Build Deep Learning Models with TensorFlow
Use TensorFlow to build and tune deep learning models.
Includes Python, Deep Learning, Neural Networks, TensorFlow, Keras, and more.
Time to complete6 weeks
Certificate of completionYes
Prerequisites1 courseWe suggest you complete the following courses before you get started with Build Deep Learning Models with TensorFlow:
- Learn Machine Learning
About this skill path
Deep learning is a cutting-edge form of machine learning inspired by the architecture of the human brain, but it doesn’t have to be intimidating. In this Skill Path, you will use TensorFlow and Keras to train, test, and tune neural networks for regression and classification. Along the way, you will demonstrate your skills by building actual models with real data.
Skills you'll gain
- Train and tune neural networks
- Run regression and classification models
- Choose the right model for the job
Syllabus7 units • 6 lessons • 6 projects • 3 quizzes
Welcome to the Build Deep Learning Models with TensorFlow Skill Path
Overview of material in the Build Deep Learning Models with Tensorflow skill path
Foundations of Deep Learning and Perceptrons
Before developing your own models, take a dive into deep learning fundamentals!
Getting Started with TensorFlow
Build your own neural networks using TensorFlow!
Take a dive into classification models, including image classification, with deep learning neural networks!
Deep Learning in the Real World
Deeper dives into applications of deep learning and how you can use your skills to solve real-world problems!
Deep Learning Portfolio Project
Put your deep learning skills to the test with your very own portfolio project!
Next steps to take in your deep learning journey!
Hands-on learningDon't just watch or read about someone else coding — write your own code live in our online, interactive platform. You'll even get AI-driven recommendations on what you need to review to help keep you on track.
Projects in this skill path
Perceptron Logic GatesTrain Perceptrons to work like logic gates! This simple neuron can act as an AND or an OR gate.
Implementing Neural NetworksThe World Health Organization (WHO)'s Global Health Observatory (GHO) data repository tracks life expectancy for countries worldwide by following health status and many other related factors. Although there have been a lot of studies undertaken in the past on factors affecting life expectancy considering demographic variables, income composition, and mortality rates, it was found that the effects of immunization and human development index were not taken into account. This dataset covers a variety of indicators for all countries from 2000 to 2015 including: * immunization factors * mortality factors * economic factors * social factors * other health-related factors Ideally, this data will eventually inform countries concerning which factors to change in order to improve the life expectancy of their populations. If we can predict life expectancy well given all the factors, this is a good sign that there are some important patterns in the data. Life expectancy is expressed in years, and hence it is a number. This means that in order to build a predictive model one needs to use regression. In this project, you will design, train, and evaluate a neural network model performing the task of regression to predict the life expectancy of countries using this dataset. Excited? Let's go!
Deep Learning Regression with Admissions DataYou will create a regression deep learning model that predicts an applicant's graduate admissions chances based on various parameters, such as grades, test scores, and program rating.
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What's included in skill paths
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