Von alle Teilnehmenden wird erwartet, dass sie ein Leistungszertifikat oder ECTS anstreben bzw. am Ende des Kurses die Bedingungen dafür erfüllen. Dies sind die folgenden drei Bedingungen:

• Anwesenheit an mindestens 80% der Kurstermine (es darf an maximal zwei Terminen gefehlt werden)

• Präsentation des vorgegebenen Auswertungsprojekts (siehe Beschreibung hier)

• Dokumentation des Projektes über ein open Open Source GitHub-Repository (siehe ebenfalls Beschreibung hier), zu dem man alle Team-Mitglieder beigetragen haben müssen.

Vor dem ersten Kurstermin solltet Ihr ...

• Euch Accounts erstellen für GitHub, ChatGPT und Claude.

• über diesen Link VS Code bei Euch auf dem Rechner installieren.

• bei datacamp das erste Kapitel des Kurses "Introduction to Python" durcharbeiten. Dazu müsst Ihr Euch ebenfalls einen (freien) Account bei datacamp registrieren. Ihr braucht nur das erste Kapitel des Kurses durcharbeiten, das kostenfrei ist.

At the Course Kick-Off event from opencampus.sh you will get an intro to the opencampus.sh community, and you will meet your course instructor, can ask questions about the participation in the course, and get helpful information to prepare for the start of the course. The attendance at the Kick-Off is not mandatory but recommended for all participants.

Diese Woche werden wir...

folgende Themen behandeln:

• Besprechung der Übungsaufgaben der vergangenen Woche

• Einführung in die Versionierung mit git (Teil 1)

• Datenaufebereitungsschritte für das maschinelle Lernen

• Zusammenführung von Dataframes

Lernressourcen

Bis zur nächsten Woche solltet Ihr...

• diesen Kurs bei datacamp zum Zusammenführen von Dataframes absolvieren

• für eine Einführung in die Möglichkeiten von Regular Expressions, dieses Video (11 Minuten) schauen.

• dieses Video (33 Minuten) zu Git in VS Code schauen.

• dieses Video (3 Minuten) schauen und eine Person im Team bestimmen, die wie dort gezeigt, ein Team-Repository anlegt.

• dieses Video (2 Minuten) schauen, um einen Github Codespace auf Basis Eures Team-Repositories anzulegen.

• die Dateien umsatzdaten_gekuerzt.csv, kiwo.csv und wetter.csv und in einem Pandas Dataframe zusammenführen.

• Euch im Team treffen und aufteilen, wer für welche Variablen deskriptive Statistiken und Visualisierungen erstellt.

• deskriptive Statistiken und Visualisierungen zu den jeweiligen Variablen erstellen.

Diese Woche werden wir...

folgende Themen behandeln:

• Charakteristika des maschinellen Lernens

• Definition der linearen Regression

• Kostenfunktionen

• Optimierungsfunktionen

Lernressourcen

• zur Linearen Regression

Bis zur nächsten Woche solltet Ihr...

• Euren Datensatz weiter um zusätzliche Variablen ergänzen, die für die Schätzung des Umsatzes relevant sein könnten.

• Euren Datensatz teilen in einen Trainingsdatensatz vom 01.07.2013 bis 31.07.2017 und einen Validierungsdatensatz vom 01.08.2017 bis 31.07.2018.

• eine lineare Modellgleichung aufstellen, die das adjustierte R² für Euren Trainingsdatensatz maximiert.

• im Verzeichnis „Baseline Model" Eures Team Repositories die Berechnung für die lineare Regression dokumentieren.

Diese Woche werden wir...

folgende Themen behandeln:

• Overfitting und Regularisierung

• Interaktionseffekte

• Modellgütekriterien

• Einführung in neuronale Netze

Lernressourcen

• zur Definition und Schätzung von Neuronalen Netzen für unterschiedliche Beispieldatensätze

• für die Auswirkung von Overfitting und Regularisierung

Bis zur nächsten Woche solltet Ihr...

• (12 Minuten) zur Einführung in Neuronale Netze an anschauen.

• Euren Datensatz weiter um zusätzliche Variablen ergänzen, die für die Schätzung des Umsatzes relevant sein könnten.

• die Vorhersagegüte Eures linearen Modells auf Kaggle testen.

• die Kursleitung zu Eurem Repo einladen oder, wenn es öffentlich ist, den Link zu Eurem Repo in einer privaten Nachricht schicken.

Diese Woche werden wir...

folgende Themen behandeln:

• VSCode und GitHub Code Spaces

• KI-unterstützte Programmierung

• Darstellung von unterschiedlichen Datenstrukturen

• Einlesen von Daten aus externen Quellen

• Diagramm- und Skalentypen

Lernressourcen

• Optionale

• für die grafische Darstellung von Daten

Bis zur nächsten Woche solltet Ihr...

• ein Jupyter-Notebook oder Python-Skript erstellen, das

  • den Datensatz "umsatzdaten_gekuerzt.csv" einliest und

  • mit Hilfe eines Balkendiagramms den Zusammenhang der durschnittlichen Umsätze je Wochentag darstellt.

• in einem zweiten Schritt zusätzlich Konfidenzintervalle der Umsätze je Wochentag hinzufügen.

• in einem weiteren Schritt die Wochentage in der Abbildung von Montag bis Sonntag ordnet.

Are the courses in English or German?

The following two courses are in German:

• Einführung in Data Science und maschinelles Lernen

• Maschinelles Lernen für die Medizin

All other courses are in English.

Can I enroll in multiple courses at the same time?

Yes, the courses are not overlapping. Feel free to choose all courses you like. Be aware that to complete each course, you have to do a project for each course.

Should I enroll in multiple courses at the same time?

We will be glad to have you with us during the whole week if you have time! However, with our offer growing bigger, here there is a suggestion: please try to take into account the time effort that each course will take (~5 hours per week per course) and select the courses which fits your schedule in the current semester instead of just applying for all the courses. If you are not sure about which one to apply to, contact us or come to the If courses are sequential, it is usually good practice to take them one semester after the other, not both of them at the same time.

How do I know if I can get ECTS?

Ask your professors/Prüfungsamt before the courses start and clarify with them whether the course will be recognised or not. At the CAU Kiel the courses are accepted as "Wahlpflichtmodule" via the "Zentrum für Schlüsselqualifikationen". At the FH Kiel and the Muthesius Kunsthochschule Kiel the courses are accepted in many study programs as well. However, each faculty and department has their own rules. So please, check with your local institution.

When will I get the ECTS?

Once you completed a course and fulfilled the , you will get a certificate from us. This takes up to 4 weeks after the course completion. After you get a certificate from us, you can use this to get the ECTS (for CAU Student this is an automated process). In order to see the ECTS in your profile, it will take some additional time, usually around 2 to 4 weeks more.

