Categories
Machine Learning

• hyperparameter tuning

For this assignment, you will be designing and training a language model on the Penn Treebank (text-only), that is an unsupervised learning model.
Here are some things you may try:
RNN, LSTM, GRU, etc.
• Attention (covering next week)
• Different tokenizers
• Pretrained embeddings (word2vec, GloVe; note: you may NOT use any pretrained models such
as those in transformers)
• Hyperparameter tuning
• Regularization, dropout, etc.
Note you may not use any external libraries to write your model and training/eval loops, such as those in huggingface or keras.
And no Tensorflow, only pytorch. (Further details can be found in the instructions.pdf file)
If you have further questions or concerns, please don’t hesitate to contact me!

Categories
Machine Learning

Could you also please explain what you are doing in the code so that i can follow along?

Please help me do my HW2 using the two labs provided as guidelines. For Q3 on my homework, please use grid search, random search, and one other strategy that can be found in scikitlearn user guide on model tuning.
Could you also please explain what you are doing in the code so that I can follow along? Thank you.

Categories
Machine Learning

•attention

Please look at Instructions.png for further instructions, but in summary:
You will be designing and training a language model on the Penn Treebank and write a 1-2 page report on it (using the ACL stuff)
For the model, you can make use of
•RNN, LSTM, GRU, etc.
•Attention
•Different tokenizers
•Pretrained embeddings (word2vec, GloVe; note: you may NOT use any pretrained models or any external libraries to write your model and training/eval loops, such as those in huggingface or keras.
•Hyperparameter tuning
•Regularization, dropout, etc.
You’ll load the Penn Treebank using the load_dataset function as follows
from datasets import load_dataset
ptb = load_dataset(‘ptb_text_only’) # download the dataset

Categories
Machine Learning

Please help me do my hw2 using the two labs provided as guidelines.

Please help me do my HW2 using the two labs provided as guidelines. For Q3 on my homework, please use grid search, random search, and one other strategy that can be found in scikitlearn user guide on model tuning.
Could you also please explain what you are doing in the code so that I can follow along? Thank you.

Categories
Machine Learning

Note you may not use any external libraries to write your model and training/eval loops, such as those in huggingface or keras.

For this assignment, you will be designing and training a language model on the Penn Treebank (text-only), that is an unsupervised learning model.
Here are some things you may try:
RNN, LSTM, GRU, etc.
• Attention (covering next week)
• Different tokenizers
• Pretrained embeddings (word2vec, GloVe; note: you may NOT use any pretrained models such
as those in transformers)
• Hyperparameter tuning
• Regularization, dropout, etc.
Note you may not use any external libraries to write your model and training/eval loops, such as those in huggingface or keras.
And no Tensorflow, only pytorch. (Further details can be found in the instructions.pdf file)
If you have further questions or concerns, please don’t hesitate to contact me!

Categories
Machine Learning

•regularization, dropout, etc.

Please look at Instructions.png for further instructions, but in summary:
You will be designing and training a language model on the Penn Treebank and write a 1-2 page report on it (using the ACL stuff)
For the model, you can make use of
•RNN, LSTM, GRU, etc.
•Attention
•Different tokenizers
•Pretrained embeddings (word2vec, GloVe; note: you may NOT use any pretrained models or any external libraries to write your model and training/eval loops, such as those in huggingface or keras.
•Hyperparameter tuning
•Regularization, dropout, etc.
You’ll load the Penn Treebank using the load_dataset function as follows
from datasets import load_dataset
ptb = load_dataset(‘ptb_text_only’) # download the dataset

Categories
Machine Learning

•regularization, dropout, etc.

Please look at Instructions.png for further instructions, but in summary:
You will be designing and training a language model on the Penn Treebank and write a 1-2 page report on it (using the ACL stuff)
For the model, you can make use of
•RNN, LSTM, GRU, etc.
•Attention
•Different tokenizers
•Pretrained embeddings (word2vec, GloVe; note: you may NOT use any pretrained models or any external libraries to write your model and training/eval loops, such as those in huggingface or keras.
•Hyperparameter tuning
•Regularization, dropout, etc.
You’ll load the Penn Treebank using the load_dataset function as follows
from datasets import load_dataset
ptb = load_dataset(‘ptb_text_only’) # download the dataset

Categories
Machine Learning

Note you may not use any external libraries to write your model and training/eval loops, such as those in huggingface or keras.

For this assignment, you will be designing and training a language model on the Penn Treebank (text-only), that is an unsupervised learning model.
Here are some things you may try:
RNN, LSTM, GRU, etc.
• Attention (covering next week)
• Different tokenizers
• Pretrained embeddings (word2vec, GloVe; note: you may NOT use any pretrained models such
as those in transformers)
• Hyperparameter tuning
• Regularization, dropout, etc.
Note you may not use any external libraries to write your model and training/eval loops, such as those in huggingface or keras.
And no Tensorflow, only pytorch. (Further details can be found in the instructions.pdf file)
If you have further questions or concerns, please don’t hesitate to contact me!

Categories
Machine Learning

Please help me do my hw2 using the two labs provided as guidelines.

Please help me do my HW2 using the two labs provided as guidelines. For Q3 on my homework, please use grid search, random search, and one other strategy that can be found in scikitlearn user guide on model tuning.
Could you also please explain what you are doing in the code so that I can follow along? Thank you.

Categories
Machine Learning

Explain the tools and methods used

It will require a presentation. Please provide a detailed power point to read. Here is the requirement:
Apply to learn about ML
State a problem statement
Perform exploratory data analysis
This should include comments also
Pick 2-3 algorithms
Compare the results
Prepare a 3-5 minute presentation.
Introduce the data set
Explain the tools and methods used
Presentations Nov 12th during a class meeting.
Submit the following:
Data file
CSV or python file (any code file)
Presentation
A document explaining your project 150-250 words