Keras Deep Learning and Generative Adversarial Networks (GAN)

The course begins with the fundamentals of Python, encompassing concepts such as assignment, flow control, lists, tuples, dictionaries, and functions. We then move on to the Python NumPy library, which supports large arrays and matrices.

Before embarking on the journey of deep learning, a comprehensive theoretical session awaits, expounding upon the essential structure of an artificial neuron and its amalgamation to form an artificial neural network. The exploration then delves into the realm of CNNs, text-based models, binary and multi-class classification, and the intricate world of image processing. The transformation continues with an in-depth exploration of the GAN paradigm, spanning from fundamental principles to advanced strategies. Attendees will have the opportunity to construct models, harness transfer learning techniques, and venture into the realm of conditional GANs.

Once we complete the fully connected GAN, we will then proceed with a more advanced Deep Convoluted GAN, or DCGAN. We will discuss what a DCGAN is and see the difference between a DCGAN and a fully connected GAN. Then we will try to implement the DCGAN. We will define the Generator function and define the Discriminator function.

By the end of the course, you will wield the skills to create, fine-tune, and deploy cutting-edge AI solutions, setting you apart in this evolving landscape.

Type
video
Category
Data
publication date
2023-09-26
what you will learn

Learn about Artificial Intelligence (AI) and machine learning
Understand deep learning and neural networks
Learn about lists, tuples, dictionaries, and functions in Python
Learn Pandas, NumPy, and Matplotlib basics
Explore the basic structure of artificial neurons and neural network
Understand Stride, Padding, and Flattening concepts of CNNs

duration
1036
key features
Understand Generative Adversarial Networks (GAN) using Python with Keras * Learn deep learning from scratch to expert level * Python and deep learning using real-world examples
approach
This course takes a structured approach, guiding learners step by step. We begin with foundational concepts and gradually progress to advanced topics. Hands-on exercises and real-world applications provide practical experience. Whether you are new to the field or seeking to deepen your expertise, this course offers a well-rounded learning journey.
audience
This course is designed for newcomers aiming to excel in deep learning and Generative Adversarial Networks (GANs) starting from the basics. Progress from novice to advanced through immersive learning. Suitable for roles like machine learning engineer, deep learning specialist, AI researcher, data scientist, and GAN developer.
meta description
Understand about deep learning and Generative Adversarial Networks using Python and Keras with this comprehensive course
short description
Welcome to this dual-phase course. In the first segment, we delve into neural networks and deep learning. In the second, ascend to mastering Generative Adversarial Networks (GANs). No programming experience required. Begin with the fundamentals and progress to an advanced level.
subtitle
Learn deep learning and Generative Adversarial Networks (GAN) using Python with Keras
keywords
AI, Machine Learning, Deep Learning, GANs, Neural Networks, Data Science, Artificial Intelligence, Generative Models, AI Training, GAN Techniques, AI Development, ML Algorithms, GAN Applications, AI Learning, Deep Learning Fundamentals, GAN Implementation, AI Career, Data Analysis
Product ISBN
9781805125495