Deep learning is a discipline in artificial intelligence that has recently garnered a lot of interest. It’s used to solve complex problems in various fields such as computer vision, natural language processing, robotics, and others that might be difficult to solve using traditional machine learning methods.
In this course, you’ll start with the fundamentals of deep learning, and you’ll explore the TensorFlow library. Then, you’ll learn to build, train, and evaluate deep learning regression and classification models using the TensorFlow framework.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll apply your new skills to a project to build a deep neural network model that can predict the listing gains of IPOs on the Indian market.
- Explaining the major concepts and terminology used in deep learning
- Performing data preprocessing and exploratory analysis to prepare data for modeling
- Building and evaluating deep learning regression models using TensorFlow’s Sequential and Functional APIs in Keras
- Building a deep neural network to predict listing gains of IPOs on the Indian market
Introduction to Deep Learning in TensorFlow [6 lessons]
- Compare and contrast the Sequential and Functional API for model building
- Explore and prepare a dataset for classification modeling
- Visualize the main features of a dataset
- Build and summarize a binary classification model using Functional API
- Evaluate the performance of the classification model
- Perform exploratory analysis, visualization, and preprocessing on the dataset
- Build a hold-out validation approach to model evaluation
- Build and train the multi-layer classification deep learning model using the Sequential API in TensorFlow
Projects in this course
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