Are all courses giving ECTS?

Yes, for all courses offered at the moment, it is possible to receive ECTS. However, the number of ECTS granted varies between 2.5 ECTS and 5 ECTS. Please, check the individual course descriptions for the respective ECTS.

Do I get a grade for my ECTS?

We do not give grades for any of the courses on opencampus.sh. You can only pass or fail the requirements of the course to receive ECTS.

Which platform will we use during the courses?

Can I use one account for all the platforms?

Unfortunately, not yet. We are working on this. For the moment, please create an account on all the platform you need. Using the same e-mail adress is probably the best option also for us to be sure to be able to help you quickly in case of any trouble.

The content on Coursera is behind a paywall, how do I get access?

All students which needs to get access to the Coursera contents because of their course will receive an invitation to the Opencampu.sh Unlimited program on Coursera, which will allow you to get access to all courses on the Coursera plaftorm for free. You will be invited via mail and you need to confirm. Please check the spam. In case you used a different e-mail account on Coursera, contact your course teacher and you will be added manually.

Diese Woche werden wir...

folgende Themen behandeln:

• Wiederholung Neuronaler Netze (NN)

• Umsetzung eines Dropout Layer

• Visualisierung von fehlenden Werten

• Verschiedene Imputationsverfahren

Lernressourcen

• zur Behandlung von fehlenden Werten

• dieses Kurses bei datacamp

Bis zur nächsten Woche solltet Ihr...

• ein (bzw. verschiedene) Verfahren wählen, um fehlende Werte in Eurem Datensatz zu ersetzen.

• Euch (5 Minuten) zu Zeitreihenanalysen anschauen.

• Euch die Aufgaben im Team gut aufteilen: Wer arbeitet an der Datenoptimierung, wer an der Modelloptimierung?

Diese Woche werden wir...

folgende Themen behandeln:

• Muster in Zeitreihenanalysen

• Baseline Modelle

• Naïve Forecasting

Lernressourcen

• (5 Minuten) zu Zeitreihenanalysen

• zur grafischen Auswertung von Zeitreihen

• zur Nutzung der Transformers Library

Bis zur nächsten Woche solltet Ihr...

• anhand Eures besten Modells die Vorhersagen für den Testdatensatz der Kaggle Competition berechnen und dort hochladen.

• Eure Abschlusspräsentation erstellen (siehe Vorgaben bei Woche 10 ).

Präsentation (Powerpoint, Keybote oder ähnliches)

Jedes Team hält eine 8 oder 10-minütige Abschlusspräsentation (genaue Info erfolgt in der Vorwoche - bitte darauf achten, dass Ihr die Länge einhaltet!) mit den folgenden Inhalten:

• Euren Namen auf der Titelseite

• Auflistung und kurze Beschreibung der selbst erstellten Variablen

• Balkendiagramme mit Konfidenzintervallen für zwei selbst erstellte Variablen

• Optimierung des linearen Modells: Modellgleichung und adjusted r²

• Art der Missing Value Imputation

• Optimierung des neuronalen Netzes:

  • Source Code zur Definition des neuronalen Netzes

  • Darstellung der Loss-Funktionen für Trainings- und Validierungsdatensatz

  • MAPEs für den Validierungsdatensatz insgesamt und für jede Warengruppe einzeln

• „Worst Fail“ / „Best Improvement“

On our EDU-Platform each course and its contents is described but often it will still be difficult to decide what's the best course for your ability level and your needs. Below we therefore included a quick comparison on the courses, which might provide some additional help.

However, if you have any doubt, we strongly recommend you to

Quick Comparison of the Different Courses

Roughly, the difficulty level of the courses is increasing from left to right.

The conditions to be met in order to receive a Certificate of Achievement (and ECTS) are:

• Attendance to at least 80% of the classes (it is allowed to miss maximum 2 times)

• Delivery of a machine learning project based on the TensorFlow API and including the needed documentation.

Attendance:

If you attend via Zoom, please make sure to use your full name, which should be the same that you used to register at edu.opencampus.sh. Otherwise your attendance will not be recorded!

Projects:

Check the Projects section to learn more about the projects.

Hybrid Format - Every Thursday 18h00-19h45

Taken your first steps in ML and thirsty for becoming a fully equipped practitioner? Then this course is perfectly right for you! You will learn+build all relevant NN Architectures with the best tools

The next pages serve as the course book. Go ahead and dive in!

Great that you want to dive into the deep waters of Machine Learning. These are exciting times with major advancements on a quarterly basis like ChatGPT, Whisper, StableDiffusion and so many more. Nevertheless, all these exciting models were developed using solid ML knowledge, which is what we aim to acquire in this course.

This is a course which brings you from beginner to intermediate or even advanced. It is formally called Intermediate Machine Learning but following HuggingFace🤗 terms (which we will use heavily in the course) I like to call the course SmilingFace😊. This is meant ironically, because you will never laugh in the course😊. Okay joking aside the use of smileys during learning and practioning ML helps us to remember to have fun, laugh about our mistakes and take ourself not too seriously as it was proposed by the HuggingFace🤗 community. Therefore we will use our 😊 heavily in this course.

On the next pages you can see what the content of each course week will be starting with what will happen during each of our course sessions. Then again the SmilingFace😊 will lead you to what else to do in the week. I have divided the course into three levels of course work:

😊

The part after one 😊 is mandatory for each course participant for a for successful participation.

😊😊

The part after two 😊😊 is voluntary but recommended.

😊😊😊

The part after three 😊😊😊 is completely voluntarily for the ones who really want to know.

Remember the course instructor(me) is also fallible so please question me if you see something that does not seem kind of right to you. Also always ask questions especially if you don't fully understand something. This is really why we give this course afterall so that you understand everything😊

Own contributions or suggestions for improving the course as well as feedback are always welcome😊

Let's dive right in!

Diese Woche werden wir...

folgende Themen behandeln:

• Hyperparameter in Neuronalen Netzen

• Frameworks zur Implementierung von Neuronalen Netzen

• Datenaufbereitung für TensorFlow

• Optimierung eines neuronalen Netzes mit Python und TensorFlow

Lernressourcen

• Video (12 Minuten) zur Einführung in Neuronale Netze

• Notebook zur Datenaufbereitung für TensorFlow

• Notebook zur Optimierung eines neuronalen Netzes

• Blog mit zusätzlichen Informationen zur Batch-Normalisierung

Bis zur nächsten Woche solltet Ihr...

• dieses Video (7 Minuten) schauen, um die Eigenschaften von Dropout-Layern genauer zu verstehen.

• dieses Video (5 Minuten) schauen, um die Vorteile der Normalisierung besser zu verstehen.

