CIFAR DLRL 2020 Summer School

Provides an intuitive explanation for what deep learning models are doing when they find an 'efficient feature representation'
Full course playlist here: https://www.youtube.com/playli....st?list=PLnrO0TOwDbu

Non-technical friendly course explaining machine learning and deep learning that everyone can understand.

Full course playlist here: https://www.youtube.com/playli....st?list=PLnrO0TOwDbu

We're going to build a variational autoencoder capable of generating novel images after being trained on a collection of images. We'll be using handwritten digit images as training data. Then we'll both generate new digits and plot out the learned embeddings. And I introduce Bayesian theory for the first time in this series :)

Code for this video:
https://github.com/llSourcell/....how_to_generate_imag

Mike's Winning Code:
https://github.com/xkortex/how...._to_win_slot_machine

SG's Runner up Code:
https://github.com/esha-sg/Int....ro-DeepLearning-Sira

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2 things
-The embedding visualization at the end would be more spread out if i trained it for more epochs (50 is recommended) but i just used 5.
-The code in the video doesn't fully implement the reparameterization trick (to save space) but check the GitHub repo for details on that.

More Learning resources:
https://jaan.io/what-is-variat....ional-autoencoder-va
http://kvfrans.com/variational....-autoencoders-explai
http://blog.fastforwardlabs.co....m/2016/08/12/introdu
http://blog.fastforwardlabs.co....m/2016/08/22/under-t
http://blog.evjang.com/2016/11..../tutorial-categorica
https://jmetzen.github.io/2015-11-27/vae.html

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Deep learning currently provides state-of-the-art performance in computer vision, natural language processing, and many other machine learning tasks. In this talk, we will learn when deep learning is useful (and when it isn't!), how to implement some simple neural networks in Python using Theano, and how to build more powerful systems using the OpenDeep package.

Our first model will be the 'hello world' of deep learning - the multilayer perceptron. This model generalizes logistic regression as your typical feed-forward neural net for classification.

Our second model will be an introduction to unsupervised learning with neural nets - the denoising auto-encoder. This model attempts to reconstruct corrupted inputs, learning a useful representation of your input data distribution that can deal with missing values.

Finally, we will explore the modularity of neural nets by implementing an image-captioning system using the the OpenDeep package.

Markus Beissinger
Recent graduate from the Jerome Fisher Program in Management and Technology dual degree program at the University of Pennsylvania (The Wharton School and the School of Engineering and Applied Science), and current Master's student in computer science. Focus on machine learning, startups, and management.

Slides: http://goo.gl/P9QGnV

0) Introduction 00:00
1) Visual Recognition with a Fashion Dataset 2:30
2) Pneumonia Detection using CNNs 12:12
3) Using Cycle-GAN for Changing the genre of a music track 21:33
4) Earthquake Forecasting using Deep Learning 31:23
5) Detecting Malaria 40:54
6) A Neural Network for Fundamental Analysis 49:46

Class project presentations by students in the Stat 453: Introduction to Deep Learning class. See the titles and chapter bookmarks below.

(This video replaces a previous upload with the same title where the last talk was accidentally truncated.)

What makes a system-support machine learning model development? Find out how to transit yourself into the machine learning engineering domain, with Chi Wang, the co-author of the book 📖 Engineering Deep Learning Systems 📖

This video is an excerpt from "What Developers Need to Know to Design Machine Learning Systems" - a live-streaming session by Chi Wang. To watch the full video, visit http://mng.bz/BZr8.

📚📚📚
Engineering Deep Learning Systems | http://mng.bz/lR58
To save 40% off this book use discount code: watchchiwang40
📚📚📚

About the book:
"Engineering Deep Learning Systems" teaches you to design and implement an automated platform to support creating, training, and maintaining deep learning models. In it, you’ll learn just enough about deep learning to understand the needs of the data scientists who will be using your system. You’ll learn to gather requirements, translate them into system component design choices, and integrate those components into a cohesive whole. A complete example system and insightful exercises help you build an intuitive understanding of DL system design.

🤖💡Delving into the Depths of Deep Learning.

🔍 In this video, learn about the captivating world of Deep Learning, where algorithms evolve and AI reaches new heights.

📚 The Basics of Deep Learning
🧠 Neural Networks
📊 Deep Learning Applications
🌍 Real-world Impacts and Transformations
🚀 Future Frontiers in Deep Learning

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eep learning networks are mechanical systems which “learn” by modeling high-level abstractions in datasets, and cycling through trial-and-error guesses with feedback, to establish an optimally-weighted system that can make accurate predictions about new data. LSTM RNNs (long short term memory Recurrent Neural Nets) is a deep learning method that overcomes some of the limitations in classical time series forecasting (non-linearity, fixed-time windows, time lag specification, and multivariate forecasting). Hence, deep learning is emerging as an important forecasting technique for coping with exponentially growing data, and the ability to expediently process and direct these data. In the future, deep learning smart networks might provide an advanced computational infrastructure for tackling real-time predictive data science problems such global health monitoring, energy storage and transmission, and financial risk assessment. Slides: https://www.slideshare.net/lab....logga/deep-learning-

This video is a VERY basic introduction to deep learning. I do not delve too deeply into the specifics, rather this video is meant as a cursory overview of the concept of deep learning and how it works. In later videos, we will explore the terms introduced in this video much more.

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A brief introduction into the concepts behind deep learning.

Github repo:
https://github.com/mlberkeley/intro-dl-workshop

Intro to Deep Learning - Part 1

In this video, I introduce the fundamentals of deep learning with a real-world scenario:
📌 Predicting how much it will rain tomorrow (precipitation).

Suitable for beginner to intermediate practitioners in machine learning and AI.

🔍 Topics Covered in Part 1:
1. Scenario: How can we predict precipitation?
2. Data-Driven Approach:
- Adding more variables (features)
- Nearest neighbor
- Mapping vectors to real numbers
3. Machine Learning:
- Random search

💡 Coming in Part 2:
3. Machine Learning:
- Derivative
- Gradient Descent
4. Conclusion and Summary

Welcome to the first video in my Deep Learning With Pytorch playlist!

In this video I'll talk a little bit about what a Neural Network is, and give you a little bit of background history on Pytorch vs. Tensorflow.

Then we'll set up a development environment using Google Colab, and create a first notebook and push it to a Github repository.

#pytorch #codemy #JohnElder

Timecodes

0:00​​ - Introduction
1:12 - Pytorch vs. Tensorflow
2:32 - What is a Neural Network?
4:10 - How Much Math Do You Need?
7:14 - Pytorch Documentation
7:37 - Development Environment Google Colab
9:09 - Use GPU Runtime
10:24 - Check Latest Version of Pytorch
12:04 - Set Up Github Repository
13:36 - Authorize Colab In Github
14:35 - Push Notebook To Github
16:09 - Conclusion

Sebastian's books: https://sebastianraschka.com/books/
The lecture slides are available at: https://github.com/rasbt/stat4....53-deep-learning-ss2

Introduction to Deep Learning and Generative Models (Spring 2020). Optional lecture 6.5, explaining how to use Google Colab for Jupyter notebooks with a free GPU.

Prepare for the course