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://t.iss.one/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.

---

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

---

3. Applications of RNNs

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

---

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)

---

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

---

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.

---

Exercise

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

---

#RNN #DeepLearning #SequentialData #TimeSeries #NLP

https://t.iss.one/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.

---

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.

---

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)

---

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)

---

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.

---

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)

---

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.

---

Exercise

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

---

#RNN #BidirectionalRNN #DeepLearning #TimeSeries #NLP

https://t.iss.one/DataScienceM

Topic: RNN (Recurrent Neural Networks) – Part 4 of 4: Advanced Techniques, Training Tips, and Real-World Use Cases

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1. Advanced RNN Variants

• Bidirectional LSTM/GRU: Processes the sequence in both forward and backward directions, improving context understanding.

• Stacked RNNs: Uses multiple layers of RNNs to capture complex patterns at different levels of abstraction.

nn.LSTM(input_size, hidden_size, num_layers=2, bidirectional=True)

---

2. Sequence-to-Sequence (Seq2Seq) Models

• Used in tasks like machine translation, chatbots, and text summarization.

• Consist of two RNNs:

* Encoder: Converts input sequence to a context vector
* Decoder: Generates output sequence from the context

---

3. Attention Mechanism

• Solves the bottleneck of relying only on the final hidden state in Seq2Seq.

• Allows the decoder to focus on relevant parts of the input sequence at each step.

---

4. Best Practices for Training RNNs

• Gradient Clipping: Prevents exploding gradients by limiting their values.

torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)

• Batching with Padding: Sequences in a batch must be padded to equal length.

• Packed Sequences: Efficient way to handle variable-length sequences in PyTorch.

packed_input = nn.utils.rnn.pack_padded_sequence(input, lengths, batch_first=True)

---

5. Real-World Use Cases of RNNs

• Speech Recognition – Converting audio into text.

• Language Modeling – Predicting the next word in a sequence.

• Financial Forecasting – Predicting stock prices or sales trends.

• Healthcare – Predicting patient outcomes based on sequential medical records.

---

6. Combining RNNs with Other Models

• RNNs can be combined with CNNs for tasks like video classification (CNN for spatial, RNN for temporal features).

• Used with transformers in hybrid models for specialized NLP tasks.

---

Summary

• Advanced RNN techniques like attention, bidirectionality, and stacked layers make RNNs powerful for complex tasks.

• Proper training strategies like gradient clipping and sequence packing are essential for performance.

---

Exercise

• Build a Seq2Seq model with attention for English-to-French translation using an LSTM encoder-decoder in PyTorch.

---

#RNN #Seq2Seq #Attention #DeepLearning #NLP

https://t.iss.one/DataScience4M

Topic: Handling Datasets of All Types – Part 4 of 5: Text Data Processing and Natural Language Processing (NLP)

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1. Understanding Text Data

• Text data is unstructured and requires preprocessing to convert into numeric form for ML models.

• Common tasks: classification, sentiment analysis, language modeling.

---

2. Text Preprocessing Steps

• Tokenization: Splitting text into words or subwords.

• Lowercasing: Convert all text to lowercase for uniformity.

• Removing Punctuation and Stopwords: Clean unnecessary words.

• Stemming and Lemmatization: Reduce words to their root form.

---

3. Encoding Text Data

• Bag-of-Words (BoW): Represents text as word count vectors.

• TF-IDF (Term Frequency-Inverse Document Frequency): Weighs words based on importance.

• Word Embeddings: Dense vector representations capturing semantic meaning (e.g., Word2Vec, GloVe).

---

4. Loading and Processing Text Data in Python

from sklearn.feature_extraction.text import TfidfVectorizer

texts = ["I love data science.", "Data science is fun."]
vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(texts)

---

5. Handling Large Text Datasets

• Use libraries like NLTK, spaCy, and Transformers.

• For deep learning, tokenize using models like BERT or GPT.

---

6. Summary

• Text data needs extensive preprocessing and encoding.

• Choosing the right representation is crucial for model success.

---

Exercise

• Clean a set of sentences by tokenizing and removing stopwords.

• Convert cleaned text into TF-IDF vectors.

