Deep Learning Foundations
Fusemachines AI Academy
Deep Learning Foundations
Course Overview
A ten-week ramp from “I know some Python” to “I can train, debug, and reason about neural networks.” This course is deliberately paced for engineers and analysts who are new to deep learning but serious about building a durable foundation rather than memorizing framework recipes.
Concepts come before tools. Students understand why a network learns before they reach for a high-level API.
What You’ll Learn
- The mechanics of backpropagation and gradient-based optimization
- Building and training feedforward, convolutional, and recurrent networks
- Practical skills: regularization, normalization, initialization, and debugging training
- Computer vision and sequence modeling fundamentals
- How to read a paper and reproduce a result
Module Breakdown
Weeks 1–3: Foundations
Linear models, loss functions, gradient descent, and backpropagation. Students implement a small network from scratch in NumPy.
Weeks 4–6: Convolutional Networks
CNN architectures, transfer learning, and a complete image classification project.
Weeks 7–8: Sequence Models
RNNs, LSTMs, and an introduction to attention. Applied to a text or time-series task.
Weeks 9–10: Capstone
A self-selected project applying deep learning end-to-end, with emphasis on rigorous evaluation.
Who This Course Is For
Engineers, analysts, and technically-minded professionals with basic Python who want a genuine, first-principles foundation in deep learning. No prior ML experience required.
Assessment
Weekly coding exercises, two milestone projects (vision and sequences), and a final capstone with a short written analysis.