Duration 3 days – 21 hrs
Overview
This course provides a hands-on introduction to building and training neural networks using modern deep learning frameworks such as TensorFlow or PyTorch. Participants will explore the fundamentals of neural network architectures, including perceptrons, activation functions, and backpropagation, and will gain practical experience by creating simple deep learning models for classification and prediction tasks.
Objectives
- Understand the core components of neural networks including neurons, weights, and activation functions
- Implement forward and backward propagation for simple neural networks
- Build, train, and evaluate basic deep learning models using TensorFlow or PyTorch
- Apply neural networks to real-world problems like image or text classification
- Interpret model performance and perform basic debugging of neural network training
Audience
- Aspiring AI engineers, data scientists, and machine learning developers
- Software engineers and analysts ready to move into deep learning roles
- Students or professionals who have completed an introductory machine learning course
- Anyone interested in practical neural network implementation using Python frameworks
Prerequisites
- Proficiency in Python programming
- Working knowledge of machine learning concepts (regression, classification, model training)
- Familiarity with NumPy and scikit-learn
- Completion of an introductory machine learning or Python for AI course recommended
Course Content
Day 1: Fundamentals of Neural Networks
- Biological inspiration and the perceptron model
- Neurons, weights, bias, and activation functions
- Loss functions and gradient descent
- Backpropagation and weight updates
- Hands-on: Build a basic neural network from scratch using NumPy
Day 2: Deep Learning Frameworks – TensorFlow or PyTorch
- Overview of TensorFlow and PyTorch: ecosystem and syntax
- Building a simple feedforward neural network
- Data loading, preprocessing, and batching
- Training and evaluating a deep learning model
- Hands-on: Build and train a classification model on a toy dataset
Day 3: Deep Learning in Practice
- Model tuning and overfitting prevention (dropout, regularization)
- Visualizing training progress (loss curves, accuracy)
- Applying models to real-world data (e.g., image or text)
- Final mini-project: Build and train a deep learning model using chosen framework (TensorFlow or PyTorch)
