Neural Network And Deep Learning

Coding Artificial Intelligence Systems

R, Machine Learning
Pre-Requisite:
- Classification in Machine Learning 2
Difficulty: Advanced
Associated Specializations:
- Machine Learning
Duration: 4 Days

Ad-Hoc Course Registration:

ON-SITE CLASS

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

ONLINE CLASS

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.

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Schedule

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:

ON-SITE CLASS

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

ONLINE CLASS

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.

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RDTX Square, 9th Floor. Setiabudi, DKI Jakarta 12930.
WhatsApp: 0816-692-471
Email: community@algorit.ma

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Neural Network And Deep Learning

Ad-Hoc Course Registration:

Course details :

Schedule

Course Producer

Samuel Chan

4-Day Workshop Modules

Module 1: Neural Network

Artificial Neural Networks

Neural Network Architecture

Neural Network Architecture II

Multi-Layer Perceptrons (MLP)

Module 2: Deep Learning

Neural Networks from First Principles

Neural Networks from Scratch

Neural Networks in Action

Deep Learning in Action

Deep Learning in Action II

MXNet in Action

Academy Modules

Graded Quiz

Learning-by-Building Module (3 Points)

Ad-Hoc Course Registration:

Workshop Receivables:

Workshop Lecturer’s Notes

Highly-accelerated Learning

Certification of Completion

Quality Learning Environment

Supplement Materials

This workshop is recommended for:

Past Workshops in this Series:

Hands-on Coding

Working through real-life examples

Demo and Presentation

Learn-by-building philosophy

Part of the Machine Learning Specialization

Get In Touch

Data Science School

Company