Data Science and Machine Learning (Theory and Projects) A to Z - Introduction: 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. The course covers both fundamental and high-level applications, including recent models like Mobilenet and frameworks such as Tensorflow. Students will engage in practical projects, including transfer learning and state-of-the-art object detection with YOLO, to solidify their understanding.

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

• Advanced deep learning techniques

• Fundamentals of classical computer vision

• Implementation of TensorFlow models

• Transfer learning applications

How does the course simplify complex mathematical concepts?

• By translating them into Python code

• Using graphical illustrations

• Through detailed theoretical explanations

• By using high-level frameworks

Which library is primarily used for implementing backpropagation in the course?

• Numpy

• PyTorch

• TensorFlow

• Keras

What recent model is mentioned as being used in the course for practical applications?

• AlexNet

• MobileNet

• VGGNet

• ResNet

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

• To reduce computational power and data requirements

• To increase the complexity of the model

• To avoid using Python

• To focus solely on classical techniques

Which advanced object detection algorithm is covered in the course?

• Faster R-CNN

• YOLO

• SSD

• R-FCN

What is one of the exciting projects mentioned in the course?

• Data augmentation techniques

• Neural style transfer

• Feature extraction with PCA

• Image classification using SVM