• alle Eure Modellvariablen auf die Existenz von fehlenden und unplausiblen Werten hin untersuchen.

• Euren Datensatz aufebereiten, in dem ihr alle kategoriellen Features korrekt kodiert und alle Zeilen mit fehlenden Werten entfernt.

• ein erstes Neuronales Netz optimieren.

Diese Woche werdet Ihr...

eine Einführung zu den folgenden Themen bekommen:

• Was ist Data Science?

• R vs. Python vs. SPSS vs. ...

• Jupyter Notebooks

Lernressourcen

• Leitfaden zu Markdown

• Übersicht über Colaboratory

• Wenn Ihr Schüler:in, Student:in oder Lehrkraft seid, könnt Ihr Euch wie hier beschrieben gratis für GitHub Co-Pilot registrieren, das Ihr dann ggf. in VSCode, das wir uns in der nächsten Woche anschauen, integrieren könnt. Die Bezahlversion kostet aktuell 10 US-Dollar pro Monat.

Bis zur nächsten Woche solltet Ihr...

• Optional: Euch wie hier beschrieben für GitHub Copilot anmelden und das 30-Tage-Probe-Abo nutzen, das gratis ist. Wenn Ihr Schüler:in, Student:in oder Lehrkraft seid, könnt Ihr Euch wie hier beschrieben gratis für GitHub Co-Pilot registrieren, das Ihr dann ggf. in VSCode, das wir uns in der nächsten Woche anschauen, integrieren könnt. Die Bezahlversion kostet aktuell 10 US-Dollar pro Monat.

• dieses Video zum Arbeiten mit Strings ansehen (16 Minuten).

• diesen Video-Ausschnitt zum Arbeiten mit Zahlen ("Numbers" und "Working With Numbers") ansehen (5 Minuten).

• die ersten vier Kapitel dieses Videos zu Funktionen ansehen (12 Minuten).

• dieses Video zum Arbeiten mit Listen in Python ansehen (18 Minuten).

• dieses Video zur Einrichtung von VS Code speziell für Python und Data Science ansehen (22 Minuten).

• die folgenden Aufgaben bearbeiten (siehe auch Präsentation):

  • Importiere den Datensatz “wetter.csv”, den Du über folgenden Link herunterladen kannst: https://raw.githubusercontent.com/opencampus-sh/einfuehrung-in-data-science-und-ml/main/wetter.csv

  • Berechne die Gesamtdurchschnittstemperatur.

  • Berechne die Durchschnittstemperatur für den Monat Juli.

  • Vergleiche, ob die Monate Juli und Mai sich in ihrer Durchschnittstemperatur signifikant unterscheiden.

  Denkt daran, dass Ihr jederzeit Claude oder ChatGPT zur Unterstützung einsetzen könnt, gerade anhand vergleichsweise leichter Aufgaben kann man dabei gut lernen wie man Aufgabenstellungen lösen kann, die man vorher noch nie gemacht hat.

This week you will...

• see how to improve the basic neural network for computer vision you learned last week.

• learn about what happens if your data is more complex; if the images are larger, or if the features are not always in the same place, and how to handle such issues.

Learning Resources

• Week 3 and 4 of the course Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Until next week you should...

• complete week 1 and week 2 of the course Convolutional Neural Networks in TensorFlow, including the provided assignments

• setup a repository for your project following the instructions given here

• conduct a literature review according to the instructions given here

• document your findings on the literature review according to the instructions of above

• complete the assignment in the following notebook:

  • Assignment Notebook

This week you will...

• get a soft introduction to what Machine Learning and Deep Learning are, and how they offer you a new programming paradigm, giving you a new set of tools to open previously unexplored scenarios. All you need to know is some very basic programming skills, and you'll pick the rest up as you go along.

• take the just learned new programming paradigm used in machine learning to the next level by beginning to solve problems of computer vision with just a few lines of code!

Learning Resources

• Week 1 and 2 of the course Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

• Don't forget to check out the important part of week 2 that is working through this notebook.

Until next week you should...

• complete week 3 and week 4 of the course Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning.

• decide on a project for the course and form groups

• complete the two assignments in the following notebooks:

  • Assignment Notebook 1

  • Assignment Notebook 2

Diese Woche werden wir...

folgende Themen behandeln:

• Ergänzung der Teams für die Auswertungsprojekte

• Besprechung der Aufgaben zu dieser Woche

• Einführung in die Versionierung mit git (Teil 2)

• Aktuelle KI-Anwendungen

Lernressourcen

• Beispiel für die Relevanz von Interaktionseffekten

Bis zur nächsten Woche solltet Ihr...

• das Kapitel „Creating Features“ dieses Kurses bei datacamp absolvieren.

• die erste Woche des Kurses Supervised Machine Learning: Regression and Classification (ca. 2,5 Stunden) von Andrew Ng auf Coursera absolvieren.

• Euch im Team treffen, um Ideen zur Umsatzvorhersage zu diskutieren und erste zusätzliche Variablen zu erstellen (auch, ob es Daten gibt, die ihr zusätzlich verwenden könnt).

• das Verzeichnis "Data Import and Preparation" Eures Team Repositories ergänzen um:

  • zusätzliche heruntergeladene oder selbst erstellte Daten (z.B. Listen mit Feiertagen)

  • Code um alle Daten in einem Datensatz zusammenzuführen

  • Code, um neuen Variablen zu erstellen bzw. bestehende Variablen für die Vorhersage aufzubereiten

This week you will...

• get an understanding for the importance of tokenization of a text when training a neural network for texts, for example, to do a sentiment analysis. Tokenization is the process of converting the text into numeric values, with a number representing a word or a character.

• learn about embeddings, where the text tokens are mapped as vectors in a high dimensional space. With embeddings and labelled examples, these vectors can then be tuned so that words with similar meaning will have a similar direction in the vector space. This will begin the process of training a neural network to understand sentiment in text -- and you'll begin by looking at movie reviews, training a neural network on texts that are labelled 'positive' or 'negative' and determining which words in a sentence drive those meanings.

Learning Resources

• Week 1 and 2 of the course

Until next week you should...

• complete week 3 and week 4 of the course

• complete to generate text in the unique speaking style of Star Wars character Master Yoda.

• decide on an evaluation metric for your project task and evaluate your baseline model

• document the evaluation results of your baseline model and the used metric(s) in your project repository

This week you will...

• start digging into a variety of model formats that are used in training models to understand context in sequence. In the last couple of weeks you looked first at Tokenizing words to get numeric values from them, and then using Embeddings to group words of similar meaning depending on how they were labelled. This gave you a good, but rough, sentiment analysis -- words such as 'fun' and 'entertaining' might show up in a positive movie review, and 'boring' and 'dull' might show up in a negative one. But sentiment can also be determined by the sequence in which words appear. For example, you could have 'not fun', which of course is the opposite of 'fun'.

