diff --git a/content/tutorial-nlp-from-scratch.md b/content/tutorial-nlp-from-scratch.md index e51d6b1d..2eb581da 100644 --- a/content/tutorial-nlp-from-scratch.md +++ b/content/tutorial-nlp-from-scratch.md @@ -441,7 +441,7 @@ emb_path = textproc.unzipper(glove, 'glove.6B.300d.txt') emb_matrix = textproc.loadGloveModel(emb_path) ``` -## 3. Build the Deep Learning Model¶ +## 3. Build the Deep Learning Model It is time to start implementing our LSTM! You will have to first familiarize yourself with some high-level concepts of the basic building blocks of a deep learning model. You can refer to the [Deep learning on MNIST from scratch tutorial](https://numpy.org/numpy-tutorials/content/tutorial-deep-learning-on-mnist.html) for the same. You will then learn how a Recurrent Neural Network differs from a plain Neural Network and what makes it so suitable for processing sequential data. Afterwards, you will construct the building blocks of a simple deep learning model in Python and NumPy and train it to learn to classify the sentiment of a piece of text as positive or negative with a certain level of accuracy pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.

Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy