Enroll Course: https://www.udemy.com/course/keras-deep-learning-generative-adversarial-networks-gan/
Are you ready to dive into the exciting world of Artificial Intelligence, specifically Deep Learning and Generative Adversarial Networks (GANs)? Look no further than Udemy’s ‘Keras Deep Learning & Generative Adversarial Networks (GAN)’ course. This comprehensive program is meticulously designed to take you from the absolute basics to advanced concepts, making it perfect for both beginners and those looking to solidify their understanding.
The course is cleverly split into two main sections. The first half is dedicated to the fundamentals of Deep Learning and Neural Networks. It begins by laying a strong foundation, even covering essential Python programming basics and supporting libraries like NumPy, Matplotlib, and Pandas. You’ll learn how to set up your development environment with Anaconda and utilize Jupyter Notebooks for hands-on coding. The theoretical aspects are thoroughly explained, covering artificial neurons, activation functions, loss functions, optimizers, and different types of neural network architectures.
Practical application is key, and this course excels in delivering it. You’ll build and train multi-layer neural networks for various text-based tasks, including regression (predicting house prices), binary classification (heart disease prediction), and multi-class classification (red wine quality prediction). Each project follows a clear workflow: data fetching, exploratory data analysis (EDA), data preparation, model definition, compilation, training, evaluation, and prediction.
The second half of the course shifts focus to the fascinating realm of Generative Adversarial Networks (GANs). You’ll start with an introduction to GANs, understanding the roles of the Generator and Discriminator. The course progresses through implementing a simple fully connected GAN using the MNIST dataset, followed by a more advanced Deep Convolutional GAN (DCGAN) that can generate images from grayscale datasets like MNIST and MNIST Fashion. For color image generation, you’ll tackle the CIFAR-10 dataset, leveraging Google Colab’s free GPU for efficient training.
Furthermore, the course delves into crucial optimization techniques like dropout regularization, parameter tuning, and image augmentation. It also introduces the powerful concept of transfer learning, utilizing pre-trained models like VGG16, VGG19, and ResNet50 for more efficient and effective image classification tasks. The practical implementation of these advanced models, including training on Google Colab, is explained step-by-step.
Finally, the course explores Conditional GANs, comparing them to vanilla GANs and implementing them for both MNIST and Fashion MNIST datasets. The instructor also provides access to a Git repository with all course materials, including code, images, models, and weights, encouraging you to experiment and build your own projects. Upon completion, you’ll receive a certificate to enhance your professional portfolio.
This course is an excellent investment for anyone looking to gain practical skills in deep learning and GANs using Keras. The clear explanations, hands-on projects, and progression from basic to advanced topics make it a highly recommended resource.
Enroll Course: https://www.udemy.com/course/keras-deep-learning-generative-adversarial-networks-gan/