opencampus.sh Machine Learning Program
  • opencampus.sh Machine Learning Program
  • Course Kick-Off
  • How do I choose a course?
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  • Courses
    • Einführung in Data Science und maschinelles Lernen
      • Bedingungen für ein Leistungszertifikat oder ECTS
      • Vorbereitung
      • Woche 1 - Einführung in Data Science
      • Woche 2 - Import und Visualisierung von Daten
      • Woche 3 - Versionierung mit git (Teil 1) und Datenaufbereitung
      • Woche 4 - Versionierung mit git (Teil 2) und aktuelle Entwicklungen im Bereich ML
      • Woche 5 - Einführung in das maschinelle Lernen
      • Woche 6 - Overfitting und Regularisierung
      • Woche 7 - Neuronale Netze
      • Woche 8 - Fehlende Werte
      • Woche 9 - Zeitreihenanalysen
      • Woche 10 - Projektpräsentationen
    • Machine Learning with TensorFlow
      • Requirements for a Certificate of Achievement or ECTS
      • Preparation
      • Week 1 - General Introduction
      • Week 2 - Introduction to TensorFlow,Part I
      • Week 3 - Introduction to TensorFlow,Part II
      • Week 4 - Convolutional Neural Networks, Part I
      • Week 5 - Convolutional Neural Networks, Part II
      • Week 6 - Natural Language Processing, Part I
      • Week 7 - Natural Language Processing, Part II
      • Week 8 - Project Work
      • Week 9 - Sequences, Time Series and Prediction, Part I
      • Week 10 - Sequences, Time Series and Prediction, Part II
      • Week 11 & 12 - Presentation of the Final Projects
    • Intermediate Machine Learning
      • Hello and welcome😊
      • Prequisites
      • Week 1 - Course Introduction
        • Cousera Videos
      • Week 2 - Recap ML Basics, Intro to PyTorch
        • Coursera Videos
      • Week 3 - Intro Kaggle competition - EDA and baseline models with PyTorch
        • Coursera Videos
      • Week 4 - Convolutional Neural Networks
      • Week 5 - Recurrent Neural Networks
        • Cousera Videos
      • Week 6 - CNN and RNN Applications
        • Cousera Videos
      • Week 7 - Transformers & Hugging Face
      • Week 8-10 - Kaggle competiton sessions
      • Week 11 - Final Presentations
    • Advanced Time Series Prediction
      • Requirements for a Certificate of Achievement or ECTS
      • Projects & Frameworks
      • Lecture material + YouTube
      • References / Books
      • Week 1 - Intro + Organisation
      • Week 2 - SARIMA(X) + GARCH-Models
      • Week 3 - Labour Day
      • Week 4 - State-Space models // Filtering
      • Week 5 - Dependence concepts: Copula // Gaussian Processes // RMT
      • Week 6 - Extremes // Anomalies // Signatures
      • Week 7 - Tree models: XGBoost // LightGBM // CatBoost
      • Week 8 - (Deep) recurrent architectures for time series data
      • Week 9 - Transformers + TemporalFusionTransformers
      • Week 10 - NBEATS(x) + NHITS
      • Week 11 - LLM for time series problems
      • Week 12 - Final Presentations
      • Week 13 - Final Presentations (Back-Up)
    • Python: From Beginner to Practitioner
      • Course Info
      • Week 1
      • Week 2
      • Week 3
      • Week 4
      • Week 5
      • Week 6
      • Week 7
      • Week 8
      • Week 9
      • Week 10
      • Week 11
      • Final Project
      • Resources
    • Fine-Tuning and Deployment of Large Language Models
      • Requirements for a Certificate of Achievement or ECTS
      • Preparation
      • Week 1 - General Introduction
      • Week 2 - Project Definition and Introduction to Fine-Tuning
      • Week 3 - Fine-Tuning Characteristics
      • Week 4 - Model Evaluation
      • Week 8 - Tokenization for Instruction Tuning
      • Week 9 - Efficient Inference
      • Week 10 - Project Presentations
    • Archive
      • Deep Learning from Scratch
        • Requirements for a Certificate of Achievement or ECTS
        • Preparation
        • Week 1 - General Introduction
