"Overview: This course offers a comprehensive understanding of ArtificialNeural Networks, Convolutional Neural Networks, and DeepLearning. It starts with a deep dive into the fundamental principlesof Artificial Neural Networks, including neuron operations andproblem-solving applications. Following this, the course exploresConvolutional Neural Networks, emphasizing their use incomputer vision, explaining convolutional and pooling layers, andtheir impact on image classification.Objectives: Understand the fundamental principles of Artificial NeuralNetworks (ANNs) and their applications in solving complexproblems• Apply ANNs to solve complex problems, utilizingappropriate frameworks and programming• languages, such as TensorFlow or PyTorch, to build and trainneural networks• Demonstrate the acquired knowledge and skills to developpractical solutions using ANNs and CNNsfor complexproblems in diverse domains• Implement and evaluate the performance of CNNs in imagerecognition, object detection, and other computer visionapplications• Analyze the architectural components of ConvolutionalNeural Networks (CNNs) and their role in computer visiontasks• Construct cutting-edge solutions by applying ArtificialNeural Networks, Convolutional Neural Networks to solvecomplex real-world problems across diverse domains."
Overview
Syllabus
Week 1: Introduction to ANN and Deep Learning
Week 2: Neural Networks, Hyperparameter and Model Building
Week 3: Optimizers, Model Regularization and Auto Encoding
Week 4: Deep Learning and Convolutional Neural Network
Week 5: CNN Architecture and Transfer Learning
Syllabus Page
Exam Schedule
Week 2: Neural Networks, Hyperparameter and Model Building
Week 3: Optimizers, Model Regularization and Auto Encoding
Week 4: Deep Learning and Convolutional Neural Network
Week 5: CNN Architecture and Transfer Learning
Syllabus Page
Exam Schedule
Taught by
Naveen Kumar Bhansali
