Howdy,
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)
- www.tinyurl.com/AIBootCamp ( RSVP for 3/11-12 Silicon Valley Event)
- www.tinyurl.com/AIBootCamp2 (RSVP for 4/15-16 Silicon Valley Event)
- www.tinyurl.com/AIBootCamp2X(RSVP anytime anywhere for a group of 30 - Be
our global partner!)
- www.hwswworld.com/aibootcampoverview3.pdf - 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 www.hwswworld.com 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
CLO
Deep Learning Cloud/Container Boot Camp: Build & Operate End-to-End Data
Pipeline & Data Lake with TensorFlow, Spark & Hadoop in API (Python)/CLI(Bash)
- www.tinyurl.com/AIBootCamp ( RSVP for 3/11-12)
- www.tinyurl.com/AIBootCamp2 (RSVP for 4/15-16)
- www.tinyurl.com/AIBootCampX (RSVP for Anytime Anywhere with Group of 30)
- www.hwswworld.com/aibootcampoverview3.pdf - 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!