• learn about using natural language processing (NLP) models for predictions. Given a body of words, you could conceivably predict the word most likely to follow a given word or phrase, and once you've done that, to do it again, and again. With that in mind, you'll build a text generator. It's trained with texts that mimic the style of master Yoda from Star Wars and can be used to produce sentences that sound similar those of Yoda.

Learning Resources

• Week 3 and 4 of the course

Until next week you should...

• prepare questions for the instructor team on problems in your project or on how to potentially improve your results.

• working on the definition of your project's final model(s) and their evaluation according to the instructions given

• document how you will evaluate your model results

Also, you may already start to...

• complete week 1 and week 2 of the course

• complete the tasks of the assignment in .

This week you will...

• go deeper into using ConvNets with real-world data and a much larger dataset than those you've been using thus far.

• learn about image augmentation, a technique to avoid overfitting by tweaking the training set to potentially increase the diversity of subjects it covers.

• discuss your project ideas.

Slides

Learning Resources

• Week 1 and 2 of the course

Until next week you should...

• complete week 3 and week 4 of the course

• complete the exercise assignment in

• investigate the characteristics of your project's dataset according to the instructions given

• document your findings on the dataset characteristics according to the instructions of above

This week you will...

• explore using Recurrent Neural networks (RNN) and Long Short Term Memory (LSTM) networks and see how useful they are to classify and predict on sequential data.

• add CNNs on top of Dnns and RNNs and put it together using a real world data series -- one which measures sunspot activity over hundreds of years,

Learning Resources

• Week 3 and 4 of the course

Until next week you should...

• complete your project work

• prepare your project presnetation according to the instructions given

This week you will...

• present your project in the final presentations. :-)

After the Final Presentation you should...

• complete your project documentation and submit it according to the instructions given

This week you will...

• get familiar wit transfer learning, a powerful technique to include the knowledge of models that were trained on large datasets and benefit from the features these models already learned in your own problem scenario.

• move beyond binary classification into categorical classification and the specific coding considerations for the corresponding models.

• present your key findings for the literature review considering you selected project

Learning Resources

• Week 3 and 4 of the course

Until next week you should...

• complete week 1 and week 2 of the course

• complete the exercise assignment in

• consider a baseline model or a baseline comparison for your project task according to the instructions given

• document your decision according to the instructions given in the link above

By getting accepted for the degree program you get:

• Preferred access to all our courses (for organizational reasons you still have to do the application process for each course)

• Access to a FlexDesk in the Starterkitchen coworking space and the coworking areas in the Kosmos and at the Waterkant

• Discounted or preferred access to events like the Waterkant Festival

• Access to special network events, for example, to meet potential employers working in the field of machine learning

Typically, we will accept new participants only at the beginning of each semester though. If you are already participating in one of our machine learning courses, you can also get accepted to the program during the semester though. Simply write us an email at , and we will check your application. Also before the start of each semester, we have a special information event, in which we will provide additional details on the program as well as on the specific courses offered in the upcoming semester.

What are the requirements to get the degree?

The program comprises four mandatory elements. To successfully obtain the degree, you must complete at least one element per semester, that is, the maximum time to complete the degree is four semesters. However, we recommend completing it in two or three semesters. If you completed an element of the program before the degree was offered, we will still accept these as part of the degree.

The mandatory elements to obtain the degree are:

Completion of 3 or more of the machine learning courses at opencampus.sh with a minimum of 12.5 ECTS in total. (Check the current list of available course .)

Completion of at least one of the following hackathons:

• (participating with an AI project or startup)

• hackathon

Course session

Quiz

ML Basics recap

Solutions exercises

Presentation from the participants of the tasks from PyTorch101

Walk-through

PyTorch 202 (Lab 02)

To-do

😊

Go for your own through the Colab Notebook above (Pytorch202) and try to understand and repeat the steps for your own.

Watch the videos on the next page

Go through the following notebooks and complete the second one (assignment notebook):

The redundancy between some notebooks is desired to reintroduce the concepts in a different way and hence enrich your learning experience!

😊😊

Try to improve the accuracy in the PyTorch 202 notebook by tweaking the amount of layers and number of neurons

😊😊😊

Familiarize yourself with basic PyTorch Tutorials:

Before the first class you should ...

• register with and the platform to get corresponding accounts.

• watch the videos (19 minutes) and (21 minutes) It will help you getting familiar with the vocabulary and basic concepts in machine learning and get a first intuition for their meaning. Don't worry if you do not understand everything viewing it the first time, and maybe you want to watch the videos again at a later point in the course to understand more details.

• do the introductory course on the basics of Python if you do not have any experience in Python yet:

This week you will...

• get a basic introduction to neural nets in order to get a first intuition in the underlying mechanisms

• get a first idea about possible projects you might want to work on throughout the course

Learning Resources

• Video (12 minutes)

• from Kaggle

• on Medium

Until next week you should...

• complete week 1 and week 2 of the course

• tthink about project ideas to present them in class (post your ideas in the chat during the week)

• complete the two assignments given in the following notebooks:

There are certain requirements which form the basis for a successful course participation. If you do not have the mandatory requirements listed below, you should consider enrolling in a more basic course of our offerings. Alternatively bring yourself up to speed. Under additional ressources on the left sidebar you find the necessary ressources. Since the course has a really high pace it will be absolutely necessary to straighten these basic requirements before the course!

Mandatory

Python

Here is a refresher notebook:

Math

Linear Algebra, Probability Theory (at least the basics)

Machine Learning

Basics:

• What is a neural network

• What is a forward/backprogragation

• What is a loss

• What is an activation function

• What is train/val/test

Totally optional

You can set up your PC for local development. A guiding notebook is here:

Here different IDEs are presented and compared:

Course session

Welcome and Introduction round

Introduction of the course, opencampus, the course instructor and the course participants

Tool Set-Up

• Colab

• Editor (VSCode)

• Virtual Environments

• Git/Github

Walk-through

PyTorch 101 (Lab 01)

A visual overview of the workflow in the Colab notebook you can get in the PyTorch diagram below:

To-do

😊

1. Watch the following introduction video to the PyTorch framework

1. Watch all the videos on the next page - they are derived from a former Coursera Course

2. Go for your own through the Colab Notebook above (Pytorch101) and try to understand and repeat the steps for your own. Thereby you should also solve Task 1-3 in the notebook. You can create therefore a copy of the notebook in your Drive or download the notebook to work locally on it. Ensure that you sufficient computing resources available (i.e gpu) if you choose to work locally.

😊😊

Try to improve the accuracy in the PyTorch 101 notebook by tweaking the amount of layers and number of neurons

😊😊😊

Familiarize yourself with basic PyTorch Tutorials:

This week you will...

