Deep Learning CNN Convolutional Neural Networks with Python - Focus of the Course

This course provides a comprehensive introduction to convolutional neural networks (CNNs), starting from classical computer vision techniques and progressing to advanced deep learning models. It emphasizes a Pythonic approach, using Python code to simplify complex concepts like backpropagation and convolution. The course covers recent models and frameworks, including Tensorflow and Mobilenet, and includes practical projects on transfer learning and object detection. Designed for beginners, it offers live coding sessions and activities to enhance understanding.

What is the primary focus of the initial modules in the course?

• Transfer learning methods

• State-of-the-art CNN architectures

• Classical computer vision techniques

• Advanced deep learning models

How does the course simplify complex theoretical concepts?

• Through detailed mathematical proofs

• By using high-level frameworks

• By implementing them in Python code

• Using pre-recorded lectures

Which framework is introduced for building CNNs in the course?

• TensorFlow

• PyTorch

• Keras

• Scikit-learn

What is one of the advanced topics covered in the course?

• Simple linear regression

• Transfer learning

• Classical object detection

• Basic image processing

What is the benefit of using pre-trained models in transfer learning?

• They are outdated and not recommended

• They require a large amount of data

• They are only suitable for GPU usage

• They provide comparable accuracy with less data

Which state-of-the-art object detector is discussed in the course?

• Faster R-CNN

• YOLO

• SSD

• R-FCN

What dataset is mentioned for practical activities in the course?

• MNIST

• Caltech 256

• CIFAR-10

• ImageNet