PESU I/O Fundamentals of Deep Learning
Course Description
This crash course is designed to help CS students understand the basics of deep learning and its applications. The aim of the course is to help students understand the basics of deep learning, its applications, and how to implement deep learning models using popular libraries like TensorFlow and Keras. The course will also cover the basics of neural networks, convolutional neural networks, and how to use them for various applications like image classification and object detection
Pre-requisites
- Basic understanding of Python programming and Python libraries like Pandas
- Basic understanding of Data Structures and Algorithms
- Basic understanding of Machine Learning (Linear Regression, Logistic Regression)
Course Content
Week 1: Introduction to Deep Learning
- Course Introduction Video Slides
- Data preprocessing and cleaning Numpy
- Introduction to Neural Networks Neural Networks Video Slides
- Google Colab Tutorial
Week 2: Convolutional Neural Networks
- Convolution Neural Networks CNN Video Slides
- Image Processing and Convolutional Layer Convolutions Video Slides
Week 3: Keras
- Introduction to Keras and Image Data Generator Keras Video Slides
- Building models using Keras and Sequential Models Building Models Video Slides
Week 4: Tensorflow
- Introduction to Tensorflow Video Slides Slides
- Tensorflow Architecture and Building Models Video Slides