Feeds: iCal | RSS | JSON (as of )
Login | Need an account?
Hacker Dojo has a new Entrance on the left side of the building. Follow the signs to the new Entrance!
Our regular visiting hours are back 10AM to 9PM 7 days a week! Thanks for your patience.

Overview of Build & Operate Deep Learning Data Pipeline & Data Lake Cloud/Container Cluster with TensorFlow, Spark & Hadoop in GUI/AP/CLI

Event status: Suspended
This event will not appear on the public calendar until it has been approved. If this does not happen soon, contact [email protected] for assistance.

Setup Time:
15 minutes
Saturday, April 22, 2017 at 10:00am
Saturday, April 22, 2017 at 1:00pm
Teardown Time:
15 minutes
Estimated size:

Follow our AI Big Data Cloud Thinktank @ClouDatAI

PB-Scale AI Big Data Cloud Boot Camp Overview Slides

Fog Computing/Cloud Computing, Serverless Computing/Cloud-Native Computing, BlockChain/Bitcoin, Lambda Architecture, Microservices-oriented Architecture/monolithic architecture, Immutable Datalake, Real-time Data Pipeline, Container/VM/Bare Metal, IaaS/PaaS/SaaS, Machine Learning/Deep Learning, Supervised Learning/Unsupervised Learning, Big Data/Deep Learning, Hadoop/Spark, YARN/Mesos, Docker Engine/Kubernetes, OpenStack, SQL/NoSQL/HDFS, GUI/CLI/API, Hyper-scale/Hyper-convergence, SDN/NFV, GPU/CPU/TPU, File Storage/Object Storage/Block Storage, and much more. So are you feeling you are lost in the jungle of fast-pacing tech frontier? We Are Here to Help You to Get Out of It and Lead instead of Follow It!

You go to a lot of trainings and/or meetups, whether free or not, expensive or cheap, ALL of those are either marketing fluff, sales pitches, or short of global pictures, or short of details, no insight, let alone foresight. Our 2-day Boot Camp is radically different, vendor agnostic, no strings attached, full of meat, lots of hands-on, offering you both macro & micro perspective of the state-of-the-art in practical way with hindsight, insight and foresight!
What you'll learn, and how you can apply it

Learn how Machine & Deep Learning AI Big Data Container enables data scientists to help companies reduce costs, increase profits, improve products, retain customers, and identify new opportunities
Topics include:
How to identify potential business use cases in leveraging big data container AI technology
How to obtain, clean, and combine disparate data sources to create a data pipeline for data lake
What Machine-Learning (Shallow Learning) & Deep Learning technique to use for a particular data science project
How to conduct PoC & productionalized big data projects in cloud/container cluster at scale
How to create real-time data pipelines using the latest open source with public cloud or private cloud/container, ingest data in real time and at scale, process the data in real-time/interactive/batch, and build data products from real-time data sources
How to combines ETL, batch analytics, real-time stream analysis with machine learning, deep learning, and visualizations through both data pipeline & data lakes
Understand & master TensorFlow's fundamentals & capabilities
Explore TensorBoard to debug and optimize your own Neural Network Architectures, train, test, validate & serve your models for real-life Deep Learning applications at Scale

Agenda (Subject to Change at Anytime without Notice) - 50% Lecture, 50% Hands-On, Vendor Agnostic, No Strings Attached, You Working on a Cluster instead of only an Instance in cloud, True PB-Scale Depends on Your Own Cloud Budget (could be outstanding) as opposed to Free Trial Limited Budget

Day 1
10:00 AM - 10:50AM Elastic Cloud Computing and Scalabe Big Data AI: What, Why and How?

11:00 AM - 11:50AM Deep Dive into Public/Private/Hybrid Cloud Infrastructure: Elastic/Plastic Cloud; Bare Metal/VM/Container; IaaS/PaaS/SaaS; Hyper-Scale/Hyper-Convergence; From Linux Kernel to Distributed System's CAP Theorem; OpenStack as the De facto Private Cloud; Capacity Planning & Auto-scaling Challenges of Cloud; Micro-service-based Immutable Architecture

12:00 AM - 12:50AM Deep Dive into Big Data Technology Stack: Nature of Big Data - Structural/Unstructural; Hot/Warm/Cold; Machine/Human; Text/Numerical, SQL(ACID)/NoSQL(BASE); Batch(Hindsight)/Interactive (Insight)/Streaming(Foresight); Data Pipeline & Datalake; Hadoop/Spark/Kafka/HDFS/HBase/HIVE/ZooKeeper

1:00 PM - 1:50M Lunch Session (Lunch included, Veggie option available): Google/AWS Cloud|Docker/CoreOS Container In-Depth: Computation/Storage/Networking Models

2:00PM - 6PM Hands-on I: I Set Up & Test Drive Your Own AI Big Data Google/AWS Cloud|Docoker/CoreOS Container Cluster (Hadoop, Spark, Kafka, HDFS, Tensorflow) : Using Spark/Hadoop for Word Counting of Twitter Data/Kafka Stream of system logs