---

#NLP #TextProcessing #DataScience #MachineLearning #Python

https://t.iss.one/DataScienceM

# 📚 PyTorch Tutorial for Beginners - Part 4/6: Sequence Modeling with RNNs, LSTMs & Attention
#PyTorch #DeepLearning #NLP #RNN #LSTM #Transformer

Welcome to Part 4 of our PyTorch series! This comprehensive lesson dives deep into sequence modeling, covering recurrent networks, attention mechanisms, and transformer architectures with practical implementations.

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## 🔹 Introduction to Sequence Modeling
### Key Challenges with Sequences
1. Variable Length: Sequences can be arbitrarily long (sentences, time series)
2. Temporal Dependencies: Current output depends on previous inputs
3. Context Preservation: Need to maintain long-range relationships

### Comparison of Approaches
| Model Type | Pros | Cons | Typical Use Cases |
|------------------|---------------------------------------|---------------------------------------|---------------------------------|
| RNN | Simple, handles sequences | Struggles with long-term dependencies | Short time series, char-level NLP |
| LSTM | Better long-term memory | Computationally heavier | Machine translation, speech recognition |
| GRU | LSTM-like with fewer parameters | Still limited context | Medium-length sequences |
| Transformer | Parallel processing, global context | Memory intensive for long sequences | Modern NLP, any sequence task |

---

## 🔹 Recurrent Neural Networks (RNNs)
### 1. Basic RNN Architecture

class VanillaRNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.hidden_size = hidden_size
        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)
        
    def forward(self, x, hidden=None):
        # x shape: (batch, seq_len, input_size)
        out, hidden = self.rnn(x, hidden)
        # Only use last output for classification
        out = self.fc(out[:, -1, :])  
        return out

# Usage
rnn = VanillaRNN(input_size=10, hidden_size=20, output_size=5)
x = torch.randn(3, 15, 10)  # (batch=3, seq_len=15, input_size=10)
output = rnn(x)

### 2. The Vanishing Gradient Problem
RNNs struggle with long sequences due to:
- Repeated multiplication of small gradients through time
- Exponential decay of gradient information

Solutions:
- Gradient clipping
- Architectural changes (LSTM, GRU)
- Skip connections

---

## 🔹 Long Short-Term Memory (LSTM) Networks
### 1. LSTM Core Concepts

Key Components:
- Forget Gate: Decides what information to discard
- Input Gate: Updates cell state with new information
- Output Gate: Determines next hidden state

### 2. PyTorch Implementation

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super().__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, 
                           batch_first=True, dropout=0.2 if num_layers>1 else 0)
        self.fc = nn.Linear(hidden_size, output_size)
        
    def forward(self, x):
        # Initialize hidden state and cell state
        h0 = torch.zeros(self.lstm.num_layers, x.size(0), 
                        self.lstm.hidden_size).to(x.device)
        c0 = torch.zeros_like(h0)
        
        out, (hn, cn) = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

# Bidirectional LSTM example
bidir_lstm = nn.LSTM(input_size=10, hidden_size=20, num_layers=2,
                    bidirectional=True, batch_first=True)

# Learning rate scheduler for transformers
def lr_schedule(step, d_model=512, warmup_steps=4000):
    arg1 = step ** -0.5
    arg2 = step * (warmup_steps ** -1.5)
    return (d_model ** -0.5) * min(step ** -0.5, step * warmup_steps ** -1.5)

---

### **📌 What's Next?
In **Part 5, we'll cover:
➡️ Generative Models (GANs, VAEs)
➡️ Reinforcement Learning with PyTorch
➡️ Model Optimization & Deployment
➡️ PyTorch Lightning Best Practices

#PyTorch #DeepLearning #NLP #Transformers 🚀

Practice Exercises:
1. Implement a character-level language model with LSTM
2. Add attention visualization to a sentiment analysis model
3. Build a transformer from scratch for machine translation
4. Compare teacher forcing ratios in seq2seq training
5. Implement beam search for decoder inference

# Character-level LSTM starter
class CharLSTM(nn.Module):
    def __init__(self, vocab_size, hidden_size, n_layers):
        super().__init__()
        self.embed = nn.Embedding(vocab_size, hidden_size)
        self.lstm = nn.LSTM(hidden_size, hidden_size, n_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, vocab_size)
        
    def forward(self, x, hidden=None):
        x = self.embed(x)
        out, hidden = self.lstm(x, hidden)
        return self.fc(out), hidden