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From Simple Perceptron to Transformer Master Neural Network

Name: From Simple Perceptron to Transformer Master Neural Network
Author: Baturi

Deep Learning Fundamentals: MLPs, CNNs, RNNs, LSTMs & Transformers with Hands-on Keras Labs | PhD-Level AI Course
What you'll learn
✓ Understand the mathematical foundations of perceptrons, MLPs, and backpropagation — not just use them, but truly grasp WHY they work
✓ Design and implement Convolutional Neural Networks for image recognition, understanding convolution, pooling, and feature extraction intuitively
✓ Build RNNs and LSTMs for sequential data, and understand how LSTM gates solve the vanishing gradient problem that limits standard RNNs
✓ Master the Transformer architecture including self-attention, positional encoding, and how modern LLMs like GPT process and generate text
✓ Implement neural networks from scratch using Keras and TensorFlow, with the ability to debug, optimize, and adapt models to new problems
✓ Read and understand AI research papers by connecting theoretical concepts to practical implementations covered throughout the course
Requirements
● Basic linear algebra: matrices, vectors, dot products, and matrix multiplication (you don't need to be an expert — we'll build intuition)
● Calculus fundamentals: derivatives, gradients, and the chain rule (essential for understanding backpropagation)
● Basic probability and statistics: distributions, Bayes' theorem, and expectation values
● Python programming with NumPy: comfortable writing Python code and working with arrays (intermediate level recommended)
● No prior deep learning experience required — we start from the very first neuron and build up systematically
Description
This course contains the use of artificial intelligence.
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.
Who this course is for
■ PhD Students and Academic Researchers You need deep theoretical understanding to advance your research. This course provides rigorous mathematical foundations while building the intuition necessary for innovation. Perfect preparation for reading papers and developing novel architectures.
■ Machine Learning Engineers You've completed tutorials and built models, but want to move beyond copy-pasting code. This course gives you the understanding to debug confidently, choose architectures intelligently, and optimize effectively in production environments
■ Data Scientists Transitioning to Deep Learning You have strong fundamentals in statistics and traditional ML, and want to add neural networks to your toolkit with genuine comprehension rather than superficial familiarity.
■ Software Engineers Entering AI You're a strong programmer who wants to understand AI systems deeply — not just use APIs, but truly grasp what's happening inside. This course bridges the gap between coding skills and AI understanding.
■ Technical Leaders and Architects You need to make informed decisions about AI systems, evaluate approaches, and communicate with ML teams. This course gives you the depth to understand tradeoffs and ask the right questions.
Homepage

https://www.udemy.com/course/transformer-neural-network/

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From Simple Perceptron to Transformer Master Neural Network

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