Course Overview
This intensive training course offers a deep dive into the foundations and practical applications of neural networks. This course equips participants with the knowledge to design, implement, and optimize neural models using Theano, a powerful Python-based library for numerical computation. Through hands-on exercises, participants will explore key topics such as multi-layer perceptrons, backpropagation, activation functions, and model optimization techniques. The course also covers real-world use cases, helping data scientists apply neural networks to solve complex challenges in areas like computer vision, natural language processing, and predictive analytics. Whether you're new to neural networks or looking to enhance your skills, this course provides the tools and insights needed to build efficient, scalable models.
Duration
5 days
Target Audience
- Data scientists and ML practitioners
- AI enthusiasts exploring neural networks
- Developers transitioning into machine learning
- Researchers working on predictive models
- Professionals applying AI in finance, healthcare, etc.
Organizational Impact
- Improved decision-making through advanced predictive models
- Enhanced product development with AI-driven insights
- Increased efficiency by automating complex tasks
- Competitive advantage through innovative neural network applications
- Strengthened data science teams with cutting-edge skills
Personal Impact
- Mastery of neural networks using Theano
- Enhanced problem-solving and model-building skills
- Broader career opportunities in AI and data science
- Ability to tackle real-world challenges with advanced ML techniques
- Confidence in applying deep learning to various domains
Course Level:
Course Objectives
- Understand the fundamentals of neural networks and their architectures
- Implement neural networks using Theano for efficient computation
- Apply backpropagation and optimization techniques to train models
- Explore real-world applications such as NLP and computer vision
- Develop and fine-tune deep learning models for specific tasks
- Evaluate model performance and improve accuracy with advanced methods
Course Outline
Module 1: Introduction to Theano
- What is Theano?
- Theano code – Basics
- Graph Visualization
- Variables, constants, functions, and Placeholders
- Theano Basic operations in Neural Networks
- Case Study: Building a simple neural network using Theano to predict house prices.
Module 2: Introduction to Neural Networks
- Understanding Neural networks
- Supervised and unsupervised learning with Neural Networks
- Logistic and linear regression with Theano
- Understanding vectorization in Neural Networks
- Working with Activation functions
- Model Training and Validation
- Forward and Backward propagation
- Understanding the perceptron concept
- Case Study: Using a neural network to classify handwritten digits from the MNIST dataset.
Module 3: Improving Neural Networks
- Understand the optimization algorithm
- Regularizing your Neural Network
- Learn the tuning process
- Work with Hyperparameter Tuning
- Understand Normalization in Neural Networks
- Fitting and testing the Batch Norm into a Neural Network
- Understand the Softmax Regression
- Case Study: Fine-tuning a neural network to improve accuracy for customer churn prediction.
Module 4: Convolutional Neural Network for Data Visualization and Analysis
- What is CNN?
- Understanding pooling layers
- Edge Detection
- Building CNN components
- Build a full CNN and test on SVHN
- Analyze a model’s training performance in Theano
- Identify cases of overfitting and apply techniques to prevent it
- Visualizing learned filters
- Working with a XOR problem
- Perceptron Networking
- Case Study: Applying a CNN to classify traffic signs for autonomous driving systems.
Module 5: Feed Forward Network for Data Visualization and Analysis
- Introduction to Feed Forward Network
- Combination of units to layers and networks
- Understanding Non-linear Neural Networks
- Understanding various activation functions used
- Working with a XOR problem
- Gradient-based learning
- Input-output mapping
- The Applications of the Feed Forward Network
- Case Study: Predicting stock prices using a feedforward neural network.
Related Courses
Course Administration Details:
Methodology
These instructor-led training sessions are delivered using a blended learning approach and include presentations, guided practical exercises, web-based tutorials, and group work. Our facilitators are seasoned industry experts with years of experience as professionals and trainers in these fields. All facilitation and course materials are offered in English. Participants should be reasonably proficient in the language.
Accreditation
Upon successful completion of this training, participants will be issued an Indepth Research Institute (IRES) certificate certified by the National Industrial Training Authority (NITA).
Training Venue
The training will be held at IRES Training Centre. The course fee covers the course tuition, training materials, two break refreshments, and lunch. All participants will additionally cater to their travel expenses, visa application, insurance, and other personal expenses.
Accommodation and Airport Transfer
Accommodation and Airport Transfer are arranged upon request. For reservations contact the Training Officer.
- Email: [email protected]
- Phone: +254715 077 817
Tailor-Made
This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our IRES Training Centre or at a convenient location. For further inquiries, please contact us on:
- Email: [email protected]
- Phone: +254715 077 817
Payment
Payment should be transferred to the IRES account through a bank on or before the start of the course. Send proof of payment to [email protected]
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