Deep Learning and Reinforcement Learning

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  • Introduction to Neural Networks
    • This module introduces Deep Learning, Neural Networks, and their applications. You will go through the theoretical background and characteristics that they share with other machine learning algorithms, as well as characteristics that makes them stand out as great modeling techniques for specific scenarios. You will  also gain some hands-on practice on Neural Networks and key concepts that help these algorithms converge to robust solutions.
  • Neural Network Optimizers and Keras
    • You can leverage several options to prioritize the training time or the accuracy of your neural network and deep learning models. In this module you learn about key concepts that intervene during model training, including optimizers and data shuffling. You will also gain hands-on practice using Keras, one of the go-to libraries for deep learning. 
  • Convolutional Neural Networks
    • In this module you become familiar with convolutional neural networks, also known as space invariant artificial neural networks, a type of deep neural networks, frequently used in image AI applications. There are several CNN architectures, you will learn some of the most common ones to add to your toolkit of Deep Learning Techniques.
  • Recurrent Neural Networks and Long-Short Term Memory Networks
    • In this module you become familiar with Recursive Neural Networks (RNNs) and Long-Short Term Memory Networks (LSTM), a type of RNN considered the breakthrough for speech to text recongintion. RNNs are frequently used in most AI applications today, and can also be used for supervised learning. 
  • Deep Learning with Autoencoders
    • In this module you become familiar with Autoencoders, an useful application of Deep Learning for Unsupervised Learning. Autoencoders are a neural network architecture that forces the learning of a lower dimensional representation of data, commonly images. In this module you will learn some Deep learning-based techniques for data representation, how autoencoders work, and to describe the use of trained autoencoders for image applications
  • Deep Learning Applications and Reinforcement Learning
    • In this module you become familiar with other novel applications of Neural Networks. You will learn about Generative Adversarial Networks, frequently referred to as GANs, which are an application of Neural Networks to generate new data. Finally, you learn about Reinforcement Learning, one of the big promises for A.I., based on training algorithms by using rewards, instead of using a method to minimize error, which is what we have been using throughout the course.