/mlg/ machine learning general

Anybody know who the girl in OP is? I tried to reverse image search, but couldn't find anything.

you need to use the ML reverse image recognition service

Attached: 1487608590469.jpg (960x1280, 136K)

isn't this information export controlled?

did you try deepnude? that's just scratching the surface. I want to learn ML/AI etc and improve deepnude but I'm too fucking lazy

What's the difference between Machine Learning Scientist and Data Scientist?

ML implements many of the same algorithms used by data scientists, ML is sort of a superset of data science

are there more Machine Learning Scientist jobs than Data Scientists?

This is... a girl right?

vag, butthole and all

Aneu - ancient attention slut from back in the day.

Have a rare pic.

Attached: 11-20-2015 (2).jpg (1438x540, 43K)

noice throwback brapper post

didn't you drop out of uni?
still living with your folks

Can you be a machine learning scientist without a PhD?

>didn't you drop out of uni?
yes
>still living with your folks
no

Used to be petsu or tepru on lainchan.
Now laboum on irc, shitposting about kpop and mechanical keyboards.
I never saw her emacs config tho.

#imports in next post

tf.logging.set_verbosity(tf.logging.ERROR)
pd.options.display.max_rows = 10
pd.options.display.float_format = '{:.1f}'.format

california_housing_dataframe = pd.read_csv("download.mlcc.google.com/mledu-datasets/california_housing_train.csv", sep=",")

# california_housing_dataframe = california_housing_dataframe.reindex(
# np.random.permutation(california_housing_dataframe.index))

def preprocess_features(california_housing_dataframe):
"""Prepares input features from California housing data set.

Args:
california_housing_dataframe: A Pandas DataFrame expected to contain data
from the California housing data set.
Returns:
A DataFrame that contains the features to be used for the model, including
synthetic features.
"""
selected_features = california_housing_dataframe[
["latitude",
"longitude",
"housing_median_age",
"total_rooms",
"total_bedrooms",
"population",
"households",
"median_income"]]
processed_features = selected_features.copy()
# Create a synthetic feature.
processed_features["rooms_per_person"] = (
california_housing_dataframe["total_rooms"] /
california_housing_dataframe["population"])
return processed_features

def preprocess_targets(california_housing_dataframe):
"""Prepares target features (i.e., labels) from California housing data set.

Args:
california_housing_dataframe: A Pandas DataFrame expected to contain data
from the California housing data set.
Returns:
A DataFrame that contains the target feature.
"""
output_targets = pd.DataFrame()
# Scale the target to be in units of thousands of dollars.
output_targets["median_house_value"] = (
california_housing_dataframe["median_house_value"] / 1000.0)

return output_targets

from __future__ import print_function

import math

from IPython import display
from matplotlib import cm
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from sklearn import metrics
import tensorflow as tf
from tensorflow.python.data import Dataset

tf.logging.set_verbosity(tf.logging.ERROR)
pd.options.display.max_rows = 10
pd.options.display.float_format = '{:.1f}'.format

california_housing_dataframe = pd.read_csv("download.mlcc.google.com/mledu-datasets/california_housing_train.csv", sep=",")

# california_housing_dataframe = california_housing_dataframe.reindex(
# np.random.permutation(california_housing_dataframe.index))

bumping

pumping

this is actually good

Is AMD viable yet or is everything still CUDA dominated?

Cuda
Those tpu edge look neatnif you're ok with tensor flow lite.
Intel is also checking in nextplatform.com/2019/07/15/intel-prepares-to-graft-googles-bfloat16-onto-processors/

Thanks user

She looks exactly like my ex. Except that I taught her how to use vim like a patrician, instead of emacs.

>Like my ex
So glad for you it didn't work out user

Thanks, me too. She was a crazy leftist.

I don't think the CUDA situation is bound to change, they got smart and got in first.
It's really sad because AMD seems like a better alternative, but there's already too much built on CUDA, and people are lazy fags and have no reason to learn to put shit on ayyymd GPUs when Nvidia is already the marketforce

What are you complaining about?

What's the best cloud computing for deep learning? I want to be competitive on kaggle but colab isn't cutting it.

Free bump from me.
Already used to playing around with Python. And dabbled a bit in ML, but that never got off the ground.
Will try harder this time

> OP got 360 noscoped by xXx_Ex_de_de_de_de_xXx gr8 m8 8/8 420 blaze it gg no re.
> Sponsored by Doritos™ and Mountain Kek®