diff --git a/Introduction/2-history-of-ML/README.md b/Introduction/2-history-of-ML/README.md index ce761a9802f71f78eb2980fa50c3913668ad388f..fed3e3a9b03ffe482412db46923bcc97d20063df 100644 --- a/Introduction/2-history-of-ML/README.md +++ b/Introduction/2-history-of-ML/README.md @@ -17,10 +17,13 @@ The history of Artificial Intelligence as a field is intertwined with the histor - 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) is a type of linear classifier invented by American psychologist Frank Rosenblatt that underlies advances in deep learning. - 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) is an algorithm originally designed to map routes. In an ML context it is used to detect patterns. - 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) is used to train [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network) -- 1982 [Recurrent Neural Network](https://wikipedia.org/wiki/Recurrent_neural_network) are artificial neural networks derived from feedforward neural networks that create temporal graphs. +- 1982 [Recurrent Neural Network](https://wikipedia.org/wiki/Recurrent_neural_network) are artificial neural networks derived from feedforward neural networks that create temporal graphs. + +✅ Do a little research. What other dates stand out as pivotal in the history of ML and AI? + ## 1950: Machines that Think -Alan Turing +Alan Turing, a truly remarkable person who was voted [by the public in 2019](https://en.wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) as the greatest scientist of the 20th century, was ## 1956: Dartmouth Research Project @@ -80,7 +83,7 @@ AI started as a single area, now there are many parts and they cross-collaborate [](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of Deep Learning") > Yann LeCun discusses the history of Deep Learning in this lecture -✅ Knowledge Check - use this moment to stretch students' knowledge with open questions + ## 🚀Challenge Add a challenge for students to work on collaboratively in class to enhance the project diff --git a/NLP/1-Introduction-to-NLP/README.md b/NLP/1-Introduction-to-NLP/README.md index b008936613433ff82b91806b10bfe720a6459357..e4c273d2bdd42a8758e80e734e727acde24c477d 100644 --- a/NLP/1-Introduction-to-NLP/README.md +++ b/NLP/1-Introduction-to-NLP/README.md @@ -114,7 +114,7 @@ Choose one of the "stop and consider" elements above and either try to implement In the next lesson, you'll learn about a number of other approaches to parsing natural language and machine learning. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/30/) +## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/30/) ## Review & Self Study diff --git a/NLP/2-Tasks/README.md b/NLP/2-Tasks/README.md index a32583422acb0af472d2e04fce20c2adc994b109..b50eef5e9cb88314890b0698311993a0b72a5559 100644 --- a/NLP/2-Tasks/README.md +++ b/NLP/2-Tasks/README.md @@ -179,7 +179,7 @@ One possible solution to the task is [here](solution/bot.py) Take a task in the prior knowledge check and try to implement it. Test the bot on a friend. Can it trick them? Can you make your bot more 'believable?' -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/) +## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/) ## Review & Self Study diff --git a/NLP/3-Translation-Sentiment/README.md b/NLP/3-Translation-Sentiment/README.md index e276bf6ed66b353a7986efeaa9d20669079954f0..e47b8d3a06bada3765310dde36e5594cbf30dc93 100644 --- a/NLP/3-Translation-Sentiment/README.md +++ b/NLP/3-Translation-Sentiment/README.md @@ -138,7 +138,7 @@ Here is a sample [solution](solutions/book.py). Can you make Marvin even better by extracting other features from the user input? -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/34/) +## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/34/) ## Review & Self Study