Dive into Deep Learning

Course Overview: Dive into Deep Learning

Introduction

Embark on a hands‑on journey through the core principles and practices of modern deep learning. From understanding the mechanics of individual neurons to mastering state‑of‑the‑art architectures, this course bridges theory and real‑world application.

What You’ll Learn

• Foundations of Neural Networks
  – Neuron models, activations, and network topologies

• Training and Optimization
  – Backpropagation, loss functions, and optimizers (SGD, Adam, etc.)

• Advanced Architectures
  – Convolutional Neural Networks (CNNs) for computer vision
  – Recurrent Neural Networks (RNNs) and LSTMs for sequential data
  – Transformer models for language understanding

• Practical Implementation
  – Building, training, and evaluating models in MXNet/Gluon, PyTorch, or TensorFlow
  – Techniques for regularization, hyperparameter tuning, and model debugging

• Deployment and Scaling
  – Best practices for exporting models, inference optimization, and integration into product pipelines

Course Format

1. Lectures & Readings – Concise theory sessions paired with curated chapters from “Dive into Deep Learning.”

2. Labs & Projects – Guided notebooks and capstone projects to reinforce each module.

3. Quizzes & Code Reviews – Regular checkpoints to test understanding and improve code quality.

Who Should Attend

• Software engineers, data scientists, and researchers eager to deepen their mastery of AI

• Students with basic programming experience and a fundamental understanding of calculus and linear algebra

Prerequisites

• Proficiency in Python programming

• Familiarity with basic machine learning concepts (e.g., linear regression, classification)

• Comfortable with Git and Jupyter notebooks

By the end of this course, you’ll be equipped to design, implement, and fine‑tune deep learning models—ready to tackle challenges in research or deploy AI‑powered solutions in production.