The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.
In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.
AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.
Course 1: Neural Networks and Deep Learning
Course 2: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Course 3: Structuring Machine Learning Projects
Course 4: Convolutional Neural Networks
Week1 W1-Convolution_model_Application
- Create a mood classifer using the TF Keras Sequential API that determines whether the people in the images are smiling or not
- Build a ConvNet to identify sign language digits using the TF Keras Functional API
Only for educational use.