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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
Video Neural Networks Explained (12 minutes)
Introductory course on Python from Kaggle
Tutorial on Colab on Medium
complete week 1 and week 2 of the course Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
complete the two assignments given in the following notebooks:
register with Google and the Coursera platform to get corresponding accounts.
watch the videos Neural Networks from the Ground Up (19 minutes) and Gradient Descen- How Neural Networks Learn (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: https://www.kaggle.com/learn/python
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.
Week 1 and 2 of the course Convolutional Neural Networks in TensorFlow
complete week 3 and week 4 of the course Convolutional Neural Networks in TensorFlow
complete the exercise assignment in this notebook
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
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!
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.
think about project ideas to present them in class.
complete week 3 and week 4 of the course Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning.
complete the two assignments in the following notebooks:
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.
decide on a project for the course (post your ideas in the chat during the week)
complete the assignment in the following notebook:
Week 3 and 4 of the course
complete week 1 and week 2 of the course , including the provided assignments
The conditions to be met in order to receive a Certificate of Achievement (and ECTS) are:
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!
Check the Projects section to learn more about the projects.
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
Week 3 and 4 of the course Convolutional Neural Networks in TensorFlow
complete week 1 and week 2 of the course Natural Language Processing in TensorFlow
complete the exercise assignment in this notebook
investigate the characteristics of your project's dataset according to the instructions given here.
document your findings on the dataset characteristics according to the instructions of above
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.
Week 1 and 2 of the course Natural Language Processing in TensorFlow
complete week 3 and week 4 of the course Natural Language Processing in TensorFlow
complete the exercise assignment in this notebook
consider a baseline model or a baseline comparison for your project task according to the instructions given here.
document your decision according to the instructions given in the link above
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,
Week 3 and 4 of the course Sequences, Time Series and Prediction
complete your project work
prepare your project presnetation according to the instructions given here
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.
Week 3 and 4 of the course Natural Language Processing in TensorFlow
complete week 1 and week 2 of the course Sequences, Time Series and Prediction
complete Exercise 1 (Exercise 2 in the same notebook is for next week) assignments in this notebook.
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.
present your project in the final presentations. :-)
complete your project documentation and submit it according to the instructions given here
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.
Week 1 and 2 of the course Sequences, Time Series and Prediction
complete week 3 and week 4 of the course Sequences, Time Series and Prediction
complete the two assignments in the following notebooks:
TBD
working on the definition of your project's final model(s) and their evaluation according to the instructions given here.
document how you will evaluate your model results