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

Totally optional

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

Here different IDEs are presented and compared:

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!

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!

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:

• (Second part)

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!

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:

• (First part)

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

Course session

Walk-through

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.

Watch the videos on the next page

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

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:

😊😊

😊😊😊

Word Vectors

Attention Mechanism

1. Introduction to the Concept of Word Vectors

1. Words to Vectors

1. Example of Word Embeddings

1. Neural Model of Text

1. The Softmax Function

1. Methods for Learning Model Parameters

1. More Details on How to Learn Model Parameters

1. The Recurrent Neural Network

1. Long Short-Term Memory

1. Long Short-Term Memory Review

1. Use of LSTM for Text Synthesis

1. Simple and Effective Alternative Methods for Neural NLP

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

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!

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 () and go through a 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!