Articles About Deep Learning

June 3, 2021 Deep Learning

Break down your CNN and visualize the features from within the model

Rover Reverse engineer your CNNs, in style. Rover will help you break down your CNN and visualize the features from within the model. No need to write weirdly abstract code to visualize your model’s features anymore. :computer: Usage git clone https://github.com/Mayukhdeb/rover.git; cd rover install requirements: pip install -r requirements.txt from rover import core from rover.default_models import models_dict core.run(models_dict = models_dict) and then run the script with streamlit as: $ streamlit run your_script.py if everything goes right, you’ll see something like: […]

May 29, 2021 Advanced, Deep Learning, NLP, Text, Unstructured Data

Measuring Text Similarity Using BERT

This article was published as a part of the Data Science Blogathon BERT is too kind — so this article will be touching on BERT and sequence relationships! Abstract A significant portion of NLP relies on the connection in highly-dimensional spaces. Typically an NLP processing will take any text, prepare it to generate a tremendous vector/array rendering said text — then make certain transformations. It’s a highly-dimensional charm. At an exceptional level, there’s not much extra to it. We require to […]

May 24, 2021 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 […]

May 23, 2021 Deep Learning

A collection of pretrained models in Flax

Flax Models The goal of this project is to make current deep learning models more easily available for the awesome Jax/Flax ecosystem. Models Example Notebooks to play with on Colab Installation You will need Python 3.7 or later. For GPU usage, follow the Jax installation with CUDA. Then install: > pip install –upgrade git+https://github.com/matthias-wright/flaxmodels.git For CPU-only you can skip step 1. Documentation The documentation for the models can be found here. Checkpoints The checkpoints are taken from the repositories that […]

May 19, 2021 Deep Learning, Intermediate, NLP, Python, Unstructured Data

SMS Spam Detection Using LSTM – A Hands On Guide!

Introduction In today’s world, almost everyone is using a mobile phone and all of them will receive messages(SMS/ email) daily on their phone. But the main thing is that many of the received messages will be spam and only a few of them are ham or required messages. In this article, we are going to create an SMS spam detection model which will help you to find whether an SMS is spam or not using LSTM. About Dataset: Here we […]

May 18, 2021 Advanced, Deep Learning, NLP, Python, Text

Hands-on Experience With GPT3!

This article was published as a part of the Data Science Blogathon. Yes, you read it right. In this article, I will give you a hands-on experience of GPT3. I was lucky enough to get access to private beta after waiting for 8 months 🙂 Introduction Last week I was exploring with GPT-3. I was thinking I will learn it in a data it two. But boy it took me a lot of time to learn it. I was very surprised […]

May 12, 2021 Deep Learning

Medical image analysis framework merging ANTsPy and deep learning

ANTsPyNet A collection of deep learning architectures and applications ported to the python language and tools for basic medical image processing. Based on keras and tensorflow with cross-compatibility with our R analog ANTsRNet. Documentation page https://antsx.github.io/ANTsPyNet/. Installation Publications Nicholas J. Tustison, Talissa A. Altes, Kun Qing, Mu He, G. Wilson Miller, Brian B. Avants, Yun M. Shim, James C. Gee, John P. Mugler III, Jaime F. Mata. Image- vs. histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images. (medrxiv) […]

April 23, 2021 Beginner, Deep Learning, NLP

Role of Machine Learning in Natural Language Processing

Introduction Machine Learning and Natural Language Processing are important subfields of Artificial Intelligence that have gained prominence in recent times. Machine Learning and Natural Language Processing play a very important part in making an artificial agent into an artificial ‘intelligent’ agent. An Artificially Intelligent system can accept better information from the environment and can act on the environment in a user-friendly manner because of the advancement in Natural Language Processing. Similarly, an Artificially Intelligent System can process the received information […]

February 26, 2021 Advanced, Deep Learning, NLP, Videos Leave a comment

Basics of Natural Language Processing(NLP) for Absolute Beginners

Introduction According to industry estimates, only 21% of the available data is present in a structured form. Data is being generated as we speak, as we tweet, as we send messages on WhatsApp and in various other activities. The majority of this data exists in the textual form, which is highly unstructured in nature. Despite having high dimension data, the information present in it is not directly accessible unless it is processed (read and understood) manually or analyzed by an […]

February 24, 2021 Beginner, Career, Computer Vision, Data Exploration, Data Visualization, Deep Learning, Education, Excel, Interviews, Learning Path, NLP, Python, SQL, Statistics, Tableau, TIME SERIES Leave a comment

A Comprehensive Step-by-Step Guide to Become an Industry Ready Data Science Professional

Introduction to Artificial Intelligence and Machine Learning Artificial Intelligence (AI) and its sub-field Machine Learning (ML) have taken the world by storm. From face recognition cameras, smart personal assistants to self-driven cars. We are moving towards a world enhanced by these recent upcoming technologies. It’s the most exciting time to be in this career field! The global Artificial Intelligence market is expected to grow to $400 billion by the year 2025. From Startups to big organizations, all want to join […]

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