Text Generation in Deep Learning with Tensorflow & Keras: Fundamentals
This tutorial is the first part of the “Text Generation in Deep Learning” series. We will cover all the topics related to Text Generation with sample implementations in Python Tensorflow Keras. You can access the codes, videos, and posts from the below links. In this part, we will learn the Fundamentals of Text Generation in Deep Learning.
If you would like to learn more about Deep Learning with practical coding examples, please subscribe to my YouTube Channel or followmy blog on muratkarakaya.net. Please, turn on notifications to notify you when new parts are uploaded.
If you would like to learn more about Deep Learning with practical coding examples, please subscribe to the Murat Karakaya Akademi YouTube Channel or followmy blog on muratkarakaya.net. Do not forget to turn on notifications so that you will be notified when new parts are uploaded.
Author:Murat Karakaya Date created: 21 April 2021 Last modified: 24 May 2021 Description: This is an introductory tutorial on Controllable Text Generation in Deep Learning which is the second part of the “Controllable Text Generation with Transformers” series. This series will focus on developing TensorFlow (TF) / Keras implementation of Controllable Text Generation from scratch. You can access all these parts from my blog at muratkarakaya.net.
Before getting started, I assume that you have already reviewed:
How to save and load a TensorFlow / Keras Model with Custom Objects?
Author:Murat Karakaya Date created: 30 May 2021 Last modified: 30 July 2021 Description: This tutorial will design and train a Keras model (miniature GPT3) with some custom objects (custom layers). We aim to learn how to save the trained model as a whole and load the saved model “TensorFlow SevedModel”. Accessible on:
Bookmarks to the selected Deep Learning / Machine Learning Resources on the Web
Author:Murat Karakaya Date created: 19 May 2020 Last modified: 15 Dec 2021 Description: In this post, I share my bookmarks classified according to specific topics in Deep Learning / Machine Learning. Thus, you can save your time searching for similar information on the web. If you have any comments or updates please feel free to share with me!
If you are interested in Deep Learning / Machine learning, you can find hundreds of video tutorials with Python code samples in Jupyter notebooks at the following links:
Multi-Class Text Classification with a GPT3 Transformer block: An End-to-End Example
Author:Murat Karakaya & Cansen Çağlayan Date created: 05 Oct 2021 Last modified: 19 Oct 2021 Description: This tutorial has 2 parts as explained below. Part A: Data Analysis & Text Preprocessing and Part B: Text Classification.
Multi-Topic (Multi-Class) Text Classification With Various Deep Learning Models Tutorial Series
Index Page
This is the index page of the “Multi-Topic (Multi-Class) Text Classification With Various Deep Learning Models” tutorial series.
Author: Murat Karakaya Date created….. 17 Sept 2021 Date published… 11 March 2022 Last modified…. 09 April 2023
Description: This is a tutorial series that covers all the phases of text classification: Exploratory DataAnalysis (EDA) of text, text preprocessing, and multi-class (multi-topic) text classification using the TF Data Pipeline and the Keras TextVectorization preprocessing layer.
We will design various Deep Learning models by using the Keras Embedding layer, Convolutional (Conv1D) layer, Recurrent (LSTM) layer, Transformer Encoder block, and pre-trained transformer (BERT).
We will use a Kaggle Dataset with 32 topics and more than 400K reviews.
We will cover all the topics related to solving Multi-Class Text Classification problems with sample implementations in Python TensorFlow Keras.
You can access the codes, videos, and posts from the below links.
If you would like to learn more about Deep Learning with practical coding examples, please subscribe to the Murat Karakaya Akademi YouTube Channel or follow my blog on muratkarakaya.net. Remember to turn on notifications so that you will be notified when new parts are uploaded.
