Advanced AI Diploma

Overview

Learning Outcomes

• 1) Fundamentals of Deep Learning You will learn a comprehensive introduction to Deep Learning and build a strong foundation for subsequent modules. This includes core concepts of neural networks such as perceptrons, activation functions, and multilayer perceptrons. You will learn how to train neural networks using forward and backward propagation, gradient descent optimization, and loss functions. Practical sessions will involve implementing simple neural networks using Python and NumPy, as well as exploring Deep Learning frameworks such as TensorFlow and Keras to build more complex models.
• 2) Computer Vision Techniques You will explore Computer Vision, with a focus on image processing, the basics of Computer Vision, and advanced techniques. This module covers image types, color spaces, image digitization, and image enhancement techniques. The core of this module is Convolutional Neural Networks (CNNs), where you will learn about their architecture, different CNN models (LeNet, AlexNet, Inception, VGG), and image augmentation. The module also covers object detection, image segmentation, and Optical Character Recognition (OCR), providing practical applications and hands-on experience.
• 3) Natural Language Processing (NLP) You will learn about Natural Language Processing, covering text preprocessing, representation, and advanced NLP techniques. This includes segmentation, chunking, word analysis, stop word removal, and text normalization. You will discover text representation techniques like bag of words, N-grams, and TF-IDF. The module delves into Recurrent Neural Networks (RNNs), including GRUs and LSTMs, to process sequential data. Advanced topics such as attention mechanisms, transformers, and language models (BERT, Llama) will be covered, alongside practical applications like sentiment analysis, machine translation, and chatbot development.