Deep Learning with Python
Artificial Intelligence (AI)
COURSE OVERVIEW
This intensive six-day training program is designed to provide participants with a strong theoretical foundation and practical expertise in Deep Learning using Python. The course covers the complete deep learning workflow—from neural network fundamentals and model building to advanced architectures such as CNNs, RNNs, and generative models. Participants will gain hands-on experience in building, training, and optimizing deep learning models for real-world applications including image recognition, sequence modeling, and generative AI systems.
By the end of the course, learners will be capable of designing and deploying deep learning solutions using modern Python frameworks.
COURSE OBJECTIVES
By the end of this course, participants will be able to:
- Understand the fundamentals of neural networks and deep learning concepts.
- Build and train neural networks using Python-based frameworks.
- Apply activation functions, forward propagation, and backpropagation techniques.
- Develop Convolutional Neural Networks (CNNs) for image classification tasks.
- Implement Recurrent Neural Networks (RNNs) and LSTM models for sequential data.
- Work with advanced architectures such as GANs and Autoencoders.
- Optimize deep learning models for performance and accuracy.
- Deploy deep learning models for real-world applications.
Duration: 6 Days / 48 Hours
Delivery Method: Classroom-based, Virtual Instructor Led Training
COURSE OUTLINE
Day 1: Introduction to Deep Learning
Focus: Understanding the foundations of neural networks
- Introduction to Artificial Intelligence, Machine Learning, and Deep Learning
- Biological inspiration behind neural networks
- Structure of a neural network (input, hidden, output layers)
- Introduction to neurons, weights, and biases
- Overview of deep learning frameworks (TensorFlow, Keras, PyTorch)
- Deep learning use cases in real-world applications
- Hands-on: Setting up deep learning environment in Python
Day 2: Building Neural Networks
Focus: Understanding how neural networks learn
- Forward propagation process
- Backpropagation and gradient descent optimization
- Activation functions (Sigmoid, ReLU, Tanh, Softmax)
- Loss functions and cost optimization
- Training and evaluating neural networks
- Overfitting and underfitting concepts
- Hands-on: Building and training a simple neural network
Day 3: Convolutional Neural Networks (CNNs)
Focus: Deep learning for image data
- Introduction to CNN architecture
- Convolution, pooling, and feature extraction concepts
- Understanding filters and feature maps
- Image classification workflow
- Data augmentation techniques
- Transfer learning and pre-trained models (e.g., VGG, ResNet)
- Hands-on: Building an image classification model using CNN
Day 4: Recurrent Neural Networks (RNNs)
Focus: Deep learning for sequential data
- Introduction to sequence modeling problems
- RNN architecture and working principle
- Limitations of basic RNNs (vanishing gradient problem)
- LSTM (Long Short-Term Memory) networks
- GRU (Gated Recurrent Units) overview
- Time series forecasting basics
- Hands-on: Building an RNN/LSTM model for sequence prediction
Day 5: Advanced Deep Learning Techniques
Focus: Exploring modern and generative models
- Introduction to Generative Adversarial Networks (GANs)
- Generator vs Discriminator architecture
- Autoencoders and their applications
- Variational Autoencoders (VAE) overview
- Model optimization techniques
- Regularization methods (Dropout, Batch Normalization)
- Hands-on: Simple autoencoder or GAN demonstration
Day 6: Final Project and Deployment
Focus: End-to-end implementation and real-world application
- Designing a complete deep learning project pipeline
- Data preparation and model selection strategies
- Model training, tuning, and evaluation
- Introduction to model deployment concepts (APIs, cloud deployment basics)
- Performance monitoring and improvement strategies
- Final capstone project presentation
- Assessment and feedback session
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