The Hundred-Page Language Models Book

Read it:
https://github.com/aburkov/theLMbook

#LLM #NLP #ML #AI #PYTHON #PYTORCH

https://xn--r1a.website/DataScienceM

Foundations of Large Language Models

Download it: https://readwise-assets.s3.amazonaws.com/media/wisereads/articles/foundations-of-large-language-/2501.09223v1.pdf

#LLM #AIresearch #DeepLearning #NLP #FoundationModels #MachineLearning #LanguageModels #ArtificialIntelligence #NeuralNetworks #AIPaper

Full PyTorch Implementation of Transformer-XL

If you're looking to understand and experiment with Transformer-XL using PyTorch, this resource provides a clean and complete implementation. Transformer-XL is a powerful model that extends the Transformer architecture with recurrence, enabling learning dependencies beyond fixed-length segments.

The implementation is ideal for researchers, students, and developers aiming to dive deeper into advanced language modeling techniques.

Explore the code and start building:
https://www.k-a.in/pyt-transformerXL.html

#TransformerXL #PyTorch #DeepLearning #NLP #LanguageModeling #AI #MachineLearning #OpenSource #ResearchTools

https://xn--r1a.website/CodeProgrammer

Topic: RNN (Recurrent Neural Networks) – Part 1 of 4: Introduction and Core Concepts

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1. What is an RNN?

• A Recurrent Neural Network (RNN) is a type of neural network designed to process sequential data, such as time series, text, or speech.

• Unlike feedforward networks, RNNs maintain a memory of previous inputs using hidden states, which makes them powerful for tasks with temporal dependencies.

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2. How RNNs Work

• RNNs process one element of the sequence at a time while maintaining an internal hidden state.

• The hidden state is updated at each time step and used along with the current input to predict the next output.

$$
h_t = \tanh(W_h h_{t-1} + W_x x_t + b)
$$

Where:

• $x_t$ = input at time step t
• $h_t$ = hidden state at time t
• $W_h, W_x$ = weight matrices
• $b$ = bias

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3. Applications of RNNs

• Text classification
• Language modeling
• Sentiment analysis
• Time-series prediction
• Speech recognition
• Machine translation

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4. Basic RNN Architecture

• Input layer: Sequence of data (e.g., words or time points)

• Recurrent layer: Applies the same weights across all time steps

• Output layer: Generates prediction (either per time step or overall)

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5. Simple RNN Example in PyTorch

import torch
import torch.nn as nn

class BasicRNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(BasicRNN, self).__init__()
        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        out, _ = self.rnn(x)  # out: [batch, seq_len, hidden]
        out = self.fc(out[:, -1, :])  # Take the output from last time step
        return out

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6. Summary

• RNNs are effective for sequential data due to their internal memory.

• Unlike CNNs or FFNs, RNNs take time dependency into account.

• PyTorch offers built-in RNN modules for easy implementation.

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Exercise

• Build an RNN to predict the next character in a short string of text (e.g., “hello”).

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#RNN #DeepLearning #SequentialData #TimeSeries #NLP

https://xn--r1a.website/DataScienceM

Topic: RNN (Recurrent Neural Networks) – Part 2 of 4: Types of RNNs and Architectural Variants

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1. Vanilla RNN – Limitations

• Standard (vanilla) RNNs suffer from vanishing gradients and short-term memory.

• As sequences get longer, it becomes difficult for the model to retain long-term dependencies.

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2. Types of RNN Architectures

• One-to-One
Example: Image Classification
A single input and a single output.

• One-to-Many
Example: Image Captioning
A single input leads to a sequence of outputs.

• Many-to-One
Example: Sentiment Analysis
A sequence of inputs gives one output (e.g., sentiment score).

• Many-to-Many
Example: Machine Translation
A sequence of inputs maps to a sequence of outputs.

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3. Bidirectional RNNs (BiRNNs)

• Process the input sequence in both forward and backward directions.

• Allow the model to understand context from both past and future.

nn.RNN(input_size, hidden_size, bidirectional=True)

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4. Deep RNNs (Stacked RNNs)

• Multiple RNN layers stacked on top of each other.

• Capture more complex temporal patterns.

nn.RNN(input_size, hidden_size, num_layers=2)

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5. RNN with Different Output Strategies

• Last Hidden State Only:
Use the final output for classification/regression.

• All Hidden States:
Use all time-step outputs, useful in sequence-to-sequence models.

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6. Example: Many-to-One RNN in PyTorch

import torch.nn as nn

class SentimentRNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(SentimentRNN, self).__init__()
        self.rnn = nn.RNN(input_size, hidden_size, num_layers=1, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        out, _ = self.rnn(x)
        final_out = out[:, -1, :]  # Get the last time-step output
        return self.fc(final_out)

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7. Summary

• RNNs can be adapted for different tasks: one-to-many, many-to-one, etc.

• Bidirectional and stacked RNNs enhance performance by capturing richer patterns.

• It's important to choose the right architecture based on the sequence problem.

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Exercise

• Modify the RNN model to use bidirectional layers and evaluate its performance on a text classification dataset.

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#RNN #BidirectionalRNN #DeepLearning #TimeSeries #NLP

https://xn--r1a.website/DataScienceM