deep learning tutorial

AI Generative AI

Parti – Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Parti is a new autoregressive text-to-image model that shows just how much scale can achieve. This model’s outputs are crips, accurate, realistic, and can combine arbitrary styles, concepts, and fulfil even challenging requests. Yannic explains the research paper. Time stamps: 0:00 – Introduction 2:40 – Example Outputs 6:00 – Model Architecture 17:15 – Datasets (incl. […]

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Deep Learning

Deep Learning Models Using Keras Tutorial

This Edureka Tutorial on “Keras Tutorial” provides you a quick and insightful tutorial on the working of Keras along with an interesting use-case. Topics Covered : 00:00 Introduction 00:30 Agenda 01:12 What is Keras 01:53 Who Makes Keras 02:55 What Makes Keras Special 04:08 Keras User Experience 05:04 Multi-Backend & Multi-Platform 06:55 Keras Models 09:03 […]

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Neural Networks

Recurrent Neural Networks Tutorial

This Edureka Recurrent Neural Networks tutorial video will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next […]

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Deep Learning

What is Deep Learning? | Deep Learning Tutorial For Beginners | Edureka | Deep Learning Rewind – 2

This Edureka “What is Deep Learning” video will help you to understand the relationship between Deep Learning, Machine Learning and Artificial Intelligence. It will also explain what is Deep learning and how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning.

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Natural Language Processing TensorFlow

Text Classification Using BERT & Tensorflow

Using BERT and Tensorflow 2.0, we will write simple code to classify emails as spam or not spam. BERT will be used to generate sentence encoding for all emails and after that we will use a simple neural network with one drop out layer and one output layer.

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