constantinembufung/Webscraping-using-python-and-BeutifulSoup constantinembufung/Webscraping-using-python-and-BeutifulSoup

  • Stars
    star
    1
  • Language
    Jupyter Notebook
  • Created over 6 years ago
  • Updated over 1 year ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
constantinembufung/Webscraping-using-python-and-BeutifulSoup
github

constantinembufung

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

To automate data extraction from a web page into Excel: I have explained the idea on how to automate a data extraction from a web page to Excel using Python. Libraries - Urllib, BeautifulSoup

More Repositories

1

-100DaysOfMLCode-OCR-Invoice-Recognition

In 100 days i will an OCR invoice recognition software
Jupyter Notebook
7
star
2

Neural-Network-on-MNIST-dataset

Lets implement a neural network that is able to classify digits. For this task, we will be using the famous MNIST dataset made available at http://yann.lecun.com/exdb/mnist/
Jupyter Notebook
1
star
3

multiclass-classification-with-tensorflow

Jupyter Notebook
1
star
4

lstm_image_caption

Applications of LSTM - Image caption Generation. For generating captions for images, we will use a popular dataset for image captioning tasks known as Microsoft Common Objects in Context (MS-COCO). We will ๏ฟฝrst process images from the dataset (MS-COCO) to obtain an encoding of the images with a pretrained Convolutional Neural Network (CNN), which is already good at classifying images. The CNN will take a ๏ฟฝxed-size image as the input and output the class the image belongs to (for example, cat, dog, bus, and tree). Using this CNN, we can obtain compressed encoded vectors describing images. Then we will process the captions of the images to learn the word embeddings of the words found in captions. We can also use pretrained word vectors for this task. Finally, having obtained both the image and word encodings, we will feed them into an LSTM and train it on the images and their respective captions.
1
star