San Francisco Professional Events List

Intro Build/Operate Deep Learning Data Pipeline/Lake Cloud/Container Frontier

Apr 01, 2017 - 02:00 PM
Cloudata Inc
Hacker Dojo
3350, Thomas Rd

Large Event RoomSanta Clara,
ZIP: 95054

Let's blow your bind in this 2-hr session, you wont regret it in spending weekend afternoon with us at AI Big Data Cloud/Container Frontier!

Overview of the following 2-Day Boot Camp

Build & Operate Deep Learning Data Pipeline & Data Lake Cloud/Container Cluster with TensorFlow, Spark & Hadoop in API (Python) or CLI (Bash)

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


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.


Sales end on April 1

Regular $25.00 (+$2.37 Fee)

Group of 2 Rate $30.00



Agenda (Subject to Change at Anytime without Notice) - 50% Lecture, 50% Hands-On, Vendor Agnostic, No Strings Attached, You Working on a Container Cluster instead of only an Instance in cloud

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 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 Docker/CoreOS Container Cluster (Hadoop, Spark, Kafka, HBase, HIVE, Tensorflow)

Event Categories
Keywords: fun, analysis, applications, architecture , architectures, business , Financial , Fun , Hadoop , lear


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