deep learning

Top 10 Deep Learning Concept Interview Questions and Answers Neural network model terms for data scientist and machine learning engineer interviews

Top 10 Deep Learning Concept Interview Questions and Answers

Deep learning (DL) terminologies are frequently asked during data science and machine learning interviews. In this tutorial, we will discuss the top 10 neural network model concept interview questions and how to answer them. Resources for this post: Let’s get started! Question 1: What is weight initialization for a neural network model? In the python code …

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Transfer Learning for Text Classification Using PyTorch Fine-tuning a pretrained transformer BERT model for customized sentiment analysis using PyTorch training loops

Transfer Learning for Text Classification Using PyTorch

Hugging Face provides three ways to fine-tune a pretrained text classification model: PyTorch, Tensorflow Keras, and transformer trainer. Compared with the other two ways, PyTorch training loops provide more customization and easier debugging of the training loops. This tutorial will use PyTorch to fine-tune a text classification model. We will talk about the following: If …

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Customized Sentiment Analysis: Transfer Learning Using Tensorflow with Hugging Face Fine-tune a pretrained transformer model for customized sentiment analysis using TensorFlow Keras with Hugging Face

Customized Sentiment Analysis: Transfer Learning Using Tensorflow with Hugging Face

Transfer learning is also called pretrained model fine-tuning. It refers to training a model with a small dataset while leveraging the stored information from a model trained with a large dataset for another task. In this tutorial, we will talk about how to use a small review dataset to build a sentiment prediction model while …

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Sentiment Analysis: Hugging Face Zero-shot Model vs Flair Pre-trained Model Which pre-trained Natual Language Processing (NLP) model has better prediction accuracy for the sentiment analysis, Hugging Face or Flair?

Sentiment Analysis: Hugging Face Zero-shot Model vs Flair Pre-trained Model

There are different methods for sentiment analysis. Some examples are lexicon-based methods, building customized models, using cloud services for sentiment predictions, or using pre-trained language models. In this tutorial, we will compare two state-of-art deep-learning pre-trained models for sentiment analysis, one from Hugging Face, and the other from Flair. We will talk about: Resources for …

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Create Beautiful AI Art Using Python KerasCV StableDiffusion on Google Colab Use text description to generate images in Google Colab and save the images in Google Drive

Create Beautiful AI Art Using Python KerasCV StableDiffusion on Google Colab

Stable diffusion is an open source text-to-image deep learning model by stability.ai. In this tutorial, we will talk about how to use the KerasCV’s implementation of stable diffusion to generate beautiful images based on text descriptions. We will talk about: Resources for this post: Let’s get started! Step 1: Set GPU as Runtime Type In the …

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Categorical Entity Embedding Using Python Tensorflow Keras Entity embedding for high cardinality categorical variables using Airbnb data

Categorical Entity Embedding Using Python Tensorflow Keras

Categorical entity embedding extracts the embedding layers of categorical variables from a neural network model, and uses numeric vectors to represent the properties of the categorical values. It is usually used on categorical variables with high cardinalities. For example, a marketing company can create categorical entity embedding for different campaigns to represent the characteristics using …

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Zero-shot Topic Modeling with Deep Learning Using Python Transformer-based zero-shot text classification model from Hugging Face for predicting NLP topic classes

Zero-shot Topic Modeling with Deep Learning Using Python

Zero-shot learning (ZSL) refers to building a model and using it to make predictions on the tasks that the model was not trained to do. For example, if we would like to classify millions of news articles into different topics, building a traditional multi-class classification model would be very costly because manually labeling the news …

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