        • Week 2 - Introduction to Deep Learning and Neural Network Basics
        • Week 3 - Shallow Neural Networks
        • Week 4 - Deep Neural Networks
        • Week 5 - Practical Aspects of Deep Learning
        • Week 6 - Optimization Algorithms
        • Week 7 - Hyperparameter Tuning
        • Week 8 - Machine Learning Strategy 1 & 2
        • Week 9 - Neural Networks Architecture | Project Checkpoint
        • Week 10 - Bonus: most voted topic
        • Week 11 - Presentation of Final Projects, Part I
        • Week 12 - Presentation of Final Projects, Part II
      • Deep Learning for Computer Vision
        • Requirements for a Certificate of Achievement or ECTS
        • Preparation
        • Week 1 - General Introduction
        • Week 2 - Foundations of Convolutional Neural Networks
        • Week 3 - Convolution Model Application
        • Week 4 - Residual Networks
        • Week 5 - Transfer Learning
        • Week 6 - Detection Algorithms
        • Week 7 - Project Checkpoint | Image Segmentation
        • Week 8 - Face Recognition
        • Week 9 - Art Generation with Neural Style Transfer
        • Week 10 - CNN Bonus
        • Week 11 - Final Presentation of the Projects
      • Application of Transformer Models
        • Requirements for a Certificate of Achievement or ECTS
        • Week 1 - General Introduction
        • Week 2 - Self-Attention and Prompt Design
        • Week 3 - Introduction to Transformer Models
        • Week 4 - Fine-Tuning Pretrained Models
        • Week 5 - The Datasets Library
        • Week 6 - The Tokenizers Library
        • Week 7 - Main NLP Tasks
        • Week 8 - Presentation of the Final Projects
      • Generative Adversarial Networks
        • Requirements for a Certificate of Achievement or ECTS
        • Preparation
        • Motivation - Things you can do with NLP
        • Week 1 - General Introduction to the course
        • Week 2 - Sentiment Analysis with Logistic Regression
        • Week 3 - Sentiment Analysis with Naïve Bayes
        • Week 4 - Vector Space Models
        • Week 5 - Machine Translation and Document Search
        • Week 6 - Autocorrect
        • Week 7 - Part of Speech Tagging and Hidden Markov Models
        • Week 8 - Autocomplete and Language Models
        • Week 9 - Word embeddings with neural networks
        • Week 10 - Final Projects
      • Lehren und Lernen mit KI
        • Woche 1 - Einführung
        • Woche 2 - Anwendungsbeispiele #twlz
        • Woche 3 - KI-Tools für den Bildungsbereich
        • Woche 4 - Nicht-technische Einführung in die KI
        • Woche 5 - Kreatives Schreiben
        • Woche 6 - Automatische Klassifizierung von Textantworten
        • Woche 7 - IQSH Handreichung zu CHatGPT
        • Woche 8 - Veränderungen in benötigten Kompetenzen
        • Woche 9 - Präsentation Abschlussprojekte
      • Reinforcement Learning
      • Machine Learning Operations (MLOps)
        • 19-04-2023 - General Introduction
        • 26-04-2023 ML Lifecycle Overview and Model Selection
        • 03-05-2023 Data Definition and Collection
        • 10-05-2023 From Feature Engineering to Data Storage
        • 17-05-2023 Advanced Data Processing & Intro into Model Serving
        • 24-05-2023 Model Infrastructure & Delivery
        • 31-05-2023 Model Monitoring
        • 07-06-2023 Project Presentations
      • Mathematik für maschinelles Lernen
      • TensorFlow Course: Week 10 - Special Issues Considering Your Final Projects
      • Deep Dive into LLMs
        • Week 1 - Introduction
        • Week 2 - Tokens & Embeddings revisted
        • Week 3 - Introduction to Transformers
        • Week 4 - Prompt Engineering
          • Chain of Thought
          • TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks
          • More techniques
        • Week 5 - RAG and Agents
        • Week 6 - Model Evaluation
        • Week 7 - Fine-Tuning I
        • Week 8 - Fine-Tuning II and Model Inference
