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Word embeddings are techniques used in natural language processing. This includes tools & techniques like word2vec, TD-IDF, count vectors, etc. Explore Now!
Learn about transfer learning, pre-trained models, their benefits, usage, fine-tuning techniques, and projects for identifying digits.
25 terms to explain the basics of neural networks, deep learning, Convolutional Neural Network, Recurrent Neural network & gradient descent in data science
The difference in CPUs & GPUs to help you understand the application of GPUs in training deep learning models in data science. Brief history of GPUs.
PyData 2017 held in Amsterdam includes talks on machine learning, deep learning and NLP. The content involved applications, tools and hands-on sessions.
Here are the top 40 questions on deep learning, designed for data scientists to test their knowledge, skills, and concepts.
Difference between machine learning & deep learning (ML Vs DL) along with the comparison between the two in the field of data science.Explained with examples.
TensorFlow is the popular library of deep learning. This article describes the basics of tensors and graphs and why tensors is important for tensorflow.
Get an introduction to gradient descent algorithm & its variants. How to implement gradient descent algorithm with practical tips
Deep learning applications for beginners using python. This article explains 5 deep learning applications using API and open source.
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