• take a look at some of the unique considerations involved when handling sequential time series data -- where values change over time, like the temperature on a particular day, or the number of visitors to your web site. We'll discuss various methodologies for predicting future values in these time series.

• begin to teach neural networks to recognize and predict on time series.

Learning Resources

• Week 1 and 2 of the course

Until next week you should...

• complete week 3 and week 4 of the course

• continue optimizing your final model

• complete the two assignments in the following notebooks:

  • TBD

This week you will...

• focus fully on your project.

• get feedback from the instructor team on problems in your current project or on how to potentially improve your results.

Learning Resources

• Week 3 and 4 of the course Natural Language Processing in TensorFlow

Until next week you should...

(If you haven't done so already last week.)

• complete week 1 and week 2 of the course Sequences, Time Series and Prediction

• complete tasks of the assignment in this notebook.

Welcome to all participants.

We start with a short introduction of the course topics. Get to know each other and explain the course structure and all the required tasks for a successful participation.

This requires i.e.: Give a project presentation and provide a well documented code with data via GitHub. Addtionally you are only allowed to miss two class sessions during the semester.

The conditions to be met in order to receive a Certificate of Achievement (and ECTS) are:

• Attendance to at least 80% of the classes (it is allowed to miss maximum 2 times)

• Delivery of a ML-Time Series project and including the needed documentation (GitHub-repository is recommended)

• Upload of your presentation (PDF-file is preferred) to the Edu-Campus-System

Attendance:

If you attend via Zoom, please make sure to use your full name, which should be the same that you used to register at edu.opencampus.sh. Otherwise your attendance will not be recorded!

Please switch on your camera - ask questions - be (inter)-active!!

Projects:

Check the Projects section to learn more about the projects.

Course session

Kaggle - Our first challenge: Paddy

Exploratory Data Analysis(EDA) for Paddy Disease Classification

Solutions exercise CNN

Presentation from the participants of the CNN assignment from Coursera

Walk-through

PyTorchLightning

PyTorch 303 (Lab 03)

To-do

😊

Go for your own through the Colab Notebook above (PyTorch303) and try to understand and repeat the steps for your own.

Do Week 4 of the Coursera Course

Please register at kaggle.com and join the competition. Go through the Exploratory Data Analysis Notebook session and create your own EDA. Here is the link to the competiton:

The main objective of this Kaggle competition is to develop a machine or deep learning-based model to classify the given paddy leaf images accurately. A training dataset of 10,407 (75%) labeled images across ten classes (nine disease categories and normal leaf) is provided. Moreover, the competition host also provides additional metadata for each image, such as the paddy variety and age. Your task is to classify each paddy image in the given test dataset of 3,469 (25%) images into one of the nine disease categories or a normal leaf.

So that is where we will be heading in the next sessions, trying different tools and techniques to tackle this challenge.

Here again my EDA Notebook:

😊😊

😊😊😊

Course session

https://drive.google.com/file/d/18VsrKSqNFaWeWsL24ULFrNnwpghpdwJZ/view?usp=sharing

Walk-through

Hyperparameter experiment

The following notebook will show how to set up a hyperparameter experiment in plain PyTorch. More importantly it give you the results and enables you to analyze and play around

Kaggle

• Introduction

• Titanic

Solutions exercise MLP

Presentation from the participants of the MLP from Coursera

To-do

😊

Watch the videos on the next page

Go through the following notebooks and complete the second one (assignment notebook):

The next task is to analyze the results of the hyperparameter experiment and create a small presentation on your findings(e.g. batch size of 16 with lr=0.2 seems to equal batch size of 1 with lr=0.01). Here is the notebook again:

😊😊

Run your own hyperparameter experiment

😊😊😊

Do your own EDA on the Titanic Dataset and/or look at other EDA notebooks from competitors. Make a final presentable EDA notebook.

Familiarize yourself with this PyTorch Tutorials:

Course session

Faster Coding with ChatGPT, Stackoverflow and clever search

Deep dive

• What are Embeddings?

• Reinforcements of and insights into RNNs beyond Coursera

Solutions exercise RNN

Presentation from the participants of the RNN assignment from Coursera

Paddy challenge

Logistic regression baseline for the Paddy Competition

Walk-through

Basic CNN in PyTorchLightning

PyTorch 404 (Lab 04)

To-do

😊

Watch the videos on the next page

Build your own model(s) for the Paddy challenge and try to achieve the best accuracy. Log your results that you can present them in the class!

😊😊

Add the the test functionality and create a submission.csv and upload it to the leaderboard

1. How do we define learning?

1. How do we evaluate our Networks?

1. How do we learn our Network?

1. How do we handle Big Data?

1. Early Stopping

Done!

Sequence-to-Sequence Encoder and Decoder

The Transformer Network

Course session

Kaggle

Presentation of experiments with the goal of improving the classification accuracy

Transfer Learning

Theory and Applications

Walk-through

PyTorch 505

Transfer Learning CNN in PyTorchLightning:

To-do

😊

Watch the videos on the next page

Watch the following Seminar about Transformers:

😊😊

Go on using ideas discussed in this session and go on improving the accuracy on the Paddy Dataset

Course session

Explanatory Session Part 1

Self-attention and multihead attention

Hugging Face Introduction

Library and Use of HuggingFace for working with transfomer models

Explanatory Session Part 2

Transformer Encoder and Positional Encoding

Explanatory Session Part 3

Vision Transformer

Walk-through

Finetuning Vision Transformer on Kaggle Paddy Dataset

To-do

😊

Look at current Kaggle competitions and make proposals

😊😊

Go through this excellent site explaining Transformers:

Do Chapter 1-3 of the HuggingFace NLP course

😊😊😊

Look closer at the Pytorch module nn.Transformer (documentation) and go through a tutorial on how to use it for next token prediction.

Watch this excellent "Build from Scratch" video from Andrej Karpathy

1. Motivation Diabetic Retinopathy

1. Breakdown of the Convolution (1D and 2D)

1. Core Components of the Convolutional Layer

1. Activation Functions

1. Pooling and Fully Connected Layers

1. Training the Network

1. Transfer Learning

Done!

Last but not least - here are some very good book references. It is not required or requested to buy any of these books. If you are enrolled as a student at CAU, FH Kiel or another university you might have access to these books via the SpringerLink of your home institution. They cover the course material quite well and provide GitHub-Repositories for the codes which where used throughout the book. Unfortunately there is no 1:1-Coursera course matching these books perfectly - but we will do our best ;-)

Some additional course material and MOOCs if you want you expand your knowledge at your own pace:

For the summer-term 2024 we use mainly YouTube playlists and freely available resources.

Check out the more detailed playlists in each week.

