Transformer (LLM Course) скачать в хорошем качестве

Transformer (LLM Course)

🔴 Course : https://bigdatalandscape.gumroad.com/... Stop copying code you don't understand. Start building real AI intuition. Have you ever wondered how ChatGPT actually understands what you say? What's really happening inside these neural networks that can write, translate, and create? Most AI courses drown you in equations without building intuition, or have you copy-pasting code from tutorials without truly understanding what it does. When something breaks, you're completely lost. This course is different. I designed this PhD-level course to build your understanding from the ground up — starting with the simplest possible neural network (a single perceptron) and systematically building toward the transformer architecture that powers modern AI systems like GPT and BERT. What makes this course unique: Intuition-First Approach — Every concept is explained with clear visualizations and analogies before diving into the math. You'll understand WHY things work, not just HOW to implement them. Complete Historical Journey — Follow the actual evolution of neural networks from 1958 to today. Understanding this progression reveals why each architecture was invented and what problems it solves. Hands-On Labs with Real Code — 4 practical labs using Keras and TensorFlow where you'll build, train, and debug models yourself. No copy-pasting — you'll write the key components from understanding. PhD-Level Depth, Accessible Explanations — Rigorous mathematical foundations presented in a way that builds genuine comprehension. Perfect for researchers who need depth and practitioners who want to level up. Course Structure: The course follows a carefully designed progression: Neural Network Foundations — Perceptrons, activation functions, and the universal approximation theorem Multilayer Perceptrons — Backpropagation, gradient descent, and optimization techniques Convolutional Neural Networks — Convolution operations, pooling, feature extraction, and image recognition Recurrent Neural Networks — Sequential data, hidden states, and the vanishing gradient problem Long Short-Term Memory — Gates, cell states, and learning long-term dependencies Transformer Architecture — Self-attention, positional encoding, and how LLMs process information By the end of this course, you won't just know how to use these models — you'll understand them deeply enough to debug problems, choose the right architecture for your task, and even read cutting-edge research papers. Join thousands of learners who have transformed their understanding of AI. Your journey from perceptron to transformer starts now.