Silabus Big Data dan Machine Learning , Dyavacs Globalsukses. CV

1. CV. DYAVACS GLOBALSUKSES
JL. TEGALWARENG II NO.15C , CANDISARI, SEMARANG, 50257
Phone : +62 24 76727427 / +62 852 2565 3294
Email : bagusalfa@dyavacs.com / bagusalfa@gmail.com
Penawaran Pelatihan Dasar Ekosistem BigData Hadoop (Promo)
Nomor : 01/BD.HDFS/TU/2018 25 Januari 2018
Perihal : Penawaran Pelatihan
Kepada Yth. :
Bp. M. Haddiel Fuad SKom, MTI, ITIL-F
PT Mora Telematika Indonesia
Grha 9, Jalan Panataran No.9
Proklamasi Jakarta 10320 Indonesia
Phone: +6221 3199 8600
Fax: +6221 314 2882
Di Tempat
Dengan hormat,
Dengan ini kami dari CV. Dyavacs Globalsukses – sebuah perusahaan teknologi di Semarang sejak
2010 yang bergerak di bidang edukasi – Iot, Hadoop, Intelligent ERP, Machine Learning, menawarkan
Pelatihan Dasar Ekosistem BigData Hadoop (BigData Ecosystem Basic Training Course) sebagai
berikut :
Durasi : 5 hari
Hari 1 Pengenalan
 Pengenalan Hadoop
 Pengenalan MapReduce
 Pengenalan Hadoop v.2
 Instalasi Hadoop dan piranti lunak pendukung
 Konfigurasi SingleNode
 Pengenalan Manajemen Hadoop
Hari 2 Hadoop Core
 Konfigurasi Multi Node
 Menjalankan examplescript
 Map and Reduce programming dengan Java
Hari 3 Ekosistem Hadoop
 Pengenalan Ekosistem Hadoop (HBase, Hive, Kafka,Spark,Pig, Sqoop, Oozie, Flume,
Zookeper, Mahout, dsb)
 Menggunakan Ekosistem Hadoop
Hari 4 Spark
 Pengenalan Spark, Mengapa Spark, ProjectTungsten
 Menggunakan Spark

2. Hari 5 Review
 Pendalaman / review ulangdari materi sebelumnya
 Troubleshoot / Problema Hadoop
 Korelasi BigData keMachineLearning (Introduction to MachineLearning)
Infrastruktur pelatihan disediakan oleh peserta / client.
Komputer dengan RAM minimum 8G Linux, 100GB minimal Harddisk,QuadCorei3
(minimum), 1 GigabitNetwork, koneksi internet 2 Mbps per peserta.
Machine Learning
Durasi :5 hari
Day 1 . Introduction
 What is AI
 History of AI (Timeline – Big Picture)
 Application of AI
 Ethical / Policy of AI
 Why AI & Hype
 MachineLearning & Why
 What areNeural Network?
 Some simplemodels of neurons
 A simpleexample of learning
 Types of neural networks architectures
Day 2. Basic NN – Perceptron and Backpropagation
 Perceptrons
 What perceptrons can’t do
 Learning the weight of logistic outputneuron
 Why the learningworks
 Learning the weights of linear neuron
 Activation functions
 Error function
 The backpropagation algorithm
 Usingthe derivatives computed by backpropagation
 Overview of mini-batch gradientdescent
 The momentum method
 Adaptive learningrate for each connection
Day 3. Enhancement NN & Modern NN
 Overview of ways to improve generalization
 Limitingthe size of the weights
 Usingnoiseas regularize
 The up and down backpropagation
 Quick and dirty method of setting weight costs
 Hopfield nets
 Using stochastic units to improve search
 How Boltzmann machinemodels data
 Restricted Boltzmann Machines
 And example of RBM learning
 Convolutional NN
 Recurrent NN

3. Day 4. ExerciseNN
 ExercisebuildingNN
 ApplyingUnsupervised Learning NN
 ApplyingSupervised LearningNN
Day 5. ApplyingReal World casein NN
 Discussion Real Case
 Data Preparation Strategy
 Trying apply to NN
 Troubleshoot problem
 Evaluation