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Working in a hands-on learning environment, led by our Keras 2.x Projects expert instructor, students will learn about and explore: Experimental projects showcasing the implementation of high-performance deep learning models with Keras.Use-cases across reinforcement learning, natural language processing GANs and computer vision.Build strong fundamentals of Keras in the area of deep learning and artificial intelligence.
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Last Updated
July 29, 2021
Students Enrolled
20
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Certification
Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more. By the end of this course, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.
Course Curriculum
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- Getting Started with Keras 00:00:00
- Introduction to Keras 00:00:00
- Keras backend options 00:00:00
- Installation 00:00:00
- Model fitting in Keras 00:00:00
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- Modeling Real Estate Using Regression Analysis 00:00:00
- Defining a regression problem 00:00:00
- Creating a linear regression model 00:00:00
- Multiple linear regression concepts 00:00:00
- Neural networks for regression using Keras 00:00:00
- Heart Disease Classification with Neural Networks 00:00:00
- Basics of classification problems 00:00:00
- Different types of classification 00:00:00
- Pattern recognition using a Keras neural network 00:00:00
- Fashion Article Recognition Using Convolutional Neural Networks 00:00:00
- Understanding computer vision concepts 00:00:00
- Convolutional neural networks 00:00:00
- Common CNN architecture 00:00:00
- Implementing a CNN for object recognition 00:00:00
- Stock Volatility Forecasting Using Long Short-Term Memory 00:00:00
- The basics of forecasting 00:00:00
- Time series analysis 00:00:00
- Time series models 00:00:00
- Long short-term memory in Keras 00:00:00
- Implementing an LSTM to forecast stock volatility 00:00:00
- Robot Control System Using Deep Reinforcement Learning 00:00:00
- Robot control overview 00:00:00
- The environment for controlling robot mobility 00:00:00
- Reinforcement learning basics 00:00:00
- Keras DQNs 00:00:00
- DQN to control a robot’s mobility 00:00:00
- What is Next? 00:00:00
- Deep learning methods 00:00:00
- Automated machine learning 00:00:00
- Differentiable neural computer 00:00:00
- Genetic programming and evolutionary strategies 00:00:00
- Inverse reinforcement learning 00:00:00
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