Description
Are you short on time to start from scratch to use deep learning to solve complex problems involving topics like neural networks and reinforcement learning? Than this course is for you! This course is designed to help you to overcome various data science problems by using efficient deep learning models built in TensorFlow. You will begin with a quick introduction to TensorFlow essentials. Next, you start with deep neural networks for different problems and also explore the applications of Convolutional Neural Networks on two real datasets. We will than walk you through different approaches to RL. You'll move from a simple Q-learning to a more complex, deep RL architecture and implement your algorithms using Tensorflow's Python API. You'll be training your agents on two different games in a number of complex scenarios to make them more intelligent and perceptive. By the end of this course, you'll be able to implement RL-based solutions in your projects from scratch using Tensorflow and Python. Also you will be able to develop deep learning based solutions to any kind of problem you have, without any need to learn deep learning models from scratch, rather using tensorflow and it's enormous power. Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Hands-on Deep Learning with TensorFlow is designed to help you to overcome various data science problems by using efficient deep learning models built in TensorFlow. The course begins with a quick introduction to TensorFlow essentials. Next, we start with deep neural networks for different problems and then explore the applications of Convolutional Neural Networks on two real datasets. If you're facing time series problem then we will show you how to tackle it using RNN. We will also highlight how autoencoders can be used for efficient data representation. Lastly, we will take you through some of the important techniques to implement generative adversarial networks. All these modules are developed with step by step TensorFlow implementation with the help of real examples. By the end of the course you will be able to develop deep learning based solutions to any kind of problem you have, without any need to learn deep learning models from scratch, rather using tensorflow and it's enormous power. In the second course, Hands-on Reinforcement Learning with TensorFlow will walk through different approaches to RL. You'll move from a simple Q-learning to a more complex, deep RL architecture and implement your algorithms using Tensorflow's Python API. You'll be training your agents on two different games in a number of complex scenarios to make them more intelligent and perceptive. By the end of this course, you'll be able to implement RL-based solutions in your projects from scratch using Tensorflow and Python. About the AuthorsSalil Vishnu Kapur is a Data Science Researcher at the Institute for Big Data Analytics, Dalhousie University. He is extremely passionate about Machine Learning, Deep Learning, Data mining and Big Data Analytics. Currently working as a Researcher at Deep Vision and prior to that worked as a Senior Analyst at Capgemini for around 3 years with these technologies. Prior to that Salil was an intern at IIT Bombay through the FOSSEE Python TextBook Companion Project and presently with the Department of Fisheries and Transport Canada through Dalhousie University. Satwik Kansal is a Software Developer with more than 2 years experience in the domain of Data Science. He's a big open source and Python aficionado, currently the top-rated Python developer in India, and an active Python blogger. Satwik likes writing in-depth articles on various technical topics related to Data Science, Decentralized Applications, and Python. Apart from working full time as a software engineer, you may find him guest blogging for IBM DeveloperWorks and Learndatasci, freelancing, participating in Hackathons, or attending tech-conferences.
