September 25, 2020 Python

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, […]

September 25, 2020 Python

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 […]

September 25, 2020 Python

Introduction to PyTorch for Classification

PyTorch and TensorFlow libraries are two of the most commonly used Python libraries for deep learning. PyTorch is developed by Facebook, while TensorFlow is a Google project. In this article, you will see how the PyTorch library can be used to solve classification problems. Classification problems belong to the category of machine learning problems where given a set of features, the task is to predict a discrete value. Predicting whether a tumour is cancerous or not, or whether a student […]

September 25, 2020 Python

Time Series Prediction using LSTM with PyTorch in Python

Time series data, as the name suggests is a type of data that changes with time. For instance, the temperature in a 24-hour time period, the price of various products in a month, the stock prices of a particular company in a year. Advanced deep learning models such as Long Short Term Memory Networks (LSTM), are capable of capturing patterns in the time series data, and therefore can be used to make predictions regarding the future trend of the data. […]

September 25, 2020 Python

Dimensionality Reduction in Python with Scikit-Learn

Introduction In machine learning, the performance of a model only benefits from more features up until a certain point. The more features are fed into a model, the more the dimensionality of the data increases. As the dimensionality increases, overfitting becomes more likely. There are multiple techniques that can be used to fight overfitting, but dimensionality reduction is one of the most effective techniques. Dimensionality reduction selects the most important components of the feature space, preserving them and dropping the […]

September 25, 2020 Python

Tensorflow 2.0: Solving Classification and Regression Problems

After much hype, Google finally released TensorFlow 2.0 which is the latest version of Google’s flagship deep learning platform. A lot of long-awaited features have been introduced in TensorFlow 2.0. This article very briefly covers how you can develop simple classification and regression models using TensorFlow 2.0. Classification with Tensorflow 2.0 If you have ever worked with Keras library, you are in for a treat. TensorFlow 2.0 now uses Keras API as its default library for training classification and regression […]

September 25, 2020 Python

Introduction to Speech Recognition with Python

Speech recognition, as the name suggests, refers to automatic recognition of human speech. Speech recognition is one of the most important tasks in the domain of human computer interaction. If you have ever interacted with Alexa or have ever ordered Siri to complete a task, you have already experienced the power of speech recognition. Speech recognition has various applications ranging from automatic transcription of speech data (like voicemails) to interacting with robots via speech. In this tutorial, you will see […]

September 25, 2020 Python

Ensemble/Voting Classification in Python with Scikit-Learn

Introduction Ensemble classification models can be powerful machine learning tools capable of achieving excellent performance and generalizing well to new, unseen datasets. The value of an ensemble classifier is that, in joining together the predictions of multiple classifiers, it can correct for errors made by any individual classifier, leading to better accuracy overall. Let’s take a look at the different ensemble classification methods and see how these classifiers can be implemented in Scikit-Learn. What are Ensemble Models in Machine Learning? Credit: Pixabay […]

September 25, 2020 Python

Text Classification with BERT Tokenizer and TF 2.0 in Python

This is the 23rd article in my series of articles on Python for NLP. In the previous article of this series, I explained how to perform neural machine translation using seq2seq architecture with Python’s Keras library for deep learning. In this article we will study BERT, which stands for Bidirectional Encoder Representations from Transformers and its application to text classification. BERT is a text representation technique like Word Embeddings. If you have no idea of how word embeddings work, take […]

September 25, 2020 Python

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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