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Build & Operate Deep Learning Data Pipeline & Data Lake Container Cluster with TensorFlow, Spark & Hadoop in GUI or API (Python) or CLI (Bash)
Link to Website
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
9:00 AM - 9:50AM Elastic Cloud Computing and Scalabe Big Data AI: What, Why and How?
10:00 AM - 10:50AM 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
11:00 AM - 11:50AM 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
12:00 AM - 12:50AM Docker Container In-Depth: Computation/Storage/Networking Models
1:00 PM - 2:00PM Lunch Break (Lunch included, Veggie option available)
2:00 PM - 6PM Hands-on: I Set Up & Test Drive Your Own AI Big Data Docker 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
9:00 AM - 9:50AM 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
10:00 AM - 10:50AM 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
11:00 AM - 11:50PM 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
12:00 AM - 12:50AM 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
1:00 PM - 2:00PM Lunch Break (Lunch included, Veggie option available)
2:00PM - 6PM Hands-on I Continued or
Hands-on II (Only for Advanced Attendeeds): Build, Train & Serve Your Own Chosen AI Application Using Python in Your Own Scalable AI Big Data Docker 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.
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) Docker Container pre-installed in your laptop (We provide WiFi access for you)
Sales end on April 15
$449.97 (+$25.74 FEE)
Group of 2 Rate
$399.97 (+$22.99 FEE)
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