Neural Network And Deep Learning

Coding Artificial Intelligence Systems

Ad-Hoc Course Registration:

  • Date: 8 – 11 February 2021
  • Time: 18.30 – 21.30
  • Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
  • Investment: Rp. 5.200.000

  • Date: 8 – 11 February 2021
  • Time: 18.30 – 21.30
  • Investment: Rp. 2.600.000


Course details :

Develop artificial neural networks that can recognize a face, handwriting patterns and are at the core of some of the most cutting-edge cognitive models in the AI landscape. We will learn to create a backpropagation neural network from scratch, and use our neural network for classification tasks. This class is the final course in the Machine Learning Specialization.

We strongly recommend that you complete the pre-requisite workshops prior to taking this course. Some concepts presented throughout the lecture may be less-than-ideal for practitioners who have not completed the pre-requisite courses.


  • Artificial Neural Networks

    Day 1

  • Neural Network Architecture

    Day 1

  • Neural Network Architecture II

    Day 1

  • Multi-Layer Perceptrons (MLP)

    Day 2

  • Neural Networks from First Principles

    Day 2

  • Neural Networks from Scratch

    Day 2

  • Neural Networks in Action

    Day 2

  • Deep Learning in Action

    Day 3

  • Deep Learning in Action II

    Day 3

  • MXNet in Action

    Day 3

  • Learn-by-Building

    Day 4

Course Producer

Samuel Chan

An  RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce.  Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.

Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.

Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.

4-Day Workshop Modules

Syllabus: Neural Network & Deep Learning

Module 1: Neural Network

Artificial Neural Networks

  • The Biological Brain Inspiration
  • Cost Function
  • The Building Blocks of Neural Networks

Neural Network Architecture

  •  Layers, Nodes, and Signals
  •  Network topology
  • Feed-forward vs Recurrent Signal

Neural Network Architecture II

  • Hidden Layers
  • Computing with Neural Network
  • Mathematical Details

Multi-Layer Perceptrons (MLP)

  • Backpropagation of Error
  • Feed-forward vs Recurrent
  • Mathematical Details

Module 2: Deep Learning

Neural Networks from First Principles

  • Sum of Squared Errors
  • Cross-Entropy Error
  • The Gradient Descent Algorithm

Neural Networks from Scratch

  • Gradient Descent by Hand
  • Neural Network by Hand
  • Learning Rate and Implementation Details

Neural Networks in Action

  • Putting It All Together
  • Parameterization and Practical Advice
  • Deep Learning for Classification and Regression

Deep Learning in Action

  • Theorizing With Effect of Depth
  • Activation Functions
  • Visualizing Logarithmic Loss

Deep Learning in Action II

  • Predicting Bank Telemarketing Campaign
  • Visualizing Tricks for Deep Neural Networks
  • Parameterization and Practical Advice

MXNet in Action

  • Thinking About Parallelism
  • MNIST Handwritten Digit Recognition
  • Predictions With MXNet and Practical Advice

Academy Modules

Graded Quiz

Learning-by-Building Module (3 Points)

Image Classification Using Neural Network

  • Build a neural network capable of classifying images into one of many classes and explain the choice of your architecture. Test your neural network using unseen images – can your algorithm correctly classify 80% of the images?

Ad-Hoc Course Registration:

  • Date: 8 – 11 February 2021
  • Time: 18.30 – 21.30
  • Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
  • Investment: Rp. 5.200.000

  • Date: 8 – 11 February 2021
  • Time: 18.30 – 21.30
  • Investment: Rp. 2.600.000


Workshop Receivables:

  • Workshop Lecturer’s Notes

    Including 2x Course Books (PDF), HTML files, course transcripts (if any).

  • Highly-accelerated Learning

    Learn under the assistance of mentorship of our lead instructor and a band of qualified teaching assistants throughout the 4-day course.

  • Certification of Completion

    Show current and prospective employers that you’ve completed the course with a signed certificate of completion.

  • Quality Learning Environment

    We pay meticulous attention to the logistical details of our workshops: quality audio and visual setups, comfortable sitting arrangements, small group size. Dinners are included for evening workshops.

  • Supplement Materials

    Receive supplement datasets to practice on, reference notes, working files (R Notebook or Jupyter Notebook), and other materials that will help you master the topics.

This workshop is recommended for:

The Neural Network and Deep Learning workshop is an advanced-level programming workshop best suited to R programmers that have completed the pre-requisite courses offered through the machine learning specialization.

Students are assumed to have a working knowledge of R and have completed the necessary pre-requisites. Consider taking the pre-requisite course or a beginner-level course instead if you have no prior programming experience or statistics knowledge.

Past Workshops in this Series:

Students work through tons of real-life examples using sample datasets donated by our team of mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.

Part of the Machine Learning Specialization

This workshop is part of the Machine Learning Specialization offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.