This course build on the subject of Convolutional Neural Networks and dives into the complex pre-trained state- of-the-art CNN architectures. It also gives students a preview of what transfer learning is and why it is such a powerful concept in Deep Learning. By the end of this course students will be able to have implemented and explored pre-trained models such as ResNet, VGG16, and Inception3.

This course continues on tackling topics in deep learning for text analysis. In this course students will be getting to know how to use and implement Gated Recurrent Units (GRUs) and model and predict longer sequences of text by leveraging Seq2Seq models.

This course takes students through a journey into the world od autoencoders - a set of powerful deep learning models that have a special place in the world of image analysis. By the end of this course students will be able to navigate through the application space of autoencoders and implement autoencoders to perform tasks such as image denoising and more.

This course starts of a series of topics on neural networks designed to solve a particular family of tasks. In this course students will be able to get an overview of how to work with image data and build Convolutional Neural Networks (CNNs) - the industry standard for tackling image-based data.

This course continues on tackling topics in deep learning that address specific problem types. In this course students will be getting to know RNNs and LSTMs - types of neural networks that are often used for solving problems in text analysis.

This course covers the area in image analysis and computer vision that deals with generative models. By the end of this course students will be able to implement a GAN model to generate new images from a set of training examples.

This course gives students the first preview of the world of Neural Networks and how they work. These state-of- the-art methods build powerful predictive systems and find latent patterns in large amounts of data. By the end of this course, students will learn the foundations of this complex topic and acquire practical skills to build neural networks in order to solve real-world problems.

This course builds on the foundations of neural networks and takes through a series of practical examples of how to measure the performance of a neural network algorithm, tune it and accelerate it.

This course introduces students to a special case of image analysis that addresses the problem of object detection in images. By the end of this course students will be able to create a YOLO - a deep learning model used specifically for such tasks.

This course covers the specialized branch of machine learning and deep learning called reinforcement learning (RL). By the end of this course students will be able to define RL use cases and real world scenarios where RL models are used, they will be able to create a simple RL model and evaluate its performance.