(Subsections here: Overview, Schedule, Address, Maps, Past Sessions)
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Date: Saturday, October 14, 2017
Overview: Data Science Camp is SF Bay ACM’s annual event combining sessions, keynote, and optional tutorial (extra fee). It’s an excellent opportunity to increase your experience in Data Science and connect with others. We keep it near-free ($10 charge, includes lunch & coffee), now running in its eighth year. You can also sign up as part of a group of 2-6 people for $8 per person. The morning class is $60 and includes the afternoon camp.
8:00 am – 8:40 Arrive, register for class, network, coffee
8:40 am – 10:40 Class: AI and Deep Learning with Python and Keras ($60, includes full day)
10:30 am – 11:00 People coming for just the Camp ($10) arrive, register and network
11:00 am Camp Kickoff
Major Sponsor 5 min presentations
Keynote Presentation, 50 min (call for speaker proposals)
12:25 Session Proposals (30 sec description, count audience hands, assign to a room for that sized audience)
1:15 Lunch, post Session Matrix (4 time slot rows by 4-7 room columns)
2:00 – 2:50 Session 1 (over all the rooms used, likely subdivide the main room that seats 410)
3:00 – 3:50 Session 2
afternoon coffee and snacks
4:00 – 4:50 Session 3
5:00 – 5:50 Session 4
6:00 – 6:30 Session Summary, in the largest part of the main room
6:45 All audience should be out of the building
PayPal Town Hall
2161 North 1st Street
San Jose, CA 95131
See map: Google Maps
Past Years Sessions (as example content):
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Time and Cost:
The class will be from 8:40 – 10:40am, with a $60 charge which includes the afternoon camp. For just the Camp, from 10:40am and the rest of the day is $10 or less. Registration is on Eventbrite.
Title: AI and Deep Learning with Python and Keras
Speaker: Bhairav Mehta is Senior Data Scientist at Apple and founder of DataInquest, which gives data science training
What Will I Learn?
- To describe what Deep Learning is in a simple yet accurate way
- To explain how deep learning can be used to build predictive models
- To distinguish which practical applications can benefit from deep learning
- To install and use Python and Keras to build deep learning models
- To apply deep learning to solve supervised and unsupervised learning problems involving images, text, sound, time series and tabular data.
- To build, train and use fully connected, convolutional and recurrent neural networks
- To look at the internals of a deep learning model without intimidation and with the ability to tweak its parameters
- To train and run models in the cloud using a GPU
- To estimate training costs for large models
- To re-use pre-trained models to shortcut training time and cost (transfer learning)
- Knowledge of Python, familiarity with control flow (if/else, for loops) and pythonic constructs (functions, classes, iterables, generators)
- Use of bash shell (or equivalent command prompt) and basic commands to copy and move files
- Basic knowledge of linear algebra (what is a vector, what is a matrix, how to calculate dot product)
- Use of ssh to connect to a cloud computer
This training is designed to provide a introduction to Deep Learning using Keras. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems.
We start with a review of Deep Learning applications and a recap of Machine Learning tools and techniques. Then we introduce Artificial Neural Networks and explain how they are trained to solve Regression and Classification problems.
We introduce and explain several architectures including Fully Connected, Convolutional and Recurrent Neural Networks, and for each of these we explain both the theory and give plenty of example applications.
The goal is to provide students with a strong foundation, not just theory, not just scripting, but both. At the end of the course you’ll be able to recognize which problems can be solved with Deep Learning, you’ll be able to design and train a variety of Neural Network models and you’ll be able to use cloud computing to speed up training and improve your model’s performance.
Who is the target audience?
Software engineers who are curious about data science and about the Deep Learning buzz and want to get a better understanding of it
Data scientists who are familiar with Machine Learning and want to develop a strong foundational knowledge of deep learning
- Machine Learning and Deep Learning Introduction
- Gradient Descent
- Neural Network
- Convolution Neural Networks
- Recurrent Neural Network
- Keras Intro along with other libraries like Theano, Tensorflow, DL4J, MXnet etc.
- Set up your environment and AWS powered GPU based Jupyter Notebook
- Identify Keras and other Deep Learning libraries are installed
- Load image data from MNIST and CIFAR.
- Preprocess input data for Keras.
- Preprocess class labels for Keras.
- Define model architecture.
- Compile model.
- Fit model on training data.
- Evaluate model on test data.
- Load Time Series data from Airline Passenger Data
- Define RNN model Architecture
- Fit RNN model
- Evaluate model on test data
- Improve Performance
- Track performance
- Some demonstrations of AI based technologies
Bhairav Mehta is Senior Data Scientist with extensive professional experience and academic background. Bhairav works for Apple Inc. as Sr. Data Scientist.
Bhairav Mehta is experienced engineer, business professional and seasoned Statistician / programmer with 19 years of combined progressive experience working on data science in electronics consumer products industry (7 years at Apple Inc.), yield engineering in semiconductor manufacturing (6 years at Qualcomm and MIT Startup) and quality engineering in automotive industry (OEM, Tier2 Suppliers, Ford Motor Company) (3 years). Bhairav founded a start up DataInquest Inc. in 2014 that is specialized in training/consulting in Artificial Intelligence, Machine Learning, Blockchain and Data Science.
