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Thomas Henson
Deep learning is one of the hottest topics for machine learning engineers. In this course, you'll quickly jump into building your first neural network using TFLearn on top of Tensorflow.
Read more
Deep learning is one of the hottest topics for machine learning engineers. In this course, you'll quickly jump into building your first neural network using TFLearn on top of Tensorflow.
Read more
Deep learning is one of the hottest topics for machine learning engineers. In this course, you'll quickly jump into building your first neural network using TFLearn on top of Tensorflow.
TFLearn offers machine learning engineers the ability to build Tensorflow neural networks with minimal use of coding. In this course, Implementing Multi-layer Neural Networks with TFLearn, you’ll learn foundational knowledge and gain the ability to build Tensorflow neural networks. First, you’ll explore how deep learning is used to accelerate artificial intelligence. Next, you’ll discover how to build convolutional neural networks. Finally, you’ll learn how to deploy both deep and generative neural networks. When you’re finished with this course, you’ll have the skills and knowledge of deep learning needed to build the next generation of artificial intelligence.
Register for this course and see more details by visiting:
OpenCourser.com/course/mcoitg/implementing
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What's inside
Syllabus
Course Overview
Why Deep Learning?
What Is TFLearn?
Implementing Layers in TFLearn
Read more
Syllabus
Course Overview
Why Deep Learning?
What Is TFLearn?
Implementing Layers in TFLearn
Read more
Building Activations in TFLearn
Managing Data with TFLearn
Running Models with TFLearn
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops foundational knowledge of TFLearn for building Tensorflow neural networks
Teaches foundational deep learning knowledge for the next generation of artificial intelligence
Provides a comprehensive study of TFLearn, a deep learning library for building neural networks
Teaches how to build convolutional neural networks, a type of deep neural network used for image and pattern recognition
Taught by Thomas Henson, an experienced instructor in machine learning and deep learning
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Save Implementing Multi-layer Neural Networks with TFLearn to your list so you can find it easily later:
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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 Implementing Multi-layer Neural Networks with TFLearn with these
activities:
Review Python Basics
Show steps
Refresh your understanding of Python to ensure a smooth transition into this course.
Browse courses on
Python
Show steps
-
Review core Python concepts such as data types, variables, and operators.
-
Practice writing simple Python scripts.
Explore Tensorflow Tutorials
Show steps
Strengthen your foundation in Tensorflow and neural networks by following expert-led tutorials.
Browse courses on
TensorFlow
Show steps
-
Identify specific areas where you need additional support.
-
Search for online tutorials and resources provided by Tensorflow or other reputable sources.
-
Follow the tutorials step-by-step, taking notes and experimenting with the code examples.
Review 'Deep Learning with Python'
Show steps
Enhance your understanding of deep learning concepts by exploring a comprehensive reference book.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read specific chapters relevant to the course topics.
-
Take notes and highlight key concepts.
-
Work through the exercises and examples provided in the book.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice TFLearn Functions
Show steps
Reinforce the fundamentals of TFLearn by working through practice drills.
Show steps
-
Solve practice problems using the TFLearn syntax.
-
Experiment with different TFLearn functions and their effects on neural network performance.
Create a Neural Network Tutorial
Show steps
Solidify your understanding of neural networks and TFLearn by creating a comprehensive tutorial.
Browse courses on
TensorFlow
Show steps
-
Choose a specific topic within TFLearn or neural networks to focus on.
-
Gather relevant resources and materials, such as code snippets, diagrams, and explanations.
-
Develop a clear and concise outline for your tutorial.
-
Write and record your tutorial content, ensuring it is well-organized and engaging.
Contribute to Open Source Deep Learning Projects
Show steps
Gain valuable experience and contribute to the deep learning community by volunteering on open source projects.
Browse courses on
Open Source
Show steps
-
Identify open source deep learning projects aligned with your interests.
-
Review the project documentation and codebase.
-
Identify areas where you can contribute your skills and knowledge.
Attend a Deep Learning Hackathon
Show steps
Gain practical experience and collaborate with others on real-world deep learning projects.
Browse courses on
TensorFlow
Show steps
-
Find and register for an appropriate deep learning hackathon.
