Gain a robust understanding of deep learning through both theory and hands-on implementation, spanning domains such as computer vision, natural language processing (NLP) and graph data analysis. Explore neural network architectures, optimization techniques, and advanced models (CNNs, RNNs, GANs, GNNs).
Apply practical skills to build and train neural networks using TensorFlow and Keras
Implement and evaluate advanced deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Graphical Neural Networks
Optimize and fine-tune deep learning models to enhance performance
Deploy deep learning models in production environments
Apply deep learning techniques to address real-world problems in various domains such as computer vision, natural language processing (NLP), and graph data analysis
About this course:
Deep Learning is designed to provide students with a solid understanding of deep learning principles, techniques, and applications. The course is structured to cover both theoretical concepts and hands-on implementation, ensuring students are equipped with the necessary skills to tackle real-world challenges in various domains such as computer vision, natural language processing, and graph data analysis. Throughout the course, students will delve into topics such as neural network architectures, optimization techniques, and advanced deep learning models including CNNs, RNNs, GANs, and GNNs. Practical sessions using Python, TensorFlow, and Keras will enable students to build and train neural networks, gaining valuable experience in model development and evaluation. In addition to core topics, the course offers specialized tracks in natural language processing (NLP), allowing students to explore advanced NLP techniques and applications. Students can choose from a selection of NLP-focused projects spanning areas such as sentiment analysis, text generation, machine translation, and question-answering systems. By the end of the course, students will have developed a deep understanding of deep learning concepts and techniques along with the practical skills necessary to apply them to real-world problems. The capstone project provides an opportunity for students to showcase their expertise and creativity, reinforcing their learning and preparing them for future endeavors in the field of deep learning.
Prerequisites
Before embarking on the Deep Learning course, it is imperative to establish a robust foundational knowledge. We suggest the following preparatory courses: Linear Algebra, Introduction to Data Science to acquaint yourself with the basic principles of data science and Machine Learning Using Python.
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