Bhairav Mehta has MBA from Johnson School of Management at Cornell University, Masters in Computer science from Georgia Tech (Expected 2018), Masters in Statistics from Cornell University, Masters in Industrial Systems Engineering from Rochester Institute of Technology and BS Production Engineering from Mumbai University.
CALL FOR KEYNOTE SPEAKER
We are currently seeking a Keynote speaker for a 50 minute segment to address our full audience, which could be up to 410 data scientists. If you would like submit a proposal please complete the form below. For details about our local ACM chapter, see the next tab, “Sponsors”. For examples of prior year’s keynote speakers, see the bottom of this section.
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We welcome advance session proposals on any data-science-related topic (data mining, algorithms, big data, Apache open source, case studies,..). A session may be slides, panel, have a software demo component. There are 4 time slots (2pm, 3pm 4pm, 5pm) x 6 rooms. To submit a proposal, please authenticate (so we know you’re human and not a spammer), then click ‘Submit’.
For further session Guidelines, scroll down below the form.
- Anabolizantes Estimulantes y Calmantes En La Practica Deportiva pdf, epub, mobi
- 2 months, 3 weeks ago
You must be logged in to create new topics.
Guidelines & Schedule:
Proposing sessions in advance is encouraged, to attract people to attend specific talks. The voting on the web gives guidance on the popularity of a talk and gives the audience an advance idea of their 4 favorite talks. Talks can be proposed the day of the event.
Please provide in your session proposal:
- Session title
- Description of your session: Include any related links for the content (i.e. Blog, Slideshare, github, paper, Kaggle competition)
- Bio: Linkedin, previous presentations or other work, company/organization and role are all helpful. If you have a panel or multiple people, providing background on the people helps.
- Tags (comma separated, can have space between words in a phrase)
- Session type: presentation, panel, demo, discussion
- Audience level: beginner, intermediate, advanced
- Any software components: Python, TensorFlow, Spark, Kafka, R, Docker, Cloudera
- Any algorithm components: deep learning, LSTM, Convolutional, SVD, RandomForests
- Any related vertical market or broad application space: finance, security, social media, health care
- Others that make sense
- People can vote in advance on the web. We recommend you limit votes to 4, because there are 4 session time slots you can attend. However, assigning rooms (seating 20-160) is determined the day of the event with a 30-second pitch to the audience. We will get a rough count on a show-of-hands on the day. We will merge similar proposals.
- Talks must be technical or educational focused on audience take-aways that do not require a purchase. No sales pitches; no commercial product demos.
- You should have enough material to cover 45-50 minutes (the length of a session).
- If you only have enough material for 1/2 or 1/3 of a session, let us know and we will do our best to combine your session with a related one, or other short sessions.
- It may be possible to present remotely (Skype/ WebEx/ Zoom), by arrangement. Please submit the session above. You must have a local person to propose the session, to go to the session and provide technical setup, and to moderate the session (repeating audience questions to the speaker as needed).
- If you want to use a video, please have it on your device.
- Please bring a video adapter/ converter/ dongle, USB key in case you need to change laptop, charger, power cord. Please show up 5 min early for your talk to allow time for connecting laptop, projector, testing video etc.
- Slides should be legible. Please no fonts under 14pt. If you want to estimate the readability of your slides from the middle of a large room, print 6 slides on a page, and put it next to a newspaper.
- We invite one or more “session note takers” to add comments or notes to the session discussion thread, which can be reviewed in the session summary at the end of the day.
- If the speaker has note provided a link to their final slides or content, we invite them to add a link to the discussion thread for their session before the session summary at 6pm.
- 12:25-1pm Session proposals and voting. We then allocate room sizes based on expected audience sizes from vote counting.
- 1:30pm Session matrix will be posted online (over lunch)
- 2-2:50pm, 3-3:50pm, 4-4:50pm, 5-5:50pm. Speakers must show up and be on time for their talks. Please give us feedback about no-show or unprepared speakers.
- 6pm Session Summary, with ~1-2 min overview of each session, providing feedback, lessons learned and suggestions for 2018 Data Science Camp.
- (see the [Overview] tab for the full schedule)
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The San Francisco Bay Area ACM (SFbayACM) is a local professional chapter of the ACM. We are a 501c(3) non-profit, run by unpaid volunteers. We were founded in the bay area in 1957. We have about 20-25 events per year, about 6k people active in our Meetup group, coming to our two talks a month, on Data Science (data mining, big data) and General Computing. We post about 120 of our more recent talks on our YouTube channel. Let us know if you have a regular audience for live streaming of our talks.
The Association of Computing Machinery (ACM) is the largest professional computing society, publishing journals and hosting conferences among other activities. The ACM was founded in the bay area in 1947.
If you would like to discuss being a sponsor, fill out the form below and make sure to include available times and contact details.
|PayPal – Who we are
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We offer an accredited, convenient, and attractively priced alternative to degree programs, serving the advanced professional education needs of Silicon Valley and beyond. Each year, more than 10,000 adults who live and work in the greater South Bay area study here to earn University of California certified credentials that are widely recognized in a range of industries. We are the region’s leading educator of professionals in more than 40 areas of expertise that are in high demand among Silicon Valley employers.
KDD provides the premier forum for advancement and adoption of the “science” of knowledge discovery and data mining. KDD encourages: