According to Indeed, machine learning engineer salaries currently start at USD 100,809 and top out at just over USD 254,000.
Gain advanced Keras and TensorFlow 2.x techniques you need to build and optimize machine learning models. In this course, practice techniques for deep learning, reinforcement learning, generative models, and sequential data handling that will prepare you to tackle complex real-world challenges.
You’ll begin by learning about Keras's advanced features, including its functional API used to design complex models. You’ll then learn how to create custom layers and models to tailor solutions to unique challenges and seamlessly integrate Keras with TensorFlow 2.x for enhanced functionality.
Next, you’ll use Keras to develop advanced convolutional neural networks (CNNs) that can solve complex computer vision tasks. You’ll apply data augmentation to improve model generalization, implement transfer learning with pre-trained models, and leverage TensorFlow for advanced image processing. You’ll also explore transpose convolution
Then, learn how to build and train advanced Transformers using Keras for sequential data tasks, including time series prediction. You’ll gain hands-on experience developing Transformer-based models for text generation and explore how to utilize TensorFlow to manage sequential data effectively.
Then you’ll dive into unsupervised learning with Keras. You’ll build and train autoencoders, experiment with cutting-edge diffusion models, and develop generative adversarial networks (GANs). You’ll also learn to integrate TensorFlow for advanced unsupervised learning tasks and expand your expertise in generative modeling techniques.
You’ll master advanced Keras techniques for model development by creating custom training loops and optimizing model performance. You’ll explore hyperparameter tuning using Keras Tuner and leverage TensorFlow for enhanced model optimization and custom training workflows.
In the final module, you’ll explore reinforcement learning and its applications in Keras. You’ll implement Q-Learning algorithms and develop deep Q-networks (DQNs) to tackle advanced reinforcement learning tasks, gaining practical experience with this powerful AI technique.
By the end of this course, you’ll have the knowledge and skills to build and optimize advanced models using Keras and TensorFlow 2.x, tackling challenges in computer vision, NLP, reinforcement learning, and generative modeling.
