biranchi2018

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
model = AutoModelForCausalLM.from_pretrained("distilgpt2", output_hidden_states=True)

print(model)
print(model.lm_head)
print(model.transformer.wte) # Dimensions are: (Number of tokens in vocabulary, dimension of model)
print(model.transformer.wte(torch.tensor(464)))

biranchi2018

%%writefile remove_widgets.py

# pip install nbformat

import nbformat
from nbformat import v4
import glob

def remove_widgets_metadata(notebook_path):
    """Removes the 'metadata.widgets' entry from the notebook, preserving cell outputs."""

biranchi2018

Gradio :
=========

pip install gradio


=============================================================

biranchi2018

POST Requests :
===================

import requests

url = 'https://clothing-model.default.kubeflow.mlbookcamp.com/v1/models/clothing-model:predict'

data = {
    "instances": [

biranchi2018

from asyncio import run


async def sum():
	total = 0
	for i in range(0,10):
		print(f'i : {i}')
		total += i
	return total

biranchi2018

import requests

url = 'https://www.facebook.com/favicon.ico'
r = requests.get(url, allow_redirects=True)

with open('facebook.ico', 'wb') as f:
	f.write(r.content)

biranchi2018

import nltk
nltk.download('punkt')
import itertools 

text = "today is 'Nayan's birthday. she loves ice cream. she is also fond of cream cake. we will celebrate her birthday with ice cream cake"

sentences = nltk.sent_tokenize(text)
words = [nltk.word_tokenize(sent) for sent in sentences]
print(words)

biranchi2018

Tensorflow Object Detection API :
==================================

https://www.youtube.com/watch?v=COlbP62-B-U&index=1&list=PLQVvvaa0QuDcNK5GeCQnxYnSSaar2tpku

https://github.com/tensorflow/models/tree/master/research/object_detection

https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md

biranchi2018

Steps for Installing LabelImg :
=================================

Step 1. 
git clone https://github.com/tzutalin/labelImg

Step 2. 
sudo pip3 install pyqt5 lxml

Step 3.

biranchi2018

from imutils import face_utils
import dlib
import cv2
import time

# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor

cap = cv2.VideoCapture(0)