-
Form a team or work individually on a project.
-
Develop and implement a deep learning solution for a specific problem or challenge.
Build a Deep Learning Web Application
Show steps
Apply your knowledge of deep learning and TFLearn to create a tangible and practical web application.
Browse courses on
TensorFlow.js
Show steps
-
Identify a problem or use case that can be addressed with deep learning.
-
Design and develop the web application architecture.
-
Implement the deep learning model using TFLearn and integrate it into your web application.
-
Test and deploy your web application.
Review Python Basics
Show steps
Refresh your understanding of Python to ensure a smooth transition into this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review core Python concepts such as data types, variables, and operators.
- Practice writing simple Python scripts.
Explore Tensorflow Tutorials
Show steps
Strengthen your foundation in Tensorflow and neural networks by following expert-led tutorials.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify specific areas where you need additional support.
- Search for online tutorials and resources provided by Tensorflow or other reputable sources.
- Follow the tutorials step-by-step, taking notes and experimenting with the code examples.
Review 'Deep Learning with Python'
Show steps
Enhance your understanding of deep learning concepts by exploring a comprehensive reference book.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read specific chapters relevant to the course topics.
- Take notes and highlight key concepts.
- Work through the exercises and examples provided in the book.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice TFLearn Functions
Show steps
Reinforce the fundamentals of TFLearn by working through practice drills.
Show steps
Show steps
Show steps
- Solve practice problems using the TFLearn syntax.
- Experiment with different TFLearn functions and their effects on neural network performance.
Create a Neural Network Tutorial
Show steps
Solidify your understanding of neural networks and TFLearn by creating a comprehensive tutorial.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Choose a specific topic within TFLearn or neural networks to focus on.
- Gather relevant resources and materials, such as code snippets, diagrams, and explanations.
- Develop a clear and concise outline for your tutorial.
- Write and record your tutorial content, ensuring it is well-organized and engaging.
Contribute to Open Source Deep Learning Projects
Show steps
Gain valuable experience and contribute to the deep learning community by volunteering on open source projects.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open source deep learning projects aligned with your interests.
- Review the project documentation and codebase.
- Identify areas where you can contribute your skills and knowledge.
Attend a Deep Learning Hackathon
Show steps
Gain practical experience and collaborate with others on real-world deep learning projects.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find and register for an appropriate deep learning hackathon.
- Form a team or work individually on a project.
- Develop and implement a deep learning solution for a specific problem or challenge.
Build a Deep Learning Web Application
Show steps
Apply your knowledge of deep learning and TFLearn to create a tangible and practical web application.
Browse courses on
TensorFlow.js
Show steps
Show steps
Show steps
- Identify a problem or use case that can be addressed with deep learning.
- Design and develop the web application architecture.
- Implement the deep learning model using TFLearn and integrate it into your web application.
- Test and deploy your web application.
Career center
Learners who complete Implementing Multi-layer Neural Networks with TFLearn will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
As a Deep Learning Engineer, you will design and implement deep learning solutions. This course can help you get started. It will teach you the foundational knowledge of deep learning and how to build Tensorflow neural networks. This will allow you to get started with the next generation of artificial intelligence and build powerful deep learning models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop machine learning algorithms that can make predictions or take actions. This course will help you become a Machine Learning Engineer. It will teach you how to build Tensorflow neural networks from the ground using TFLearn. You will learn how to deploy models as well. Master the skills and knowledge needed to leverage machine learning to create the next generation of AI.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers develop and maintain artificial intelligent software and systems. This course can help you get started. It will teach you foundational knowledge and help you build Tensorflow neural networks. You will learn how to implement deep and generative neural networks. These skills will give you the foundation you need to kick off your career as an Artificial Intelligence Engineer.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and build systems that can interpret and understand images. This course is useful for the aspiring Computer Vision Engineer. It will help you build a strong foundation in TensorFlow neural networks. Using TFLearn, you will learn how to build powerful deep learning models. This skillset is in high demand for Computer Vision Engineers.
