Unlock the next level in your AI journey with the Artificial Neural Network (ANNs) – Intermediate course.
Artificial Neural Network (ANNs) Intermediate
Unlock the next level in your AI journey with the Artificial Neural Network (ANNs) – Intermediate course. Designed to provide an in-depth understanding of advanced neural network structures and optimization techniques, this course builds on foundational ANN concepts and dives deep into real-world applications. You will explore the inner workings of Feedforward Neural Networks, delve into advanced activation functions, and master cutting-edge models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and LSTMs. By the end of this course, you’ll grasp the true meaning of neural networks and learn how to harness the power of generative models like Auto encoders and GANs.
Whether you're aiming to enhance your skills in AI development or looking to integrate deep learning into projects, this course delivers hands-on learning through code-based tutorials and case-driven exercises. Understanding the Artificial Neural Network (ANN) landscape at this level prepares you to build, train, and fine-tune highly efficient and specialized AI models.
This course is ideal for learners with a basic understanding of neural networks who are ready to take their AI expertise to the next level. It's perfect for data scientists, software developers, AI researchers, and machine learning engineers aiming to build advanced models for complex tasks such as image recognition, time-series prediction, and generative AI. If you’re familiar with the basics of Python and neural networks, and are looking to refine your skills in real-world ANN applications, this intermediate-level course is designed for you.
Define and understand the deeper meaning of neural network architectures.
Build and train Artificial Neural Networks (ANN) with advanced structures.
Implement Feedforward Neural Networks and apply Backpropagation.
Apply Convolutional Neural Networks (CNNs) to image-related tasks.
Leverage Recurrent Neural Networks (RNNs) and LSTMs for sequential data.
Explore and utilize advanced activation functions.
Understand and apply regularization techniques to prevent overfitting.
Create generative models like Auto encoders and GANs.
Perform neural network optimization and hyper parameter tuning effectively.
-
Understand the core concepts and biological inspiration behind ANNs, and explore the architecture and types of neural networks.
-
Learn to build feedforward networks, implement backpropagation algorithms, and optimize learning with gradient descent.
-
Dive into advanced activation functions like Leaky ReLU, ELU, and Swish to improve model performance and convergence.
-
Master the principles of CNNs for image recognition, including convolution layers, pooling, and architecture design.
-
Explore sequential data modelling with RNNs, understand vanishing gradient problems, and implement LSTM networks for better temporal learning.
-
Apply techniques like L1/L2 regularization, Dropout, and Early Stopping to enhance model generalization and reduce overfitting.
-
Discover how to build Auto encoders for dimensionality reduction and explore GANs for data generation and creativity in AI.
-
Learn methods for selecting optimal parameters, performing grid/random search, and using tools like learning rate schedules and batch normalization.
Earn a Professional Certificate
Earn a certificate of completion issued by Learn Artificial Intelligence (LAI), recognised for demonstrating personal and professional development.
Saim Ali
Network Operator.It effectively bridged basic neural network concepts with practical, hands-on model building.
Yes, having foundational knowledge of neural networks, including basic feedforward models and activation functions, is recommended for this intermediate course.
The course uses Python, with hands-on exercises and implementation using popular deep learning libraries like TensorFlow and PyTorch.
You’ll do both—learn to create neural networks from scratch and explore best practices using pre-built architectures for real-world applications.
An artificial neural network (ANN) is a machine learning model inspired by the human brain, designed to recognize patterns and solve complex tasks such as classification, regression, and prediction.
The ANN approach involves using layers of interconnected nodes (neurons) to process data, learn from inputs, and make predictions or decisions without explicit programming.
The main function of ANNs is to enable machines to learn from data, identify patterns, and make intelligent decisions—crucial for tasks like image recognition, language processing, and autonomous control.
Key Aspects of Course
Employer approved
Boost your career prospects for free
