Attention Is All You Need (Fully Explained) скачать в хорошем качестве

Attention Is All You Need (Fully Explained)

In this video, we break down the groundbreaking paper "Attention Is All You Need" by Vaswani et al. (2017), which introduced the Transformer architecture — the foundation of today’s most powerful AI models like BERT, GPT, and many others. We’ll explore: Why the authors moved away from recurrent and convolutional networks How the self-attention mechanism works Key innovations such as positional encoding, multi-head attention, and feed-forward layers The impact of Transformers on modern Natural Language Processing and beyond Whether you’re a student, researcher, or just curious about how today’s AI works, this video will give you a clear and intuitive understanding of the Transformer architecture. 🔑 Key Topics Covered: The problem with RNNs and CNNs Self-attention explained simply The architecture of the Transformer Real-world applications and legacy If you find this helpful, don’t forget to like, share, and subscribe for more AI and machine learning explainers! #Transformer #DeepLearning #MachineLearning #AI #ArtificialIntelligence #NLP #NaturalLanguageProcessing #NeuralNetworks #SelfAttention #MultiHeadAttention #PositionalEncoding #GenerativeAI #LargeLanguageModels #LLM #GPT #BERT #ResearchPaper #AIEducation #TechExplained #AttentionIsAllYouNeed