how transformers remember facts

#Transformers #NLP #LLM #MachineLearning #DeepLearning #AI #ArtificialIntelligence #TechInnovation #DataScience #NeuralNetworks

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Discover an incredible LLM course designed to deepen your understanding of the transformer architecture and its role in building powerful Large Language Models (LLMs). This course breaks down complex concepts into easy-to-grasp modules, making it perfect for both beginners and advanced learners. Dive into the mechanics of attention mechanisms, encoding-decoding processes, and much more. Elevate your AI knowledge and stay ahead in the world of machine learning!

Enroll Free: https://www.deeplearning.ai/short-courses/how-transformer-llms-work/

#LLMCourse #Transformers #MachineLearning #AIeducation #DeepLearning #TechSkills #ArtificialIntelligence

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Last week we introduced how transformer LLMs work, this week we go deeper into one of its key elements—the attention mechanism, in a new #OpenSourceAI course, Attention in Transformers: Concepts and #Code in #PyTorch

Enroll Free: https://www.deeplearning.ai/short-courses/attention-in-transformers-concepts-and-code-in-pytorch/

#LLMCourse #Transformers #MachineLearning #AIeducation #DeepLearning #TechSkills #ArtificialIntelligence

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

# 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)

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### **📌 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

🌟 Vision Transformer (ViT) Tutorial – Part 1: From CNNs to Transformers – The Revolution in Computer Vision

Let's start: https://hackmd.io/@husseinsheikho/vit-1

#VisionTransformer #ViT #DeepLearning #ComputerVision #Transformers #AI #MachineLearning #NeuralNetworks #ImageClassification #AttentionIsAllYouNeed

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🌟 Vision Transformer (ViT) Tutorial – Part 2: Implementing ViT from Scratch in PyTorch

Let's start: https://hackmd.io/@husseinsheikho/vit-2

#VisionTransformer #ViTFromScratch #PyTorch #DeepLearning #ComputerVision #Transformers #AI #MachineLearning #CodingTutorial #AttentionIsAllYouNeed

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🌟 Vision Transformer (ViT) Tutorial – Part 3: Pretraining, Transfer Learning & Real-World Applications

Let's start: https://hackmd.io/@husseinsheikho/vit-3

#VisionTransformer #TransferLearning #HuggingFace #ImageNet #FineTuning #AI #DeepLearning #ComputerVision #Transformers #ModelZoo

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