Kunal Pratap Singh kunalmessi10

## pad_packed_demo.py
import torch
from torch import LongTensor
from torch.nn import Embedding, LSTM
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
#
#     Step 1: Construct Vocabulary
#     Step 2: Load indexed data (list of instances, where each instance is list of character indices)

## gist:83d642af3b22811fac6719bf28ceb048

      
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                hardik2396
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              August 3, 2023 21:01
            
              
                Learning Path for Applied ML/DL
              
          
    LINEAR ALGEBRA


Chapter 2 of Deep Learning book (by Ian Goodfellow, short path) Link


Lecture series on Linear Algebra by three blue one brown (Highly recommended) Link  + singular value decomposition (shortpath).


And if you have sufficient time, then highly recommended to take “Introduction to Linear Algebra” by Gilbert Strang (on
YouTube, long path) Link.


PROBABILITY
	import torch
	from torch import LongTensor
	from torch.nn import Embedding, LSTM
	from torch.autograd import Variable
	from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

	## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
	#
	# Step 1: Construct Vocabulary
	# Step 2: Load indexed data (list of instances, where each instance is list of character indices)