Neural Networks & Transformer Models: Mastering Modern AI Foundations
A comprehensive 34-hour course exploring the theory, architecture, and implementation of neural networks and transformer models—the building blocks of today's most powerful AI systems. Designed for students and professionals with foundational machine learning knowledge ready to advance into deep learning.
Master perceptrons, activation functions, and feedforward neural networks. Learn backpropagation fundamentals and implement your first neural networks using common loss functions.
Specialized Architectures
Explore convolutional neural networks for image processing, recurrent neural networks for sequential data, and advanced architectures including VGG16, LSTMs, and GRUs.
Transformer Revolution
Dive into transformer architectures powering models like GPT and BERT. Master tokens, embeddings, attention mechanisms, and positional encoding fundamentals.
Advanced Applications
Learn model fine-tuning, knowledge distillation, reasoning capabilities, and applying evaluation metrics.
Practical Application
Apply learning through hands-on sessions, assignments, and a capstone project.
Advanced Network Architectures
1
Convolutional Neural Networks
Revolutionizing computer vision through specialized filter operations that detect spatial patterns in images. You'll implement convolution and pooling layers to build networks that can recognize objects, detect features, and process visual data with remarkable accuracy.
Convolution operations for feature extraction
Pooling techniques for dimensionality reduction
Classifier design for image recognition tasks
2
Recurrent Neural Networks
Processing sequential data with memory capabilities that recognize patterns over time. Learn how RNNs handle variable-length inputs like text and time series data, and why they're fundamental to language processing tasks.
Memory cells for temporal pattern detection
Handling gradient challenges in deep sequences
LSTM and GRU architectures for long-term dependencies
3
State-of-the-Art Models
Examining cutting-edge architectures that push the boundaries of what's possible with neural networks. You'll analyze VGG16, ResNet, and other influential models that have defined the field of deep learning in recent years.
Transfer learning with pre-trained models
Architectural innovations in modern networks
Performance benchmarks on standard datasets
Transformer Architecture & Attention Mechanisms
Tokenization & Embeddings
Convert raw text into tokens and transform them into rich vector representations that capture semantic relationships between words. Master techniques like Word2Vec and Byte Pair Encoding that form the foundation of modern NLP.
Converting text to numeric representations
Capturing semantic relationships in vector space
Position encoding to preserve sequence information
Self-Attention Mechanism
Understand the revolutionary attention concept that allows transformers to focus on relevant parts of the input sequence. Implement the Key-Query-Value paradigm that enables models to weigh information importance dynamically.
Computing attention scores between sequence elements
Parallel processing advantages over RNNs
Multi-head attention for multiple representation subspaces
Encoder-Decoder Structure
Examine the architecture behind models like BERT and GPT. Learn how encoders capture bidirectional context while decoders generate sequences, and how this design enables transformers to excel at diverse language tasks.
Encoder stacks for comprehensive context understanding
Decoder mechanics for generation tasks
Layer normalization and residual connections
Next Token Prediction
Master the fundamental training objective behind GPT and other generative models. Implement the mechanisms that allow these models to predict what comes next with remarkable coherence and contextual awareness.
Softmax output layer for token probability distribution
Training with teacher forcing
Evaluation metrics for generation quality
Applied Learning & Course Projects
Hands-On Assignments
Seven comprehensive take-home assignments provide practical implementation experience. Each assignment builds upon previous concepts and requires you to code neural networks and transformers from scratch or adapt existing architectures for specific tasks.
Implementing backpropagation algorithms
Building CNN image classifiers
Creating attention-based language models
Fine-tuning pre-trained transformers
Advanced Techniques
Explore cutting-edge methodologies like model fine-tuning with QLORA, quantization for efficiency, and reinforcement learning from human feedback. Learn how to optimize large models for deployment in resource-constrained environments.
Model distillation and compression
Chain-of-thought prompting
Mixture of Experts (MoE) architectures
Reinforcement Learning from Human Feedback (RLHF)
Capstone Project
Apply your knowledge to develop a complete neural network solution for a real business problem. You'll identify appropriate evaluation metrics, select suitable architectures, and present your solution to demonstrate mastery of the course material.