From 5ae3d377898e078ad341f73c986b05bee8492639 Mon Sep 17 00:00:00 2001 From: Rowan Cockett Date: Wed, 5 Jun 2024 10:45:24 +0200 Subject: [PATCH] =?UTF-8?q?Remove=20likely=20copy-paste=20of=20pilcrow=20(?= =?UTF-8?q?"=C2=B6")?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- content/tutorial-nlp-from-scratch.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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