In case you enjoy Coursera - here are some suggestions:

• register with Google and the Coursera platform to get corresponding accounts. Since we switched for the second/third run our video lecture sources (the Coursera part is optional - but still benefical for the interested) this part is optional.

Please enroll/attend/complete the following Coursera MOOCs during the semester:

Think about your project:

What are your skills and interests?

What motivates you?

Watch the first YouTube-Tutorials

THIS PLAYLIST IS OPTIONAL:

1. Why Machine Learning is exciting

1. What is Machine Learning?

1. Logistic Regression

1. Interpretation of Logistic Regression

1. Motivation for Multilayer Perceptron

1. Multilayer Perceptron Concepts

1. Multilayer Perceptron Math Model

1. Deep Learning

1. Example: Document Analysis

1. Interpretation of Multilayer Perceptron

1. Transfer Learning

1. Model Selection

1. Early History of Neural Networks

1. Hierarchical Structure of Images

1. Convolutional Filters

1. Convolutional Neural Networks

1. CNN Math Model

1. How the Model learns

1. Advantages of Hierachical Features

1. CNN on Real Images

1. Applications and Use in Practice

1. Deep Learning and Transfer Learning

Done!

Watch the second YouTube-Tutorial:

Work on your semester project topic and get started.

This means:

Connect to your team members

Launch a GitHub Repository

Prepare your data

OPTIONAL:

Continue working on your semester project !!!

Define an appropriate approach for your problem

Formulate some questions about things you do not know

OPTIONAL:

Try to answer/motivate these questions:

What is you idea/topic?

Which data do you use?

How is it structured?

Is there something special you want to forecast?

OPTIONAL:

For the semester project - you can bring your own data - we will discuss this in the first/second session. Nevertheless here you can find some resources to think about a potential project:

Check out these frameworks:

OPTIONAL --- If you need to brush-up your knowledge on Trees:

Word Vectors

Attention Mechanism

Finalize your semester project !!!

OPTIONAL:

The session takes place every Tuesday 18h15 in starterkitchen

For the course it is mandatory to purchase the course material (https://www.udemy.com/course/100-days-of-code/. Discounts are available via udemy and https://appbrewery.com/. It should cost you around 15€.)

This gitbook contains the main information you need for the course (e.g. homework and extra materials)

CONGRATULATIONS - YOU MADE IT !!!

CONGRATULATIONS - YOU MADE IT !!!

To-Do (Until 07/01/2025)

Homework:

• Do days 18 and 19 of the course

• Days 20-23 will be skipped, you can still do them for practice

• Don't forget your Final Project

• The deadline for submitting your well documented project is the 01/03/2025

• You have to prepare a pitch presentation for the session of 21/01/2025

• Your submission should tell a story:

  • What is the goal of the project?

  • What is the roadmap to reach that goal?

  • What did you try / did not work / can be improved upon?

• You have to at least hand in a Jupyter Notebook documenting code snippets, also if you are just working with .py files

• You can alternatively upload a zip file containing the Jupyter Notebook and needed project files (own libraries, ressources, diagrams)

• In a group project it should become clear, who participated and who worked on what part

General remarks on the project

• The project should at least look and feel like the Projects you build in the course

• Use a GUI

  • Turtles at least, it is demonstrated in the udemy course

  • Pygame is not difficult either

  • Tkinter? Others?

• Use of libraries is encouraged!

Model Notebook

Please follow the structure of this notebook for your submission:

If you want to include graphics or other files, please contain everything in one zip file. The opencampus.sh system only accepts single file uploads. Subsequent uploads will overwrite previous submissions!

Finalize your semester project !!!

OPTIONAL:

Finalize your semester project !!!

OPTIONAL:

Work on your semester project !!!

Plot your data... ;-)

Think about an appropiate approach for your problem

Formulate some questions about things you do not know

OPTIONAL:

To-Do (until 29/10/2024)

• Buy the udemy course (ideally for the lower price) https://www.udemy.com/course/100-days-of-code

• Homework:

  • Do the first day of the course

    • Watch the videos

    • Do the interactive coding exercises

Totally Optional

If you are very eager you can have a look at this additional, more advanced material:

• Watch lecture 0 of the Havard Python Course:

For the best learning experience, it's recommended to watch the 2-hour lecture twice: first for a quick overview, and then more slowly while taking notes. Please ensure you allocate sufficient time for this.

• Work through the following homework assignments

  • https://colab.research.google.com/drive/1Rk6TAFacJJQ4CxM2SF59ezqJj2VkBJ9P?usp=sharing

  • https://colab.research.google.com/drive/1pfFP971d3aRHno91KdVTTbywVpUMnMSk?usp=sharing

Always first create a copy with "Copy to Drive" or download the notebooks to work locally on them. Otherwise your progress won't be saved!!

To-Do (until 05/11/2024)

Homework :

• Do days 2 & 3 of the course

  • Watch the videos

  • Do the interactive coding exercises (online at udemy and in PyCharm)

Totally Optional

• Watch lecture 1 of the Havard Python Course:

For the best learning experience, it's recommended to watch the 1-hour lecture twice: first for a quick overview, and then more slowly while taking notes. Please ensure you allocate sufficient time for this.

• Work through the following homework assignment

  • https://colab.research.google.com/drive/1HVemFckA3FqYe3DdirA3tYJYTx5_Pvga

Always first create a copy with "Copy to Drive" or download the notebooks to work locally on them. Otherwise your progress won't be saved!!

To-Do (Until 10/12/2024)

Homework:

• Decide for one of the two final projects (they will be shown on your certificate):

  • Knowledge Cards: Flash Card Tool

  • Digital Pet: Interactive Virtual Companion

• List out what data the program has to handle (e.g. flash card with front and back, pet stats like hunger) and the matching data structures you need in python

• Create a flow chart for the program, like you have seen with hangman in the course

• We will form project groups of ~5 next session

Totally Optional

• Watch lecture 5 of the Havard Python Course:

For the best learning experience, it's recommended to watch the lecture twice: first for a quick overview, and then more slowly while taking notes. Please ensure you allocate sufficient time for this.

To-Do (until 12/11/2024)

Homework :

• Do days 4 & 5 of the course

  • Watch the videos

  • Do the interactive coding exercises

• Find the Worklab of Week 2 under Resources

Totally Optional

• Watch lecture 2 of the Havard Python Course:

For the best learning experience, it's recommended to watch the 1-hour lecture twice: first for a quick overview, and then more slowly while taking notes. Please ensure you allocate sufficient time for this.

• Work through the following homework assignment

  • https://colab.research.google.com/drive/1vC07xaVcj20v7GC-w4vt-2aoOKaeQKuE?usp=sharing

  • https://colab.research.google.com/drive/1R6tIPUzGGTvAyyFgkr2xRwGotQXPZP_L?usp=sharing

Always first create a copy with "Copy to Drive" or download the notebooks to work locally on them. Otherwise your progress won't be saved!!