        • Week 9 - Advisory Session
        • Week 10 - Project Presentations
      • Intermediate Machine Learning (Legacy SS2023)
        • Hello and welcome😊
        • Prequisites
        • Week 1 - Course Introduction
        • Week 2 - Recap ML Basics, Intro to PyTorch
        • Week 3 - Intro Kaggle competition - EDA and baseline models with PyTorch
        • Week 4 - Convolutional Neural Networks
        • Week 5 - Recurrent Neural Networks
        • Week 6 - CNN and RNN Applications
        • Week 7 - Transformers Part 1
        • Week 8 - Transformers Part 2
        • Week 9 - Vision Transformers
        • Week 10-12 - Projects sessions
        • Week 13 - Project Presentations
        • Week 14+
      • Practical Engineering with LLMs
        • Week 1- General Introduction
        • Week 2 - Prompt Engineering
        • Week 3 - Introduction to LangChain
        • Week 4 - Introduction to Retrieval Augmented Generation
        • Week 5 - Advanced Retrieval Augmented Generation
        • Week 6 - Building User Interfaces with Gradio
        • Week 7 - Evaluation of LLM outputs and structured outputs
        • Week 8 - Open-Source LLMs
        • Week 9 - Project Presentations
      • Python: From Beginner to Practictioner (Legacy WS2023)
        • Course Info
        • Week 1
        • Week 2
        • Week 3
        • Week 4
        • Week 5
        • Week 6
        • Week 7
        • Week 8
        • Week 9
        • Solutions & Materials
      • Machine Learning für die Medizin
        • Bedingungen für ein Leistungszertifikat oder ECTS
        • Vorbereitung
      • Time Series Prediction
        • Requirements for a Certificate of Achievement or ECTS
        • Projects & Frameworks
        • Preparation / YouTube
        • References / Books
        • Week 1 - Intro + Organisation
        • Week 2 - Forecasting basics with trends: AR + MA-models
        • Week 3 - Covering seasonality: From ARMA to SARIMA-models
        • Week 4 - Towards multidimensional settings: SARIMAX + VAR-models
        • Week 5 - Non-Stationary model classes: GARCH + DCC-GARCH
        • Week 6 - Copula Methods
        • Week 7 - Milestone Meeting + Spectral Analysis of Time Series + Kalman-Filtering
        • Week 8 - Supervised Learning I: Trees + Random Forests + Boosting
        • Week 9 - Supervised Learning II: XGBoost + LightGBM + CatBoost
        • Week 10 - Neural Networks for Sequences: RNNs + GRUs + LSTMs + LMUs
        • Week 11 - Prophet(Facebook) + DeepAR(Amazon) + GPVAR
        • Week 12 - Transformers + TFTs
        • Week 13 - NBEATS(s) + NHITS(x)
        • Week 14 - Final Presentation
      • Python: From Beginner to Practitioner (Legacy 2024S)
        • Course Info
        • Week 1
        • Week 2
        • Week 3
        • Week 4
        • Week 5
        • Week 6
        • Week 7
        • Week 8
        • Week 9
        • Week 10
        • Week 11
        • Week 12
        • Material
  • Events
    • Coding.Waterkant 2023
    • Prototyping Week
  • Projects
    • How to Start, Complete, and Submit Your Project
    • Possible Projects
    • Past Projects
  • Additional Resourses
    • Glossary
    • Coursera
    • Selecting the Optimizer
    • Choosing the Learning Rate
    • Learning Linear Algebra
    • Learning Python
    • Support Vector Machines
    • ML Statistics
  • Tools
    • Git
    • RStudio
    • Google Colab
    • Zoom
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  1. Courses
  2. Intermediate Machine Learning

Week 1 - Course Introduction

PreviousPrequisitesNextCousera Videos

Last updated 4 months ago

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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:

  • https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html (First part)

  • https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial2/Introduction_to_PyTorch.html

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