Global AI Big Data Cloud IoT Boot Camp Partners Wanted
Sat Mar 11 00:11:00 GMT 2017


Would you like to be our Global 2-day AI Big Data Cloud IoT Boot Camp partner in your area?
If you can find 30 or plus attendees, provide venue, food, drinks and all other necessary logistics, then we're happy to split the total revenue evenly with you by sending our top-niche industry practitioner as instructor to your area and mission accompolished!
We have 20 or so advanced Boot Camps like this one:
-  Deep Learning Cloud/Container Boot Camp: Build & Operate End-to-End Data Pipeline & Data Lake with TensorFlow, Spark & Hadoop in GUI/API(Python)/CLI(Bash) 
 - ( RSVP for 3/11-12 Silicon Valley Event)                       
 - (RSVP for 4/15-16 Silicon Valley Event)
 - anytime anywhere for a group of 30 - Be our global partner!)
 - - 60-page Boot Camp Overview Slides

BTW this is a one-time email, no more if you keep silient 

Below is the detailed introduction of the above Boot Camp, if you are interested in working with us, please let us know

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 business pictures, or lack of technical 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-ons, 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 Cloud 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 cloud 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

Detailed agend is being listed at and also enclosed here:

genda (Subject to Change at Anytime without Notice) - 50% Lecture, 50% Hands-On, Vendor Agnostic, No Strings Attached, You Working on a Cloud/Container Cluster instead of only an Instance/single machine in Cloud/your laptop

Day 1
8:00 AM - 8:50AM Elastic Cloud Computing and Scalabe Big Data AI: What, Why and How?

9:00 AM - 9:50AM 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

10:00 AM - 10:50AM 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/HBas/HIVE

11:00 AM - 11:50AM Google/AWS Cloud|Docker/CoreOS Container In-Depth: Computation/Storage/Networking Models

12:00 PM - 1:00PM Lunch Break (Lunch included, Veggie option available)

1:00 PM - 5PM Hands-on: I Set Up & Test Drive Your Own AI Big Data Google/AWS Cloud|CoreOS Container Cluster (Hadoop, Spark, Kafka, HDFS, HBase, HIVE, Tensorflow) : Using Spark for Real-time Word Counting from Kafka Stream of system logs; for Supervised Learning: Regression (Linear) & Classification - Logrithic Regression, Support Vector Machine(SVM), Decision Tree, Random Forest, Naive Bayes, Gradient Boost Tree; for Unsupervised Learning: Clustering using K-Means, Dimension Reduction using Princple Component Analysis (PCA), Dimention Reduction using SVD (Single Value Decomposition); for Recommendation Systems: Collaborative filtering using both implicit & explicit feedback

Day 2
8:00 AM - 8:50AM 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

9:00 AM - 9:50AM 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

10:00 AM - 10:50PM 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

11:00 AM - 11:50AM 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

12:00 PM - 1:00PM Lunch Break (Lunch included, Veggie option available)

1:00PM - 5PM Hands-on II: Architect, Design & Develop (Modeling/Training -> Inferencing/Testing) Your Own Chosen AI Application Using Python in Your Own Scalable AI Big Data Google/AWS Cloud|CoreOS Container Cluster (Hadoop, Spark, Kafka, HBase, HIVE, Tensorflow)

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) Own wireless connection (hot spot)

3) Google/AWS Cloud account ready|Pre-installed Docker/CoreOS in your laptop

4)  Reasonable Bash or Python

Forbes Z 
Deep Learning Cloud/Container Boot Camp: Build & Operate End-to-End Data Pipeline & Data Lake with TensorFlow, Spark & Hadoop in API (Python)/CLI(Bash)
- ( RSVP for 3/11-12)             
- (RSVP for 4/15-16)
- (RSVP for Anytime Anywhere with Group of 30)
- - 60-page Boot Camp Overview Slides
@ClouDatAI for Latest Boot Camp Update - 1M Tweets/Yr., 2.6M Tweets so far
Cloudata Inc - DAOing Your AI Big Data Cloud IoT!

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