Sauti Yangu From Deep Learning Indaba 2019 (Part 2)

Daouda (Tanji)
Nov 1 · 6 min read

This is the second part of my article. If you didn’t read the first part click the following link to get access Sauti Yangu From Deep Learning Indaba 2019 (Part 1) .

In this second part, I will talk about Sessions (special and parallel), then Practicals and finally the Hackathon.

Special sessions :

The special sessions took place in the Indaba Research Day.
They were 21 to present their research project separated into 2 spotlights.

First Spotlight session:
1. Jecinta Mulongo, Anomaly Detection in Power Generating Plant using Machine Learning Techniques
2. Fauste NDIKUMANA, Monitoring System to Strive against Fall Armyworm in Crops Case Study: Maize in Rwanda
3. Jean AMUKWATSE, Soil Mobile Tester Laboratory (SoMiT Lab)
4. Edna Milgo, A Stochastic Optimization Based MCMC
5. Olaniyan Oluwasegun Emmanuel, Development of a Multi-Target Regression Models to Predict the Physical and Chemical Properties of Soil
6. Mohamed Tarek Shaaban Dawoud, Sim-to-real Conditional End-to-End Self-Driving Vehicle Through Visual Perception”
7. Deborah Dormah Kanubala, Risk scoring algorithms for farmers in the smallholder setting.
8. Honoré Mbaya, Prototype of semantic technology for the Congo-Africa review
9. Kale-ab Tessera, Learning compact, general-purpose neural network architectures.
10. Allan Ocholla, Algorithmic Governance: The New Normal
11. Pius Nyanumba, Gaussian Process Modelling in Rotation Measure Synthesis

Second spotlight session:
1. AYADI Alaeddine, Classification of product pose view using a unified Embedding with Hard Triplet Loss and Gradient Boosted Tree models
2. Arnaud Nzegha, Data augmentation and 3D Reconstruction
3. Mouad Riyad, Deep convolutional-recurrent neural network for SMR classification
4. Ali Bosir, Automation using IOT and NLP
5. Sam Masikini, Challenges in The Automatic Recognising and Counting Malawi Banknotes
6. Francis Chikweto, Patient Vital signs monitoring with deep Learning integration
7. Getenesh Teshome, Bidirectional Attentive Matching Based Textual Entailment Recognition using Deep Learning
8. Samantha Van Der Merwe, Predicting Social Unrest in South Africa
9. Elizabeth Benson, Urban Highway Traffic Routing And Prediction Model with HMMs
10. Rihab Gorsane, Hybrid approach for order-based optimization using Evolutionary Algorithms: Case of Capacitated Vehicle Routing Problem.

How to write a great research proposal?

Presented by Daniela Massiceti (University of Oxford), Laura Sevilla (University of Edinburgh) and George Konidaris (Brown University).
A research proposal is a description of the work you want to do during your PhD (3–5 years of research) and your motivation.
The proposal must be silk:

New, something that has never been done

Better (more accurate and more efficient)

This research proposal is used when:
- Doctoral application
- Grants: funds awarded for projects, often from government, business, etc. Seniors (like teachers)
- Fellowships and scholarships: funds for students, post-docs or even professors.

77% of respondents think this is the most important document or the second in your PhD application.
A PhD is a university degree that validates your research skills. The prerequisites are different according to the country:
- In the United States: A doctorate lasts five or six years, two of which consist of taking courses and doing research for the rest. A master’s degree could be an advantage, but it’s not necessary. Students tend to graduate with three strong conference papers of which they are the first authors.
- In the United Kingdom, Germany, Sweden, etc.: a doctorate lasts 3 to 4 years, and courses are often optional. It is therefore useful to come with a master’s degree, although this is not necessary. The best students can also graduate with three papers, but this is not a requirement.
- In Africa: A doctorate is usually a three-year degree, followed by a thesis, followed by a master’s degree.
Examples of job opportunities:
- University professor: research, teaches, mentors.
- Research Scientist (For example: in a company): write articles, maybe a little product development.
- Research Engineer (For example: in a company): develop the engineering infrastructure needed to carry out the research.
A PhD allows you to have a certain flexibility of work, to easily obtain a visa, a more independent work, more creatives. The salary can easily exceed $ 120K per year.

Parallel sessions :

I did not summarize for these sessions. They are at number 15. You can look within the list below those who speak to you the most and make a request by mail to access the presentations.

  • AI in Kenya (ANALYTICS AT AFRICASTALKING, Design Thinking in Data Science, Transfer Learning in Credit Scoring)
  • AI_Fairness (Deep Learning for Diabetic Retinal Disease Diagnosis in Zambia, Explaining Deep Learning for natural Language understanding, ICT access in Africa the age of Artificial Intelligence)
  • Data science in practice
  • ML in Resource Constrained Environments
  • Machine Learning for Health
  • Natural Language Processing
  • Bayesian optimization and Hyperparameters research
  • Deep generative models
  • Deep learning fundamentals
  • Reinforcement Learning Fundamentals
  • Recurrent Neural Networks
  • Computer Science, Camera Trap and Conservation: How can machine learning inform ecology ?
  • Measure development economic from space
  • Startups & Innovation
  • Introduction to bayesian inference

Practicals :

It’s good to read the work of others to try to understand, but to be good in AI, Data Science, you have to practice. These practical sessions are somehow a compliment or the applied part of some parallel sessions. The practicals work that we had to do during the Indaba are as follows:

  • Introduction to python
  • Machine Learning Fundamentals
  • Build your own TensorFlow
  • Deep Nets fundamentals
  • Optimization
  • Conv nets
  • Generatives Models
  • Recurrent nets
  • Reinforcement Learning

Hackathon Track :

Two Hackathon were organized throughout the week including:

  • Snapshot Serengethi

Snapshot Serengeti is the world’s largest camera project with 225 camera traps Run continuously in Serengeti National Park, Tanzania.

There were three challenges to overcome:

Species Recognition Challenge :

Obtain the best average accuracy per species.

Animal counting challenge :

Count the number of animals in each picture.

Creative challenge : everything is allowed. Be creative!

Working groups were formed, I participated as a member of the group “HackSerengethi”.

Congratulations to the winners of the challenges.

The Hackathon Team (Organizers and Participants)

  • Malaria focus on Reinforcement learning

Published on the Zindi website, this challenge was open only to participants of the 2019 Deep Indaba Learning. Malaria is believed to have been the burden of the heaviest disease of all time, while it continues to represent a significant and disproportionate global health burden. Participants used reinforcement learning to identify new solutions that could affect malaria policy in sub-Saharan Africa. Specifically, challenge participants presented solutions to determine how combinations of interventions controlling transmission, prevalence, and health outcomes of malaria infection should be distributed in a simulated human population. It is a challenge for Africa to eradicate this disease that continues to wreak havoc and we believe that with artificial intelligence a solution is possible.

I conclude this article by thanking the entire Deep Learning Indaba team, this year’s coordinator Katleen Simiyu, the University of Kenya, participants from all over the world and finally the Google Deepmind team which sponsored all Fresh from my trip especially Avishkar Bhoopchand and Ulrich Parquet with whom I share this beautiful photo.

#SautiYetu, #DLIndaba2019, #GalsenAI, #IndabaXsn #Datascientistenthusiast

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