Keras Deep Learning & Generative Adversarial Networks (GAN)

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Coursera Specialization
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Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 10 hours a week
Sessions

Self-Paced
Level

Intermediate

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Overview

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Updated in May 2025. This course now 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 offers an in-depth exploration of AI and deep learning, starting with foundational concepts and progressing to neural networks and deep learning with Keras. You'll learn how neural networks process data, predict outcomes, and solve complex problems. In the second part of the course, you'll dive into Generative Adversarial Networks (GANs), learning how they generate realistic data by using two competing neural networks: the generator and discriminator. You'll build GAN models with the MNIST dataset, explore their inner workings, and fine-tune them for optimal performance. By the course's conclusion, you'll be adept at handling various AI and deep learning libraries, training models using large datasets, and deploying deep learning solutions. Whether you're working on image generation or data augmentation, this course will provide you with the expertise needed to excel in today’s AI-driven world. This course is ideal for intermediate learners with basic Python programming skills and some familiarity with AI or machine learning concepts. You should be comfortable with Python basics, including data structures like lists and dictionaries, and have some experience with data libraries such as NumPy.

Syllabus

Course 1: Fundamentals of AI, Machine Learning, and Python Programming
Course 2: Deep Learning with Keras and Practical Applications
Course 3: Advanced Generative Adversarial Networks (GANs)

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Updated in May 2025. This course now 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. Embark on an enlightening journey into the realm of Generative Adversarial Networks (GANs), where you will master the art of AI-driven image synthesis. This course begins with a solid foundation, introducing you to the basic concepts and components of GANs, such as the Generator and Discriminator. From there, you will delve into the intricacies of fully connected and deep convolutional GANs, understanding their architectures, and learning how to implement and optimize them effectively. The course progresses with hands-on tutorials using popular datasets like MNIST and CIFAR-10, where you will learn to load, preprocess, and train GAN models. Each step is meticulously explained, ensuring you gain practical knowledge and experience. By leveraging tools such as Google Colab, you will explore the capabilities of GPU acceleration, enhancing your model training efficiency and performance. As you advance, you will tackle more sophisticated topics, including Conditional GANs, label embedding, and model optimization techniques. The course culminates with practical projects where you apply your knowledge to generate and analyze realistic images, bridging the gap between theoretical concepts and real-world applications. This comprehensive approach ensures you emerge with the skills and confidence to harness the full potential of GANs in your projects. This course is designed for data scientists, machine learning engineers, and AI enthusiasts who have a basic understanding of neural networks and Python programming. Familiarity with deep learning frameworks like TensorFlow or Keras is recommended but not mandatory.
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Updated in May 2025. This course now 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. Embark on a comprehensive journey into deep learning with Keras through this meticulously crafted course. The course begins with an engaging introduction to creating a multiclass classification model for assessing red wine quality. You'll learn to fetch, load, and prepare data, followed by exploratory data analysis (EDA) and visualization to uncover insights and patterns. As you progress, you'll delve into defining, compiling, fitting, and optimizing your model, ultimately using it for accurate wine quality predictions. Building on this foundation, the course transitions into the fascinating world of digital image processing. You'll explore the basics of digital images, followed by practical sessions on image processing using Keras functions. Advanced techniques such as image augmentation, both single image and directory-based, are covered in detail. The course also introduces Convolutional Neural Networks (CNNs), guiding you through model building, training, and optimization, specifically for flower image classification. The journey doesn't stop there. You'll venture into transfer learning with pre-trained models like VGG16 and VGG19, leveraging their power for enhanced model performance. Practical sessions on utilizing Google Colab's GPU for transfer learning ensure you gain hands-on experience in modern deep learning workflows. By the end of this course, you'll have a robust understanding of applying Keras to real-world problems, from data preprocessing to model deployment. This course is ideal for data scientists, machine learning engineers, and technical professionals with a basic understanding of Python programming and machine learning concepts. No prior experience with Keras is required, though familiarity with neural networks and deep learning frameworks will be beneficial.
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Updated in May 2025. This course now 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. Embark on a transformative learning experience designed to equip you with a robust understanding of AI, machine learning, and Python programming. This course begins with a thorough introduction to artificial intelligence and machine learning, demystifying the core concepts and exploring how algorithms and data-driven techniques empower computers to learn and adapt. As you progress, you'll delve into the architecture of deep learning and neural networks, grasping how these advanced structures mimic human cognition to process complex data and make accurate predictions. Transitioning from theory to practical application, the course guides you through setting up your development environment with Anaconda, laying the groundwork for efficient coding and package management. You'll then immerse yourself in Python programming, mastering flow control mechanisms, data structures, and functions. The journey continues with an exploration of essential Python libraries such as NumPy, Matplotlib, and Pandas, providing you with the tools to handle data manipulation and visualization effectively. The latter part of the course focuses on advanced AI topics, including the installation and application of deep learning libraries like TensorFlow and PyTorch. You'll learn about the fundamental structures of artificial neurons and neural networks, and the crucial roles of activation functions, loss functions, and optimizers in training models. Through hands-on projects, such as building regression models for house price prediction and binary classification models for heart disease prediction, you'll apply your knowledge to real-world scenarios, reinforcing your learning and enhancing your practical skills. This course is designed for aspiring data scientists, machine learning enthusiasts, and Python programmers. It is ideal for beginners seeking a comprehensive introduction to AI and machine learning, as well as professionals looking to deepen their understanding of these technologies. Prerequisites include basic programming knowledge and a keen interest in artificial intelligence and data science.