Data Scientist
Data Scientist
As a Data Scientist, you will analyze data and draw conclusions from it. Some Data Scientists use that information to make recommendations to their employers. Others use their findings to develop products. TFLearn, a Python library built on top of Tensorflow, will allow you to build neural networks to analyze diverse data and present your findings clearly. Taking this course will help you build a solid foundation for your new career as a Data Scientist.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze risks and trends. This course is useful for aspiring Quantitative Analysts. It will teach you how to build Tensorflow neural networks and gain a strong foundation in deep learning. This will enable you to develop more powerful quantitative models and expand your career opportunities as a Quantitative Analyst.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design and build systems that can understand and interpret human language. This course provides a foundation of knowledge relevant to the aspiring Natural Language Processing Engineer. You will learn how to build Tensorflow neural networks. Using TFLearn, you will learn how to deploy both deep and generative neural networks. These skills are highly valued for Natural Language Processing Engineers.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use analytical methods to solve complex business problems. This course is useful for aspiring Operations Research Analysts. You will learn how to build Tensorflow neural networks, which is a valuable skill in this domain. It will help you develop powerful optimization models that can address complex problems faced in operations research.
Software Engineer
Software Engineer
Software Engineers use coding languages to design and build software programs. This course is relevant to those who want to become a Software Engineer. It will teach you how to build Tensorflow neural networks with TFLearn. This skill is extremely valuable for those working on deep learning and machine learning applications.
Data Analyst
Data Analyst
Data Analysts use data to create insights and draw conclusions. This course may be useful for those who wish to become a Data Analyst. It will help you build foundational knowledge of TensorFlow neural networks. Using TFLearn, you will learn how to build deep and generative neural networks. This skill may support you if you wish to become a Data Analyst.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts are responsible for collecting, analyzing, and interpreting data. This course is useful for aspiring Business Intelligence Analysts. It will help you develop a foundational understanding of TensorFlow neural networks. This skill can help you analyze data and draw significant conclusions that can support your business.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, and interpret data. This course may be useful for aspiring Statisticians. It will help you develop foundational knowledge of Tensorflow neural networks. This knowledge can support you in building robust statistical models and uncovering valuable insights from data.
Product Manager
Product Manager
Product Managers are responsible for developing, launching, and marketing products. This course will help you gain knowledge that is highly valued by Product Managers. You will learn how to build Tensorflow neural networks and gain foundational knowledge of deep learning. This will help you make better decisions about products that leverage deep learning and machine learning.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risks and make financial decisions. This course may be useful for aspiring Actuaries. It will introduce you to Tensorflow neural networks and help you build a foundation in deep learning. This knowledge can support you in your actuarial work.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. This course may be useful for aspiring Financial Analysts. It will help you gain foundational knowledge of Tensorflow neural networks. This can support you as you analyze financial data to make better investment decisions.
For more career information including salaries, visit:
OpenCourser.com/course/mcoitg/implementing
Reading list
We've selected six 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
Implementing Multi-layer Neural Networks with TFLearn.
Provides a comprehensive overview of deep learning techniques for natural language processing. It covers a wide range of topics, including text classification, machine translation, and question answering.
Provides a comprehensive overview of reinforcement learning techniques using TensorFlow. It covers a wide range of topics, including reinforcement learning algorithms, environments, and applications.
Provides a comprehensive overview of deep learning, covering the fundamental concepts, architectures, and applications of deep learning models. It valuable resource for both beginners and experienced practitioners in the field.
Provides a comprehensive overview of deep learning techniques for computer vision. It covers a wide range of topics, including image classification, object detection, and image segmentation.
Provides a hands-on introduction to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers the fundamental concepts and algorithms of machine learning, and it includes practical exercises and case studies.
Save
Offers a practical guide to natural language processing using TensorFlow. It covers a wide range of topics, including text preprocessing, feature engineering, and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mcoitg/implementing
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Time
129 hours
Level
Intermediate
Via
Pluralsight
Instructor
Thomas Henson
Language
English
Good to know
Develops foundational knowledge of TFLearn for building Tensorflow neural networks
Teaches foundational deep learning knowledge for the next generation of artificial intelligence
Provides a comprehensive study of TFLearn, a deep learning library for building neural networks
Teaches how to build convolutional neural networks, a type of deep neural network used for image and pattern recognition
Taught by Thomas Henson, an experienced instructor in machine learning and deep learning
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