Day 2
10:00 AM - 10:50AM Practical Machine Learning In-Depth: Feature Engineering, From Regression to Classification, 5 Tribes of Machine Learning: Symbolists with Inverse Deduction of Symbolic Logic, Connectionists with Backpropagation of Neural Networks, Evolutionaries with Genetic Programming, Bayesians with Probabilistic Inference in Statistics, Analogizers with Support Vector Machines; Supervised Learning (Classification/Regression), Unsupervised Learning (Clustering), Semi-Supervised Learning; Data Ingestion & Its Challenges, Data Cleansing/Prep-processing; Training Set/Testing Set Partitioning; Feature Engineering (Feature Extraction/Selection/Construction/Learning, Dimension Reduction); Model Building/Evaluation/Deployment|Serving/Scaling|Reduction/Optimization with Prediction Feedbacks

11:00 AM - 11:50AM Practical Deep-Learning-based AI In-Depth: Weak/Special AI vs Strong/General AI; Key Components of AI: Knowledge Representation, Deduction, Reasoning, NLP, Planning, Learning,Perception, Sensing & Actuation, Goals & Problem Solving, Consciousness & Creativity; Rectangle of Deep Learning, Shallow Learning, Supervised Learning, and Unsupervised Learning; Basic Multi-layer Architecture of Deep Forward/Convolutional Neural Networks(FNN/CNN)/Deep Recurrent Neural Networks(RNN)/Long short-term memory(LSTM): Input/Hidden/Output Layers, Weights, Biases, Activation Function, Feedback Loops, Backpropagation from Automatic Differentiation and Stochastic Gradient Descent (SGD); Convex/Non-Convex Optimization; Ways of Training Deep Neural Networks: Data/Model Parallelism, Synchronous/Asynchronous Training, Variants of SGD, Gradient Vanishing/Explotion, Loss Function Minimization/Optimization with Dropout/Regulariztion & Batch Normalization & Learning Rate & Training Steps, and Unsupervised Pre-training (Autoencoder etc.); Deep Learning Applications - What's Fit and What's Not?: Deep Structures, Unusual RNN, Huge Models

12:00 AM - 12:50PM Embracing Paradigm Shifting from Algorithm-based Rigid Computing to Model-based Big Data Cloud IoT-powered Deep Learning AI for Real-Life Problem Solving: What, Why and How? - Problem Formulation, Data Gathering, Algorithmic & Neural Network Architecture Selection, Hyperparameter Turning, Deep Learning, Cross Validation, and Model Serving

1:00 PM - 1:50PM Lunch Session (Lunch included, Veggie option available) - Tensorflow In-Depth: The Origin, Fundamental Concepts (Tensors/Data Flow Graph & More), Historical Development & Theoretical Foundation; Two Major Deep Learning Models and Their TensorFlow Implementation: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN); GPU/Tensorflow vs. CPU/NumPy; TensorFlow vs Other Open Source Deep Learning Packages: Torch, Caffe, MXNet, Theano: Programming vs. Configuration; Tackling Deep Learning Blackbox Puzzle with TensorBoard

2:00PM - 6PM Hands-on I Continued: I Set Up & Test Drive Your Own AI Big Data Google/AWS Cloud|Docker/CoreOS Container Cluster (Hadoop, Spark, Kafka, HDFS, Tensorflow) : Using Spark/Hadoop for Word Counting of Twitter Data/Kafka Stream of system logs

Hands-on II (Only for Advanced Attendeeds): Build, Train & Serve Your Own Chosen AI Application Using Python in Your Own Scalable AI Big Data Google/AWS Cloud|Docker/CoreOS Container Cluster (TensorFlow, Spark, Hadoop, Kafka, HBase, HIVE, Zookeeper)

Who Should Attend:

CEO, SVP/VP, C-Level, Director, Global Head, Manager, Decision-makers, Business Executives, Analysts, Project managers, Analytics managers, Data Scientist, Statistian, Sales, Marketing, human resources, Engineers, Developers, Architects, Networking specialists, Students, Professional Services, Data Analyst, BI Developer/Architect, QA, Performance Engineers, Data Warehouse Professional, Sales, Pre Sales, Technical Marketing, PM, Teaching Staff, Delivery Manager and other line-of-business executives

Statisticians, Big Data Engineer, Data Scientists, Business Intelligence professionals, Teaching Staffs, Delivery Managers, Product Managers, Cloud Operaters, Devops, System admins, Business Analysts, Financial Analysts, Solution Architects, Pre-sales, Sales, Post-Sales, Marketers, Project Managers, and Big Data Cloud AI Enthusiasts.

Hands-on Requirements:
1) Each student should bring their own 64bit Linux-based or Windows with Putty installed laptop (no VM required as we are using cloud) with Minimum 8GB RAM and Free 0.5TB hard disk with administrative/root privileges and wireless connectivity.

2) Google/AWS Cloud account ready or Docker/CoreOS Container pre-installed in your laptop (We provide WiFi access for you)
3) It's better but not necessry to bring your own WiFi hotspot

4) Reasonable Bash or Python skill

Worldwide Programs seeking global partners:

Hacker Dojo members are welcome and free to attend

www.tinyurl.com/AIBootCampX - Seeking worldwide partners
www.tinyurl.com/AIBootCamp1 - 4/1-2 at Sofia University
www.tinyurl.com/AIBootCamp2 - 4/15-16 at Sofia University
www.tinyurl.com/AIBootCampLA - 4/8-8 at LA
www.tinyurl.com/AIBootCampWA - 4/8-8 at Seattle
www.tinyurl.com/AIBootCampNYC - 4/8-8 at NYC

Follow AI Big Data Cloud Thinktank @ClouDatAI now