Machine Learning & Deep Learning Foundations
Understand the mathematical foundations of Neural Networks, Transformers, Attention, and Fine-Tuning.
The assessment unlocks once you have worked through the lessons.
Independent provider. Not an accredited awarding body. Not a regulated qualification.
What You Will Master
- Understand forward propagation, loss functions, and gradient descent optimization
- Deconstruct Scaled Dot-Product Attention and Multi-Head Self-Attention mathematically
- Analyze Encoder-Only (BERT), Decoder-Only (GPT), and Encoder-Decoder (T5) architectures
- Understand Positional Encodings (Sinusoidal, RoPE) and KV-Caching during inference
- Compare Full Fine-Tuning against LoRA (Low-Rank Adaptation) and QLoRA
Skills Acquired
Prerequisites
- Basic linear algebra (matrix multiplication, vectors)
- High-school calculus (derivatives, chain rule)
- Basic Python programming knowledge
Curriculum Syllabus
4 modules · 4 lessons · 8 min · one assessment
Module 1: Neural Networks & Backpropagation
Perceptrons, activation functions (ReLU, GeLU), loss functions, and gradient descent.
Module 2: The Transformer & Self-Attention Explained
Dissect Query, Key, Value matrices, Scaled Dot-Product, and Multi-Head Attention.
Module 3: Positional Embeddings, RoPE & Inference Optimizations
How Transformers know word order, Rotary Position Embeddings (RoPE), and KV-Caching.
Module 4: Parameter-Efficient Fine-Tuning (PEFT & LoRA)
Fine-tune 70B parameter models on consumer hardware using Low-Rank Adaptation (LoRA).
Md. Rajib Hawlader
Principal AI Research Scientist, NeuroLearn AI Labs
Writes and maintains the NeuroLearn AI curriculum.
Ready to Begin Machine Learning & Deep Learning Foundations?
Self-paced curriculum with free instant certificate issuance upon passing the evaluation.
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