q&A:
+
+**What's the difference between FP32 and TF32 modes?**
+FP32 cores perform scalar instructions. TF32 is a Tensor Core mode,
+which performs matrix instructions - they are 8-16x faster and more
+energy efficient. Both take FP32 as inputs. TF32 mode also rounds
+those inputs to TF32.
+