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Technical Introduction to AI, Machine Learning & Deep Learning Part 3: Text and Audio Algorithms (RNNs and LSTMs)

 

Deep learning Part 3 Text and Audio Algorithms (RNNs and LSTMs)

This class is part three in a series.  It’s designed as a follow up to Introduction to Deep learning Part 2 Vision Algorithms, but might also be appropriate for someone who took the coursera course in machine learning and wanted something very hands on or could be appropriate for someone who took Technical Introduction to AI, Machine Learning & Deep Learning and wanted to really focus on audio and text.

This is a hands-on course that takes students from little knowledge of deep learning to comfort building real world models.  It requires very little math, but reasonably proficient programming skills.  At the end of class students will be able to build LSTMs on their own, and more importantly be able to quickly find resources to help them with new problems they encounter in their domain.

Technologies Introduced

  • Deep Learning Frameworks

    • Keras

    • Tensorflow

  • Deep Learning Model Architectures

    • RNN

    • CNN

    • LSTM

    • GRU

    • word2vec

  • Applications
    • Text generation

    • Music generation

    • Text classification

Prerequisites:

1. Completion of our introductory course “Technical Introduction to AI, Machine Learning & Deep Learning” and “Deep learning Part 2: Vision Algorithms”

or

2. Engineers with some deep learning experience but not LSTMs.  Students should have trained a CNN in keras/tensorflow and understood how the individual layers worked.

What you need to bring:

Students need to bring a laptop.  We have detailed setup instructions at https://github.com/lukas/ml-class/blob/master/README.md

Take aways:

-Practical high-level knowledge of how RNNs, LSTMs GRUs actually work.

-How to build multi layer LSTMs

-How to use popular vector embeddings like word2vec

Curriculum:

Morning: Introduction to RNNs, LSTMs

9:00 – 10:00 Breakfast, Laptop Setup and deep learning overview

10:00 - 11:00 High-level overview of RNNs, LSTMs, word vector encodings and how they work

11:00- 12:00 Train a text generating model in keras/tensorflow and run on sample data.

12:00-1:00 Lunch

Afternoon: Applications

1:00-2:00 Build a text classification model

2:00-3:00 Apply text classification model to other domains (sentiment analysis, search relevance)

3:00-4:00 Introduction to audio processing

4:00-5:00 Build a music generating model in keras/tensorflow

Testimonials and Feedback

"I found it to be really engaging and interesting. I was already familiar with some ML concepts, so it helped me understand them better and think about how to apply them. The code samples are really great and will definitely reference them in the future. I thought the class went at a generally good pace."

"Good experience - full of great resources and discussion. Good, practical intro for new folks, and also valuable for those familiar with the basics. I walked away excited to experiment!"

“Class was great, you ticked off my curiosity. I am excited to review the content and retry it by myself. Thank you for encouraging peer to peer collaboration and making the effort to build the slack channel. I think it was nice to see you debug live.”

Corporate Training:

We also do corporate trainings - send us an email for details.