Tag: keras

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Python for NLP: Creating Multi-Data-Type Classification Models with Keras

This is the 18th article in my series of articles on Python for NLP. In my previous article, I explained how to create a deep learning-based movie sentiment analysis model using Python’s Keras library. In that article, we saw how we can perform sentiment analysis of user reviews regarding different movies on IMDB. We used the text of the review the review to predict the sentiment. However, in text classification tasks, we can also make use of the non-textual information […]

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Python for NLP: Multi-label Text Classification with Keras

Introduction This is the 19th article in my series of articles on Python for NLP. From the last few articles, we have been exploring fairly advanced NLP concepts based on deep learning techniques. In the last article, we saw how to create a text classification model trained using multiple inputs of varying data types. We developed a text sentiment predictor using textual inputs plus meta information. In this article, we will see how to develop a text classification model with […]

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Solving Sequence Problems with LSTM in Keras

In this article, you will learn how to perform time series forecasting that is used to solve sequence problems. Time series forecasting refers to the type of problems where we have to predict an outcome based on time dependent inputs. A typical example of time series data is stock market data where stock prices change with time. Similarly, the hourly temperature of a particular place also changes and can also be considered as time series data. Time series data is […]

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Solving Sequence Problems with LSTM in Keras: Part 2

This is the second and final part of the two-part series of articles on solving sequence problems with LSTMs. In the part 1 of the series, I explained how to solve one-to-one and many-to-one sequence problems using LSTM. In this part, you will see how to solve one-to-many and many-to-many sequence problems via LSTM in Keras. Image captioning is a classic example of one-to-many sequence problems where you have a single image as input and you have to predict the […]

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Python for NLP: Deep Learning Text Generation with Keras

This is the 21st article in my series of articles on Python for NLP. In the previous article, I explained how to use Facebook’s FastText library for finding semantic similarity and to perform text classification. In this article, you will see how to generate text via deep learning technique in Python using the Keras library. Text generation is one of the state-of-the-art applications of NLP. Deep learning techniques are being used for a variety of text generation tasks such as […]

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Python for NLP: Neural Machine Translation with Seq2Seq in Keras

This is the 22nd article in my series of articles on Python for NLP. In one of my previous articles on solving sequence problems with Keras, I explained how to solve many to many sequence problems where both inputs and outputs are divided over multiple time-steps. The seq2seq architecture is a type of many-to-many sequence modeling, and is commonly used for a variety of tasks such as Text-Summarization, chatbot development, conversational modeling, and neural machine translation, etc. In this article, […]

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Autoencoders for Image Reconstruction in Python and Keras

Introduction Nowadays, we have huge amounts of data in almost every application we use – listening to music on Spotify, browsing friend’s images on Instagram, or maybe watching an new trailer on YouTube. There is always data being transmitted from the servers to you. This wouldn’t be a problem for a single user. But imagine handling thousands, if not millions, of requests with large data at the same time. These streams of data have to be reduced somehow in order […]

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Grid Search Optimization Algorithm in Python

Introduction In this tutorial, we are going to talk about a very powerful optimization (or automation) algorithm, i.e. the Grid Search Algorithm. It is most commonly used for hyperparameter tuning in machine learning models. We will learn how to implement it using Python, as well as apply it in an actual application to see how it can help us choose the best parameters for our model and improve its accuracy. So let’s start. Prerequisites To follow this tutorial, you should […]

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Deep Learning Models in Keras – Exploratory Data Analysis (EDA)

Introduction Deep learning is one of the most interesting and promising areas of artificial intelligence (AI) and machine learning currently. With great advances in technology and algorithms in recent years, deep learning has opened the door to a new era of AI applications. In many of these applications, deep learning algorithms performed equal to human experts and sometimes surpassed them. Python has become the go-to language for Machine Learning and many of the most popular and powerful deep learning libraries […]

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Deep Learning in Keras – Data Preprocessing

Introduction Deep learning is one of the most interesting and promising areas of artificial intelligence (AI) and machine learning currently. With great advances in technology and algorithms in recent years, deep learning has opened the door to a new era of AI applications. In many of these applications, deep learning algorithms performed equal to human experts and sometimes surpassed them. Python has become the go-to language for Machine Learning and many of the most popular and powerful deep learning libraries […]

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