mingfeima

Part III: Vectorization Techniques

(Training material on pytorch CPU performance optimization)

• Part I: Memory Formats and Channels Last Optimization
• Part II: Parallelization Techniques
• Part IV: BFloat16 Kernel Optimization

Chinese version for this chapter, link.

This section contains the following subjects:

creyesp

Blogs

• https://medium.datadriveninvestor.com/how-to-build-a-recommendation-system-for-purchase-data-step-by-step-d6d7a78800b6
• https://www.kaggle.com/c/santander-product-recommendation
• https://www.kaggle.com/retailrocket/ecommerce-dataset/home
• https://www.kaggle.com/dschettler8845/recsys-2020-ecommerce-dataset/tasks?taskId=3124
• https://www.kaggle.com/sohamohajeri/recommendation-system-for-electronic-dataset
• https://towardsdatascience.com/extreme-deep-factorization-machine-xdeepfm-1ba180a6de78
• https://medium.com/building-creative-market/word2vec-inspired-recommendations-in-production-f2c6a6b5b0bf
• https://medium.com/shoprunner/fetching-better-beer-recommendations-with-collie-part-1-18c73ab30fbd

jph00

All of the python 3.9 standard library

For a version without the collapsible details sections (so you can search the whole thing in your browser), click here.

abc

• abc.ABC
• abc.ABCMeta

nixiz

#include <iostream>
#include <memory>
#include <thread>
#include <mutex>
#include <future>
#include <condition_variable>
#include <functional>
#include <vector>
#include <deque>
#include <type_traits>

nixiz

#include <benchmark/benchmark.h>
#include <iostream>
#include <random>
#include <algorithm>
#include <vector>
#include <chrono>
#include <iterator>
#include <execution>

static void count_if_random(benchmark::State& state)

jmoyers

• color scheme
  • dracula - https://draculatheme.com/
    • tmux
    • terminal
    • vim
  • using plug (vim), tpm (tmux), and a json file for windows terminal
• terminal
  • i use bash, you can use zsh, its all mostly applicable
  • fuzzy learning cd: https://github.com/rupa/z
• z proj, takes you to /some/deep/directory/project

cbalint13

#!/usr/bin/python3

#import nnvm.frontend.darknet
import tvm.relay.testing.yolo_detection
import tvm.relay.testing.darknet
import tvm.relay.transform as _transform
import matplotlib.pyplot as plt
import numpy as np
import tvm
import onnx

viridiano

AWESOME LINGUISTICS - A curated list of anything remotely related to linguistics, sorted in alphabetical order.

Getting Started with Coding for Humanities Scholars (Python)

• Colab Notebook: introduction to Python
• Introduction to Jupyter Notebooks – Jupyter notebooks provide an environment where you can freely combine human-readable narrative with computer-readable code.
• Installing Python Modules with pip – How to download and install Python modules.

NOTE: The Programming Historian contains many coding lessons gea

cbalint13

#!/usr/bin/python3

import nnvm
import nnvm.frontend.darknet
import tvm.relay.testing.yolo_detection
import tvm.relay.testing.darknet
import tvm.relay.transform as _transform
import matplotlib.pyplot as plt
import numpy as np
import tvm

toolness

Adventures in Python Core Dumping

After watching Bryan Cantrill's presentation on [Running Aground: Debugging Docker in Production][aground] I got all excited (and strangely nostalgic) about the possibility of core-dumping server-side Python apps whenever they go awry. This would theoretically allow me to fully inspect the state of the program at the point it exploded, rather than relying solely on the information of a stack trace.