Overview

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course will introduce you to the cutting-edge techniques and architectures in deep learning and AI. You will start by mastering the fundamentals of neural networks and deep learning, including key concepts like forward propagation, backpropagation, and gradient descent. From there, you will advance to Convolutional Neural Networks (CNNs) for image classification tasks and Recurrent Neural Networks (RNNs) for sequence modeling tasks such as time series prediction and text generation. As you progress, you will explore the revolutionary Transformer architecture, its self-attention mechanism, and its application in Natural Language Processing (NLP) tasks like text summarization and translation. This course will also cover transfer learning, allowing you to fine-tune pre-trained models for your own tasks, saving time and improving model accuracy. With hands-on projects using frameworks like TensorFlow, Keras, and PyTorch, you will apply your skills to real-world challenges. The course is designed for intermediate learners with prior knowledge of machine learning or neural networks. If you're a machine learning enthusiast or aspiring AI engineer looking to deepen your understanding of deep learning models and their real-world applications, this course will take your skills to the next level. By the end of the course, you will be able to design and implement advanced deep learning models, including CNNs, RNNs, and Transformers, and use transfer learning techniques to fine-tune models for specific tasks such as image classification, text generation, and more.

Syllabus

Week 9: Neural Networks and Deep Learning Fundamentals

In this module, we will introduce you to neural networks and deep learning, exploring their foundational concepts and optimization techniques. You’ll learn how to build models with popular frameworks like TensorFlow, Keras, and PyTorch, and apply your skills to a hands-on image classification project using the CIFAR-10 dataset.

Week 10: Convolutional Neural Networks (CNNs)

In this module, we will dive deep into CNNs, focusing on their unique architecture designed for image classification tasks. You will learn how to implement CNNs using Keras, TensorFlow, and PyTorch, while enhancing model performance through regularization and data augmentation techniques. Finally, apply your knowledge in an image classification project.

Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling

In this module, we will explore RNNs and their ability to process sequential data. You will learn about advanced RNN architectures like LSTMs and GRUs, apply text preprocessing and word embeddings, and build sequence-to-sequence models. The module culminates with a project focused on either text generation or sentiment analysis.

Week 12: Transformers and Attention Mechanisms

In this module, we will explore the cutting-edge Transformer architecture and attention mechanisms that have revolutionized NLP. You will gain hands-on experience working with pre-trained models like BERT and GPT, learning how to fine-tune them for real-world tasks like text summarization and translation.

Week 13: Transfer Learning and Fine-Tuning

In this module, we will dive into transfer learning, teaching you how to leverage pre-trained models for faster and more efficient model development. You will explore fine-tuning techniques for both computer vision and NLP tasks, addressing domain adaptation challenges, and apply your skills to a project focused on fine-tuning a model for a custom task.