Tag: pytorch

Advanced, Artificial intelligence, Deep Learning, NLP, PyTorch

BERT for Natural Language Inference simplified in Pytorch!

This article was published as a part of the Data Science Blogathon Introduction to BERT: BERT stands for Bidirectional Encoder Representations from Transformers. It was introduced in 2018 by Google Researchers. BERT achieved state-of-art performance in most of the NLP tasks at that time and drawn the attention of the data science community worldwide. It is extensively used today by data science practitioners for various NLP tasks. Details about the working of the BERT model can be found here. Introduction to […]

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Beginner, Computer Vision, Deep Learning, Infographic, Infographics, Listicle, NLP, Python Leave a comment

5 Amazing Deep Learning Frameworks Every Data Scientist Must Know! (with Illustrated Infographic)

Introduction I have been a programmer since before I can remember. I enjoy writing codes from scratch – this helps me understand that topic (or technique) clearly. This approach is especially helpful when we’re learning data science initially. Try to implement a neural network from scratch and you’ll understand a lot of interest things. But do you think this is a good idea when building deep learning models on a real-world dataset? It’s definitely possible if you have days or […]

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Advanced, Algorithm, Deep Learning, NLP, Text, Unstructured Data, Unsupervised Leave a comment

Build a Natural Language Generation (NLG) System using PyTorch

Overview Introduction to Natural Language Generation (NLG) and related things- Data Preparation Training Neural Language Models Build a Natural Language Generation System using PyTorch Introduction In the last few years, Natural language processing (NLP) has seen quite a significant growth thanks to advancements in deep learning algorithms and the availability of sufficient computational power. However, feed-forward neural networks are not considered optimal for modeling a language or text. This is because the feed-forward network does not take into consideration the […]

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Advanced, Deep Learning, NLP, Project, Python, Supervised, Text, Unstructured Data Leave a comment

Transfer Learning for NLP: Fine-Tuning BERT for Text Classification

Introduction With the advancement in deep learning, neural network architectures like recurrent neural networks (RNN and LSTM) and convolutional neural networks (CNN) have shown a decent improvement in performance in solving several Natural Language Processing (NLP) tasks like text classification, language modeling, machine translation, etc. However, this performance of deep learning models in NLP pales in comparison to the performance of deep learning in Computer Vision. One of the main reasons for this slow progress could be the lack of […]

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Classification, Computer Vision, Deep Learning, Image, Libraries, NLP, Programming, Python, PyTorch, Supervised, Unstructured Data Leave a comment

Get Started with PyTorch – Learn How to Build Quick & Accurate Neural Networks (with 4 Case Studies!)

Introduction PyTorch v TensorFlow – how many times have you seen this polarizing question pop up on social media? The rise of deep learning in recent times has been fuelled by the popularity of these frameworks. There are staunch supporters of both, but a clear winner has started to emerge in the last year. PyTorch was one of the most popular frameworks in 2018. It quickly became the preferred go-to deep learning framework among researchers in both academia and the […]

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Beginner, Classification, Deep Learning, NLP, Project, Python, PyTorch, Supervised, Technique, Text, Unstructured Data Leave a comment

Build Your First Text Classification model using PyTorch

Overview Learn how to perform text classification using PyTorch Grasp the importance of Pack Padding feature Understand the key points involved while solving text classification Introduction I always turn to State of the Art architectures to make my first submission in data science hackathons. Implementing the State of the Art architectures has become quite easy thanks to deep learning frameworks such as PyTorch, Keras, and TensorFlow. These frameworks provide an easy way to implement complex model architectures and algorithms with […]

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Image Classification with Transfer Learning and PyTorch

Introduction Transfer learning is a powerful technique for training deep neural networks that allows one to take knowledge learned about one deep learning problem and apply it to a different, yet similar learning problem. Using transfer learning can dramatically speed up the rate of deployment for an app you are designing, making both the training and implementation of your deep neural network simpler and easier. In this article we’ll go over the theory behind transfer learning and see how to […]

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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 […]

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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. […]

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