Technical Introduction to AI, Machine Learning & Deep Learning @ San Francisco, CA [28 July]

Technical Introduction to AI, Machine Learning & Deep Learning

09:00 - 19:00

 Facebook event page
San Francisco
“Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years.” — Mark Cuban

Machine learning is changing the world and it’s not going to stop. Leading companies like Google, Amazon, and Facebook are betting their futures on AI. Organizations of all kinds can’t hire machine learning engineers fast enough. The world needs more people that understand machine learning and our goal is to get you started on that path as efficiently as possible. While there are plenty of online resources, we know it's tough to learn a technical topic without a teacher. This workshop should arm you with the tools to get started using machine learning in your day job and the resources to find additional help if you want to go deeper.

The course is expertly designed to leave you with the ability to take training data, do feature selection and actually build models for applications like content categorization, sentiment analysis, and image recognition. By the end of the day, students will be able to use models in their day-to-day work. You will also walk away with a high-level understanding of how common models such as Deep Neural Networks, SVMs, Logistic Regression and Naive Bayes work and when to use them.

Technologies Introduced:
— Scikit-learn
— Numpy
— Jupyter

Intro to Machine Learning Platforms:
— Azure ML
— Amazon ML

Intro to Deep Learning:
— TensorFlow
— Keras


We try to make this class as accessible as possible. Some proficiency with Python is necessary. If you can open up a Jupyter notebook and install requisite software that’s helpful but we’ll also cover how to do that quickly in the beginning.

What will be provided:

We will provide all the food, drinks & coffee your heart desires as well as provide the leave behind tools and resources for your continued success.

What you need to bring:
You must also bring your own laptop (don’t forget your charger). If you bring a laptop with a GPU that supports CUDA (for example a MacBook with Mac OS X 10.11 or later), we’ll see if we can make it GPU accelerated.


Lukas Biewald is the founder of CrowdFlower, an Artificial Intelligence company that works with data science teams at Google, Bloomberg, Facebook and hundreds of other organizations to make machine learning work in the real world. Prior to that, Lukas was the first data scientist at Powerset (Acquired by Microsoft and rebranded as Bing) and a scientist at Yahoo!, Lukas was shipping machine learning algorithms to hundreds of millions of users.

Lukas frequently teaches invited Machine Learning workshops with Galvanize, O’Reilly and ODSC. He was a TA for Stanford’s machine learning class in 2003. He is a frequent contributor to Computerworld, Forbes and O’Reilly and has presented at the best-known machine learning-related academic conferences such as AAAI, SIGIR, ACL and EMNLP. He’s had the honor of being in Inc’s annual 30 under 30 and was also a finalist at TechCrunch Disrupt.


9:00 – 10:00 Breakfast and Install Requisite Software
We always take it as a personal challenge to get the prerequisite machine learning software installed on everyone’s laptop. We can all learn to uplevel our unix-fu by helping each other get set up.

10:00 – 12:00 Build a Sentiment Classifier From Scratch
Everyone builds a Twitter sentiment classifier using scikit-learn. We try multiple feature selection approaches and multiple model types. We learn some common tricks for actually making machine learning effective in the real world.

12:00-1:00 Lunch and History/Theory of Machine Learning
Eat lunch and for your dining entertainment, Lukas will introduce a little math, stats and history of how machine learning got to where it is today.

1:00-2:30 Try the Common Machine Learning Platforms
These days, there are many excellent, low-cost machine learning platforms. We will try rebuilding our sentiment classifier on two of the most common: Microsoft Azure ML and Amazon ML. If students want to try Google Predict, Salesforce Einstein or IBM Watson we can do that too.

2:30-3:00 Break and Q&A
We can discuss other applications of this technology and look at how it might apply to real-world tasks that students may be working on.

3:00-5:00 Introduction to TensorFlow and Deep Neural Networks
We will learn how deep neural networks work and actually build one! If you bring a laptop with a GPU that supports CUDA (for example a MacBook with Mac OS X 10.11 or later), we’ll see if we can make it GPU accelerated.
We’ll all build a network to do handwritten digit recognition.

5:00-5:30 Wrap-up and Q&A
We will finish up and discuss how to apply this knowledge directly to problems that we actually face in our jobs.
5:30-7:00 Drinks & Networking
We’ll bring together top entrepreneurs, tech executives & engineers to connect with and learn from. Plus, this is a chance to meet your classmates and teachers in an informal and fun setting.

Corporate Training:

Interested in bringing this training into your company? We can host custom & private 1 or 2 day seminars as well as a 14 week deep dive into machine learning on your campus.

This is ideal for organizations looking to build deep learning expertise in-house & want to customize our courses to fit your business needs.
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