To-Do (Until 26/11/2024)

Homework:

• Do days 8, 9 & 10 of the course

  • Watch the videos

  • Do the interactive coding exercises

• We will skip day 11, but you can still do it for practice

Totally Optional

• Watch lecture 4 of the Havard Python Course:

For the best learning experience, it's recommended to watch the 1-hour lecture twice: first for a quick overview, and then more slowly while taking notes. Please ensure you allocate sufficient time for this.

• Work through the following homework assignment

Always first create a copy with "Copy to Drive" or download the notebooks to work locally on them. Otherwise your progress won't be saved!!

The conditions to be met in order to receive a Certificate of Achievement (and ECTS) are:

• Attendance to at least 80% of the classes (it is allowed to miss a maximum 2 classes)

• GitHub repository including a well-documented project on a fine-tuned large language model

• Presentation of the project at the end of the course

Attendance:

If you attend via Zoom, please make sure to use your full name, which should be the same that you used to register at edu.opencampus.sh. Otherwise your attendance will not be recorded!

Projects:

Check the Projects section to learn more about how to get started, complete, and submit you project.

Worklabs

• Week 3 (Data Types)

  https://colab.research.google.com/drive/1QxcbSduin4UkU78_ev4ZlzyI6dHTTlRO?usp=sharing

• Week 4 (Loops)

  https://colab.research.google.com/drive/1Xu6VKEmJdUJvdmdK1epUo9lkIYFqU7s1?usp=sharing

• Week 5 (Functions I)

  https://colab.research.google.com/drive/1IArpoS4uOn9Y1uhPwpudHIi4dxV-b45I?usp=sharing

• Week 6 (Dictionaries):

  https://colab.research.google.com/drive/1yL84PqfmRqsRlAKcweXex4uoqJkHtDuc?usp=sharing

• Week 7 (Jupyter Notebook):

  https://colab.research.google.com/drive/10tHG_qMwrNN8uh1Zt3CoudLkFS6UGEHI?usp=sharing

• Week 8 (Object Oriented Programming):

  See below

Solutions (for the totally optional exercises on the Harvard material)

• Week 1 (Functions & Variables)

  • https://colab.research.google.com/drive/1Qn7ZT-_yr1-PmcsetIZCljvwHb0PmOuc?usp=sharing

  • https://colab.research.google.com/drive/1xl1mkRB31zBLQhE3DtuVZObZfssDA_ak?usp=sharing

• Week 2 (Conditionals)

  • https://colab.research.google.com/drive/1I72DNw-LS3BJO_NbkqYyqFZcUsLdFTh_?usp=sharing

• Week 3 (Loops, Lists & Dicts)

  • https://colab.research.google.com/drive/1qTtuAqND6Z-OP7rkr4hOWKqWzgf3rAHN?usp=sharing

• Week 4 (Exceptions)

  • https://colab.research.google.com/drive/1z9xqzcOV5FLoQl4PhNzZSIZJDkd7JgFC?usp=sharing

• Week 5 (Libraries)

  • ​https://colab.research.google.com/drive/1swsc4earaul7lo00q1KL1FlH6MLPZ8lG?usp=sharing​

• Week 6 (File Handling)

  • ​https://colab.research.google.com/drive/1CwmTFMU36ZlOfd3OuYKN5QKfqZ6pLRDK?usp=sharing

• Week 7 (Unit Tests)

  • ​No exercises

• Week 8 (Object Oriented Programming)

  • ​https://colab.research.google.com/drive/1_XPcB6K3Rn65GwRcUTulsdtwAhKIlp9U?usp=sharing​

Note1: Object Oriented Programming is a difficult but powerful concept, please take your time to revise the topic!

Note 2: If you want to install packages in the environment we set up during the course, you can do so by running %pip installpackage_name (notice the %-sign!) e.g. %pip install PrettyTable in one of your notebook cells after you selected the environment. This is only needed once, after that it can always be reimported with import. If it doesn't work (e.g. in Colab, try !pip installpackage_name with ! instead of %)

To-Do (Until 17/12/2024)

Homework:

• Do days 16 and 17 of the course

• Get started with group project (see information on page Final Project)

Totally Optional:

• Watch lecture 8 of the Havard Python Course:

Object Oriented Programming is not an easy concept. The video is almost three hours long!

For the best learning experience, it's recommended to watch the lecture twice: first for a quick overview, and then more slowly while taking notes. Please ensure you allocate sufficient time for this.

• Work through the following homework assignment (short)

  • https://colab.research.google.com/drive/1XYmbiRmgRtK9NVm8GtSjKtE--7S2u6qQ?usp=sharing

Always first create a copy with "Copy to Drive" or download the notebooks to work locally on them. Otherwise your progress won't be saved.

Before the first class you should ...

• If you are not familiar with the Hugging Face API yet, do at minimum chapter 1 and 2 of the before the start of the course. Further, I strongly recommend to additionally complete the chapters 3, 5 and 6.

• Register to the newsletter.

• Get a Discord account and enter the and the .

This week you will...

• discuss important aspects of evaluating models.

• discuss which models you want to use for your project.

Learning Resources

Until next week you should...

• decide on a baseline model and implement an evaluation chain for your base model.

OPTIONAL:

This week you will...

• get all details about the structure and form of the course.

• get to know your fellow course participants.

• discuss the general types of training large language models.

• discuss current sources for staying up-to-date.

Learning Resources

Until next week you should...

• decide on a project.

• watch from Niels Rogge (1 hour).

• note at least one question on the video above.

• watch the four videos of the (Part 1 to Part 4; about 50 minutes in total) if the transformers architecture is new to you.

This week you will...

• learn about different ways of using special tokens for instruction tuning

• discuss problems in model evaluation

Learning Resources

• on how to fine-tune with Hugging Face AutoTrain

Until next week you should...

• continue working on your project and prepare the project presentation.

This week you will...

• present your project in the final presentations. :-)

After the Final Presentation you should...

• complete your project documentation and submit it according to the instructions given here

Before the first class you should ...

• Register yourself in the Opencampus Mattermost Chat

• Register yourself in Coursera and for the Deep Learning Specialization, and enroll at least in the first course Neural Neworks and Deep Learning.

This week you will...

• discuss the definition of your project for the course.

• discuss issues of Niels Rogge's video on Fine-Tuning

Learning Resources

Until next week you should...

• answer the following two questions: - Which model do you want to fine-tune? - How do you want to evaluate it?

• do the short course Evaluating and Debugging Generative AI Models Using Weights and Biases (1 hour).

