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This course is a part of the Self-Driving Car Engineer Nanodegree Program.
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This course is a part of the Self-Driving Car Engineer Nanodegree Program.
Read more
This course is a part of the Self-Driving Car Engineer Nanodegree Program.
Practice building deep neural networks from scratch, and then utilize deep learning libraries like TensorFlow and Keras to build and train models. You'll build models to perform traffic sign classification and then to clone human driving behavior and steer a car in a simulator.
Register for this course and see more details by visiting:
OpenCourser.com/course/s81ac6/udacity
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Taught by recognized experts in the field of autonomous vehicle development
Provides foundational knowledge and practical skills for building self-driving car systems
Focuses on deep learning techniques for traffic sign classification and driving behavior cloning
Emphasizes hands-on practice through the use of TensorFlow and Keras
Requires prior knowledge of Python and statistics
Part of a larger Nanodegree program, providing students with a more comprehensive learning experience
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Self-Driving Car Engineer - Deep Learning with these
activities:
Review linear algebra concepts
Show steps
Strengthen your foundation in linear algebra, which is essential for understanding deep learning.
Browse courses on
Linear Algebra
Show steps
-
Review notes or textbooks on linear algebra
-
Solve practice problems involving matrices and vectors
Review statistics concepts
Show steps
Refresh your knowledge of statistics, which is used in deep learning for data analysis and modeling.
Browse courses on
Statistics
Show steps
-
Review notes or textbooks on statistics
-
Solve practice problems involving probability distributions and statistical inference
Review deep learning books
Show steps
Develop a deeper understanding of deep learning concepts and algorithms.
View
Deep Learning
on Amazon
Show steps
-
Read at least three chapters
-
Take notes on key concepts and equations
-
Summarize the main ideas of each chapter
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow TensorFlow tutorials
Show steps
Familiarize yourself with using TensorFlow, a popular deep learning library.
Browse courses on
TensorFlow
Show steps
-
Find official TensorFlow tutorials on specific topics
-
Follow the tutorials step-by-step
-
Run the code examples provided in the tutorials
Build a simple neural network
Show steps
Gain practical experience in building and training neural networks.
Browse courses on
Neural Networks
Show steps
-
Choose a simple dataset
-
Implement a neural network model
-
Train the model on the dataset
-
Evaluate the performance of the model
Solve deep learning exercises
Show steps
Strengthen your understanding of deep learning through practice.
Show steps
-
Find online exercises or coding challenges
-
Solve the exercises in a timed environment
-
Review your solutions and identify areas for improvement
Participate in a Kaggle competition
Show steps
Put your deep learning skills to the test in a real-world challenge.
Browse courses on
Deep Learning Applications
Show steps
-
Find a Kaggle competition related to deep learning
-
Explore the competition data and requirements
-
Develop and submit your deep learning model
-
Monitor your model's performance and make adjustments
Review linear algebra concepts
Show steps
Strengthen your foundation in linear algebra, which is essential for understanding deep learning.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review notes or textbooks on linear algebra
- Solve practice problems involving matrices and vectors
Review statistics concepts
Show steps
Refresh your knowledge of statistics, which is used in deep learning for data analysis and modeling.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review notes or textbooks on statistics
- Solve practice problems involving probability distributions and statistical inference
Review deep learning books
Show steps
Develop a deeper understanding of deep learning concepts and algorithms.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read at least three chapters
- Take notes on key concepts and equations
- Summarize the main ideas of each chapter
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow TensorFlow tutorials
Show steps
Familiarize yourself with using TensorFlow, a popular deep learning library.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find official TensorFlow tutorials on specific topics
- Follow the tutorials step-by-step
- Run the code examples provided in the tutorials
Build a simple neural network
Show steps
Gain practical experience in building and training neural networks.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Choose a simple dataset
- Implement a neural network model
- Train the model on the dataset
- Evaluate the performance of the model
Solve deep learning exercises
Show steps
Strengthen your understanding of deep learning through practice.
Show steps
Show steps
Show steps
- Find online exercises or coding challenges
- Solve the exercises in a timed environment
- Review your solutions and identify areas for improvement
Participate in a Kaggle competition
Show steps
Put your deep learning skills to the test in a real-world challenge.
Browse courses on
Deep Learning Applications
Show steps
Show steps
Show steps
- Find a Kaggle competition related to deep learning
- Explore the competition data and requirements
- Develop and submit your deep learning model
- Monitor your model's performance and make adjustments
Career center
Learners who complete Self-Driving Car Engineer - Deep Learning will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
A Deep Learning Engineer develops, tests, and deploys deep learning models. These models are used in a variety of applications, including image recognition, natural language processing, and self-driving cars. This course will build a strong foundation for a career as a Deep Learning Engineer. The course will use TensorFlow and Keras, which are two popular deep learning libraries. You also learn about the mathematics behind deep learning, which will help you troubleshoot and improve your models.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models. These models are used in a variety of applications, including predictive analytics, fraud detection, and recommender systems. This course will provide you with a strong foundation in machine learning and help you develop the skills necessary to become a successful Machine Learning Engineer. In this course, you will learn about supervised learning, unsupervised learning, and deep learning.
