llSourcell

Achieving warp speed with Rust

Contents:

• Number one optimization tip: don't
• Never optimize blindly
• Don't bother optimizing one-time costs
• Improve your algorithms
• CPU architecture primer
• Keep as much as possible in cache
• Keep as much as possible in registers

llSourcell

Game 1: Avoid the Hooks

Goal

• Dedicate 60 hours/week to learning a subject

Feedback System

• Pomodoro Score

Rules

• Find or Build the idea gateway

llSourcell

//
//  onehiddenlayerperceptron.cu
//  onehiddenlayerperceptron
//
//  Created by Sergei Bugrov on 8/21/17.
//  Copyright © 2017 Sergei Bugrov. All rights reserved.
//

#include <stdio.h>

llSourcell

#by MBT on StackOverflow
import numpy as np

# D_in is input dimension;
# H is hidden dimension;
# D_out is output dimension.
Batch_Size, D_in, H, D_out = 12, 1000, 100, 10

# Create random input and output data
x = np.random.randn(Batch_Size, D_in)

llSourcell

#by MBT on StackOverflow
import numpy as np

# D_in is input dimension;
# H is hidden dimension;
# D_out is output dimension.
Batch_Size, D_in, H, D_out = 64, 1000, 100, 10

# Create random input and output data
x = np.random.randn(Batch_Size, D_in)

llSourcell

#pytorch team
for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

llSourcell

#by deepmind
import tensorflow as tf
import sonnet as snt

#setup a 'module'
mlp = snt.Sequential([
    snt.Linear(1024),
    tf.nn.relu,
    snt.Linear(10),
])

llSourcell

#Step 1 - Import biopython modules for sequence alignment
from Bio import pairwise2
from Bio.Seq import Seq 

#Step 2- define multiple sequences
seq1 = Seq("ACCGGT") 
seq2 = Seq("ACGT")

#step 3 - align 
alignments = pairwise2.align.globalxx(seq1, seq2) 

llSourcell

greeting_inputs = ("hey", "good morning", "good evening", "morning", "evening", "hi", "whatsup")
greeting_responses = ["hey", "hey hows you?", "*nods*", "hello, how you doing", "hello", "Welcome, I am good and you"]

def generate_greeting_response(greeting):
    for token in greeting.split():
        if token.lower() in greeting_inputs:
            return random.choice(greeting_responses)

llSourcell

#(From the Official "Reformer" Repository)

def hash_vectors(self, vecs, rng):
    # If we factorize the hash, find a factor dividing n_buckets nicely.
    rot_size, factor_list = self.n_buckets, [self.n_buckets]
    if self._factorize_hash:
      # If we are given a list of factors, verify it and use later.
      if isinstance(self._factorize_hash, list):
        rot_size, product = 0, 1
        factor_list = self._factorize_hash