To-Do (Until 19/11/2024)

Homework:

• Do days 6 & 7 of the course

  • Watch the videos

  • Do the interactive coding exercises

Totally Optional

• If you are interested in learning more about functions, the Harvard material covers them in Week 0

• Watch lecture 3 of the Havard Python Course:

For the best learning experience, it's recommended to watch the 1-hour lecture twice: first for a quick overview, and then more slowly while taking notes. Please ensure you allocate sufficient time for this.

• Work through the following homework assignment

  • https://colab.research.google.com/drive/17GV7OC2AS76dHHrgHzQj4UiqJZY7p4XP?usp=sharing

Always first create a copy with "Copy to Drive" or download the notebooks to work locally on them. Otherwise your progress won't be saved!!

This week you will...

• learn about different ways of using special tokens for instruction tuning

• discuss problems in model evaluation

Learning Resources

• Notebook showing different examples of tokenizations

Until next week you should...

• focus on implementing the evaluation chain if you do not have one yet

The conditions to be met in order to receive a Certificate of Achievement and the ECTS are:

• Attendance to at least 80% of the classes (it is allowed to miss maximum 2 times)

• Delivery of the project with the needed documentation.

Attendance:

Since the weekly session will be on Zoom, please use your full name when you join the Zoom Meeting. The full name should be the same that you used to register at edu.opencampus.sh, because we have an automatic check.

Projects:

Check the Projects section to learn more about the projects.

Coursera:

Each weekly session is complemented with the videos and homework from the Coursera courses. Going through the video and doing the assignment allows you to learn and understand each session, so it is required for the course.

However, Coursera is indipendent from us and the completion of the Coursera assignment is NOT needed for the Opencampus Certificate. Completing all assignment will give you the Coursera Certificates (which is different)

This week you will...

• discuss the definition of your project for the course.

• discuss issues of Niels Rogge's video on Fine-Tuning

Learning Resources

• from Niels Rogge

Until next week you should...

• do a “literature review” for your project.

• watch the first two chapters of (“Asking LLMs for Structured Data” and “Prompting LLMs”).

• note a least one question on the videos above.

To-Do (Until 03/12/2024)

Homework:

• Do days 12 & 13 of the course

  • Watch the videos

  • Do the interactive coding exercises

• We will skip days 14 & 15, but you can still do them for practice

Local Setup Instructions

Try to follow these instructions as far as possible, but at least step one. In the session we will walk through the setup process.

• Install Anaconda Distribution and Visual Studio Code

  • (If you are comfortable with the command line, you can just install , and VS Code to save on disk space)

  • &

  • Mac users: Move from Downloads to Applications to install VS Code system-wide

• Launch Anaconda Navigator

  • Under Environments create a new environment, e.g. python_course, with a recent version of python (3.12+)

  • In the package listing for the environment change filter from „installed“ to „all“ then search and install the package ipykernel

  • Under Home launch VS Code (if not available launch it from system)

• In VS Code:

  • Install the extensions Python and Jupyter (both signed by Microsoft)

  • Create a new file: File > New File > Jupyter Notebook

  • Select Kernel > Another Kernel > Python Environments > python_course or whichever environment you just created

• Start Coding!

Totally Optional

• Watch lecture 6 of the Havard Python Course:

For the best learning experience, it's recommended to watch the lecture twice: first for a quick overview, and then more slowly while taking notes. Please ensure you allocate sufficient time for this.

• Work through the following homework assignment

This week you will..

Get practical hints about initialization and regularization techniques to avoid overfitting and improving the training of a neural network.

Learning Resources

Slides

Until next week you should...

• Form the groups, decide the project and communicate it to the teacher.

• Finish the second week of the Improving Deep Neural Network Course

• Do the Programming Assignment

This week you will..

See Mini-batches, Momentum, RMSProp and AdamOptimizer: an overview of optimization algorithm to train faster neural networks.

Learning Resources

Slides

This week you will...

• Check if everything worked with the tools we started using

• Have the first session with a small quiz and round of discussion.

• Discuss about python environment, dot product against element wise multiplication,

• Do you first exercise session training a small neural network recognizing cats!

Learning Resources

Slides

Useful (external) Resourcers

Until next week you should...

This week you will...

check weights initialization in the training and notebook example of planar data classification changing the number of hidden unit in a shallow network - only 1 hidden layer.

Learning Resources

Slides

Useful (external) Resources

Until next week you should...

• Finish the fourth week of the course

• Finish both assignments

• Think about your project: prepare an idea and find other people willing to collaborate (there is time also next week, but please start)

This week you will...

• Explore deep neural network.

• First example of generalizing a neural network with L layers.

• Discussion and choice about the projects.

Learning Resources

Projects

Until next week you should...

• Create a group for your project!

• Finish the first week of the Improving Deep Neural Network Course

• Do the Programming Assignments: Initialization, Regularization and Gradient Checking

The Deep Learning course will guide you through the mathematics background of machine learning approaches. We will start from a simple neural network and go through the different components of a network to understand and be able to create your own project.

The Objective of the Course

The aim of this course is to develop a deeper understanding of how and why neural network work. The first part will be devoted to understanding and implementing the basic behind most of the neural network approaches, the forward- and back-propagation, loss function, optimization, training, hyper-parameters tuning and analysis.

To gain a better understanding, we will implement those part in python (mostly using the numpy library). These methods already exists in popular frameworks (like Tensorflow or Pytorch, to cite a few), but using them without knowledge may be confusing.

During the course, we will have weekly discussion to deeper our understanding of the subject and also you will work on your own project.

Requirements and Motivation

In order to get the best out of this course, some previous knowledge is required. We expect the participant to have an understanding about the fundaments of mathematics (not being afraid of derivatives), linear algebra (mostly matrix multiplication) and python programming.

Based on the past semester, the estimated time is around 5 hours a week, ranging usually from 3 to 8 depending on the week's material. The project will start after 4 weeks of the course and will take some additional hours. However, assignment will decrease in the end of the course to leave you space for the project. Be sure to allocate enough time to manage to get through the whole course. If in doubt, ask us for advice.

The Project

Groups of students should be formed to work on a project. The project idea can come from the student, from a template or proposed from us. The project is needed in order to finish the course, and a final presentation will be given in the last week of the course.

The Course Material

The course will be held weekly and will constitute of an online session of 1 hour and a half. The material and slides for each session are found in each week's page.

ECTS

For further details about Certificates and ECTS please refer to the following page:

This week you will...

• Receive an introduction about the course and the people in it. A short overview of the course, contents and how it will work.

• Information about accounts, forum and contacts are provided.

Learning Resources

Until next week you should...

• Register in the Deep Learning Channel in the Mattermost Chat

• Register on Coursera and start the Neural Networks and Deep Learning course,

• Finish the first two weeks of the course

• Do the Programming Assignment on Logistic Regression

• Do the Programming Assignment on Python Numpy