Data Scientist
Data Scientist
A Data Scientist collects, analyzes, and interprets data to extract meaningful insights. These insights are used to make better decisions, improve products, and develop new strategies. This course will provide you with the skills necessary to become a successful Data Scientist. You will learn about data collection, data cleaning, data analysis, and data visualization.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. These systems are used in a variety of applications, including self-driving cars, medical devices, and financial trading systems. This course provides you with the skills necessary to become a successful Software Engineer. The course covers topics such as software design, software development, and software testing.
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer develops algorithms and systems that enable computers to see and interpret images and videos. These systems are used in a variety of applications, including self-driving cars, medical imaging, and security. This course provides you with the skills necessary to become a successful Computer Vision Engineer. The course covers topics such as image processing, computer vision algorithms, and deep learning.
Robotics Engineer
Robotics Engineer
A Robotics Engineer designs, builds, and maintains robots. These robots are used in a variety of applications, including manufacturing, healthcare, and space exploration. This course provides you with the skills necessary to become a successful Robotics Engineer. The course covers topics such as robot design, robot control, and robot programming.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs and develops AI systems. These systems are used in a variety of applications, including self-driving cars, natural language processing, and medical diagnosis. This course provides you with the skills necessary to become a successful Artificial Intelligence Engineer. The course covers topics such as AI algorithms, machine learning, and deep learning.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to extract meaningful insights. These insights are used to make better decisions, improve products, and develop new strategies. This course will provide you with the skills necessary to become a successful Data Analyst. You will learn about data collection, data cleaning, data analysis, and data visualization.
Business Analyst
Business Analyst
A Business Analyst gathers and analyzes information to help businesses make better decisions. This information can be used to improve products, services, or processes. This course will provide you with the skills necessary to become a successful Business Analyst. You will learn about data collection, data analysis, and data visualization.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products. This course will provide you with the skills necessary to become a successful Product Manager. You will learn about product development, product marketing, and product management.
Project Manager
Project Manager
A Project Manager is responsible for planning, executing, and closing projects. This course will provide you with the skills necessary to become a successful Project Manager. You will learn about project planning, project execution, and project management.
Management Consultant
Management Consultant
A Management Consultant helps businesses improve their performance. This course will provide you with the skills necessary to become a successful Management Consultant. You will learn about business analysis, strategy development, and change management.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make investment recommendations. This course will provide you with the skills necessary to become a successful Financial Analyst. You will learn about financial analysis, investment analysis, and portfolio management.
Marketing Manager
Marketing Manager
A Marketing Manager is responsible for developing and executing marketing campaigns. This course will provide you with the skills necessary to become a successful Marketing Manager. You will learn about marketing strategy, marketing planning, and marketing execution.
Sales Manager
Sales Manager
A Sales Manager is responsible for leading and motivating a sales team. This course will provide you with the skills necessary to become a successful Sales Manager. You will learn about sales management, sales strategy, and sales execution.
For more career information including salaries, visit:
OpenCourser.com/course/s81ac6/udacity
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Self-Driving Car Engineer - Deep Learning.
Save
A comprehensive textbook that covers the fundamental concepts of deep learning, from basic neural networks to advanced topics such as generative adversarial networks and reinforcement learning.
A beginner-friendly introduction to deep learning that teaches you how to build and train deep learning models using Keras, a high-level API built on top of TensorFlow.
A comprehensive guide to machine learning that covers both supervised and unsupervised learning algorithms, as well as deep learning models like neural networks and convolutional neural networks.
A gentle introduction to machine learning that assumes no prior knowledge of the subject. Covers the basics of supervised and unsupervised learning, as well as how to build and evaluate machine learning models.
A comprehensive textbook that covers the fundamental concepts of machine learning, from basic statistical models to advanced topics such as kernel methods and Bayesian learning.
Save
A classic textbook that covers the mathematical foundations of machine learning, including topics such as probability theory, Bayesian statistics, and kernel methods.
A comprehensive introduction to deep learning for natural language processing, which covers topics such as text classification, text generation, and machine translation.
A classic textbook that covers the fundamental concepts of reinforcement learning, from basic concepts such as Markov decision processes to advanced topics such as deep reinforcement learning.
A practical guide that shows you how to build and train deep learning models using R, a popular programming language for data science.
A comprehensive introduction to deep learning for robotics, which covers topics such as robot vision, robot motion planning, and robot reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/s81ac6/udacity
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Good to know
Taught by recognized experts in the field of autonomous vehicle development
Provides foundational knowledge and practical skills for building self-driving car systems
Focuses on deep learning techniques for traffic sign classification and driving behavior cloning
Emphasizes hands-on practice through the use of TensorFlow and Keras
Requires prior knowledge of Python and statistics
Part of a larger Nanodegree program, providing students with a more comprehensive learning experience
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