Course overview
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.
Key skills
- 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
Course outline
Introduction to Deep Learning in TensorFlow [6 lessons]
Deep Learning Fundamentals 1h
Lesson Objectives- Identify the differences between a shallow and multi-layer (dense) neural network
- Implement forward propagation in Python
- Identify different types of activation functions in Python
- Implement an activation in Python
Introduction to TensorFlow Operations 1h
Lesson Objectives- Create tensors using TensorFlow
- Define data as constants and variables in TensorFlow
- Convert Tensors to NumPy arrays and vice versa
- Perform mathematical operations on tensors
Shallow Neural Network with the Sequential API 2h
Lesson Objectives- Perform exploratory analysis of the data
- Prepare data for machine learning
- Build and train a shallow neural network regression model
- Evaluate and make predictions on test data
- Visualize model results
Multi-layer (dense) Deep Learning Model 1h
Lesson Objectives- Build a multi-layer deep learning model using Sequential API
- Evaluate and make predictions on test data
- Visualize model results
- Compare the performance of a model using different activation functions
Deep Learning with the Functional API in Keras 1h
Lesson Objectives- 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
Guided Project: Predicting Listing Gains in the Indian IPO Market Using TensorFlow 1h
Lesson Objectives- 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
Predicting Listing Gains in the Indian IPO Market Using TensorFlow
For this project, you’ll work as a data scientist for an investment firm analyzing the Indian IPO market. You’ll build a deep learning model using TensorFlow to predict listing gains, applying skills in data exploration, visualization, preprocessing and modeling.
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