Announcing Microsoft Azure’s New Tutorial on Deep Learning and NLP

Here at ODSC, we couldn’t be more excited to announce Microsoft Azure’s tutorial series on Deep Learning and NLP, now available for free on Ai+. This course series was created by a team of experts from the Microsoft community, who have brought their knowledge and experience in AI and deep learning to create an insightful learning experience. This team has a passion for fostering proficiency in AI, and can’t wait to help you deepen your knowledge in machine learning and deep learning.

Below you’ll find a brief overview of the four sessions that are currently available in this series: Series Introduction, Deep Learning Models: CNNs, RNNs, LSTMs, Encoder-Decoder Models, And Attention Mechanisms — Part 1, Encoder-Decoder Models, And Attention Mechanisms — Part 2.

Series Introduction

The first session will provide you with an introduction to this series, as well as the fundamentals of machine learning and deep learning with a focus on fully connected and convolution neural networks.

You’ll also cover Gradient Descent, which is essential knowledge for effective training and fine-tuning of your machine learning models.

Deep Learning Models: CNNs, RNNs, LSTMs

The second session is dedicated to exploring Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long-Short-Term Memory Networks (LSTMNs). These networks are used for a variety of applications, such as image and video processing tasks, sequential data such as text and speech, and language modeling and time series forecasting, respectively.

Encoder-Decoder Models, And Attention Mechanisms — Part 1 and Part 2

Sessions three and 4 will cover Encoder-Decoder Models, as well as attention mechanisms. You’ll explore the fundamentals of encoder-decoder architectures, which are vital for. Encoder-decoder architectures play a vital role in AI, especially in tasks involving sequence-to-sequence predictions such as machine translation, text summarization, and speech recognition.

Attention mechanisms, on the other hand, are crucial for tasks involving sequences, like natural language processing, as they allow models to focus on specific parts of the input when generating the output.

Sign me up!

What’s more, these four sessions are only the start. We’ll be adding more sessions on the machine learning and deep learning skills and concepts that will help you build AI proficiency. Get started learning for free here.

Originally posted on OpenDataScience.com

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