llSourcell

# Load relevant libraries
import numpy as np
import pandas as pd
import seaborn as sns
from IPython.display import Image
import matplotlib.pyplot as plt
%matplotlib inline
from pathlib import Path
import random
import os

llSourcell

# Code in file nn/two_layer_net_module.py
import torch

class TwoLayerNet(torch.nn.Module):
  def __init__(self, D_in, H, D_out):
    """
    In the constructor we instantiate two nn.Linear modules and assign them as
    member variables.
    """
    super(TwoLayerNet, self).__init__()

llSourcell

export function fetchUser(address) {
  return async function(dispatch) {
    try {
      const user = await origin.users.get(address)

      dispatch({
        type: UserConstants.FETCH_SUCCESS,
        user,
      })
    } catch(error) {

llSourcell

[
   {
      "id": 0,
      "country": "ARE",
      "city": "abu dhabi",
      "lat": 24.453884,
      "lng": 54.377342,
      "deans": [
         "Abdul Qureshi"
      ],

llSourcell

Week 1 - Introduction

• Introduction
• Crop Yield Maximization
• Text Classification
• Insurance Analysis

Topics Covered

Markov Decision Processes, Policy Functions, Value Functions, and the Bellman Equation

Week 2 - Dynamic Programming

llSourcell

Step 1 - Download test.prototxt from https://raw.githubusercontent.com/rbgirshick/py-faster-rcnn/master/models/pascal_voc/VGG16/faster_rcnn_end2end/test.prototxt

Step 2 - Download the pretrained models from https://dl.dropboxusercontent.com/s/o6ii098bu51d139/faster_rcnn_models.tgz?dl=0

Step 3 - Unzip the Model archive to get the file labeled 'VGG16_faster_rcnn_final.Caffe.model'

llSourcell

python3 sraval/Desktop/mo_Caffe.py --input_model Downloads/VGG16_faster_rcnn_final.Caffe*model --input_proto sraval/Downloads/deploy.prototxt --extensions sraval/Downloads/fasterrcnn_extensions

llSourcell

// array of poses/persons
[ 
  { // pose #1
    "score": 0.42985695206067,
    "keypoints": [
      { // nose
        "position": {
          "x": 126.09371757507,
          "y": 97.861720561981
         },

llSourcell

import keras

# Step 1 - Load Data

(X_train, y_train), (X_test, y_test) = load_data()

# Step 2 Build Generative model (U-NET)

g_input = Input(input_size)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(g_input)

llSourcell

#deps
#!pip install cython
#!pip install spacy

#1 - make rects
#pseudo random number generator, dimensions of rectangle
from random import random

#OOP 0:) 
class Rectangle: