From Simple Perceptron to Transformer : Master LLMs as PHD

Name: From Simple Perceptron to Transformer : Master LLMs as PHD
Rating: 4.0 (7 reviews)

Deep Learning Fundamentals: MLPs, CNNs, RNNs, LSTMs , MLLM & Transformers with Hands-on Keras Labs | PhD-Level AI Course

Created byBig Data Landscape

Last updated 6/2026

English

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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

Course content

1 section • 12 lectures • 2h 27m total length

Course Overview & Learning Objectives7:00
Neural Networks - Perceptron & Multilayer Perceptron - Activation Function13:50
Explore neural networks from perceptron to multilayer perceptron, learn how weights and activation functions drive learning, and master backpropagation and gradient descent for training.
Handson - Neural Network Training & Hyperparameter Tuning with Keras5:23
Convolutional Neural Networks (CNNs)9:51
Discover how convolutional neural networks preserve image structure and learn hierarchical features with filters, edge detection, convolution, ReLU, pooling, and fully connected layers for powerful computer vision.
Handson CNN8:08
Recurrent Neural Networks (RNNs)9:12
Long Short-Term Memory (LSTM) Networks9:27
Explore how long short-term memory networks address vanishing gradients, enable memory through the cell state, and optimize sequence modeling with gates for language, speech, and time series.
Handson : RNN vs LSTM Implementation - Sentiment Analysis8:59
Transformers - Attention Mechanism & Large Language Models (LLMs)12:57
Implement GPT from Scratch22:34
Learn to implement a GPT from scratch using self-attention transformers, character-level tokenization on Shakespeare data, and building from a simple bigram baseline to a full autoregressive language model.
Vision Language Model7:39
Learn how a vision encoder and a language model form an llm that performs cross-modal reasoning by aligning images into its token space through the connector.
Multimodal LLM - MLLM (PHD Level)32:17
Explore multimodal large language models that fuse vision and text through perception, alignment, and reasoning, enabling cross-lingual retrieval and document intelligence with CLIP-based backbones.

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

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.

This course contains the use of artificial intelligence.

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.