Neural Networks and Deep Learning Training is a practical, hands-on program designed to help participants understand how neural networks learn from data and how deep learning techniques can be applied to develop intelligent AI systems. The course combines fundamental concepts with practical implementation to provide learners with the skills needed to build, train, evaluate, and improve deep learning models.
Duration 3 days – 21 hrs
Overview
The Neural Networks and Deep Learning Training is a practical, hands-on program designed to provide participants with a strong foundation in neural networks, deep learning, and AI model development. The course combines essential theory with guided implementation, enabling learners to understand how neural networks process data, learn patterns, and make predictions.
Participants will explore the fundamental building blocks of neural networks, including neurons, perceptrons, weights, biases, activation functions, loss functions, gradient descent, and backpropagation. These concepts provide the foundation for understanding how neural networks learn and how their parameters are adjusted during training.
The course progresses from fundamental concepts to practical model development using Python and modern deep learning frameworks such as TensorFlow or PyTorch. Participants will gain hands-on experience preparing data, designing neural network architectures, training models, evaluating results, and identifying common issues that can affect model performance.
Through practical exercises, learners will build neural networks from scratch using NumPy before applying established deep learning frameworks to classification and prediction tasks. This progression helps participants understand what happens behind the framework while also developing the practical skills needed to create functional deep learning models.
The training also introduces important techniques for improving model performance, including regularization, dropout, model tuning, training visualization, and overfitting prevention. Participants will learn how to interpret training results, identify performance issues, and make appropriate adjustments to improve their models.
To connect learning with real-world applications, participants will apply neural networks to practical datasets and explore use cases such as image classification, text classification, prediction, and pattern recognition. A final mini-project allows learners to bring together the concepts and techniques covered throughout the course by developing and evaluating their own deep learning model.
By the end of the Neural Networks and Deep Learning Training, participants will have the practical knowledge and confidence to build, train, evaluate, and improve basic neural network and deep learning models using Python, TensorFlow, or PyTorch, providing a solid foundation for further development in AI and machine learning.
Learning 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)
