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Charles Ivan Niswander II
You've learned how to use Tensorflow. You've learned the important functions, how to design and implement sequential and functional models, and have completed several test projects. What's next? It's time to take a deep dive into activation functions, the essential function of every node and layer of a neural network, deciding whether to fire or not to fire, and adding an element of non-linearity (in most cases).
Read more
You've learned how to use Tensorflow. You've learned the important functions, how to design and implement sequential and functional models, and have completed several test projects. What's next? It's time to take a deep dive into activation functions, the essential function of every node and layer of a neural network, deciding whether to fire or not to fire, and adding an element of non-linearity (in most cases).
Read more
You've learned how to use Tensorflow. You've learned the important functions, how to design and implement sequential and functional models, and have completed several test projects. What's next? It's time to take a deep dive into activation functions, the essential function of every node and layer of a neural network, deciding whether to fire or not to fire, and adding an element of non-linearity (in most cases).
In this 2 hour course-based project, you will join me in a deep-dive into an exhaustive list of activation functions usable in Tensorflow and other frameworks. I will explain the working details of each activation function, describe the differences between each and their pros and cons, and I will demonstrate each function being used, both from scratch and within Tensorflow. Join me and boost your AI & machine learning knowledge, while also receiving a certificate to boost your resume in the process!
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/jfz66x/deep
What's inside
Syllabus
Deep Dive into Tensorflow Activation Functions
By the end of this project, you will learn about an exhaustive list of activation functions usable in Tensorflow and other frameworks. I will explain the working details of each activation function, describe the differences between each and their pros and cons, and I will demonstrate each function being used, both from scratch and within Tensorflow.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suited for learners with a foundation in TensorFlow who seek deep knowledge of activation functions
Provided detailed explanations and demonstrations of activation functions, fostering a strong understanding
Delivered by an experienced instructor known for expertise in the field of machine learning
Could benefit from incorporating more hands-on exercises or projects to enhance practical application
Course duration is relatively short, limiting the depth of coverage for some activation functions
Save this course
Save Deep-Dive into Tensorflow Activation Functions to your list so you can find it easily later:
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Reviews summary
Informative tensorflow explanations
According to students, this course provides mathematical explanations of activation functions, instead of Tensorflow based implementations, despite the course title "Deep-Dive into Tensorflow Activation Functions".
Implementation is not in Tensorflow
"Good with mathematical explaination of activation functions but I expected the implementation to be with Tensorflow and not with Numpy as the title said "Tensorflow Activation Functions"."
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 Deep-Dive into Tensorflow Activation Functions with these
activities:
Deep Learning with Python
Show steps
Read a comprehensive text on deep learning, which typically covers activation functions in great detail.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read the chapter or section on activation functions.
-
Work through the exercises or examples related to activation functions.
Activation Function Reference Sheet
Show steps
Organize your knowledge by creating a reference sheet summarizing the key aspects of different activation functions.
Show steps
-
Gather information on activation function characteristics.
-
Create a table or infographic summarizing the activation functions.
Activation Function Comparison Exercises
Show steps
Engage in practice drills to differentiate between and select appropriate activation functions for specific scenarios.
Show steps
-
Complete exercises comparing activation function outputs.
-
Practice choosing activation functions based on problem characteristics.
Five other activities
Expand to see all activities and additional details
Show all eight activities
PyTorch Activation Functions Tutorial
Show steps
Reinforce your knowledge of activation functions by following a guided tutorial on their implementation and usage in PyTorch.
Show steps
-
Follow a PyTorch tutorial on activation functions.
-
Practice implementing activation functions in PyTorch.
-
Apply activation functions in a simple PyTorch neural network.
TensorFlow Activation Function Implementation
Show steps
Implement various activation functions from scratch in TensorFlow to solidify your understanding of their functionality.
Show steps
-
Review theoretical concepts of activation functions.
-
Implement the ReLU, sigmoid, and tanh activation functions in TensorFlow.
-
Compare the outputs of the implemented activation functions.
-
Apply the implemented activation functions in a simple neural network.
Activation Function Study Group
Show steps
Engage in discussions and knowledge-sharing with peers to deepen your understanding of activation functions.
Show steps
-
Join or form a study group focused on activation functions.
-
Discuss the theoretical concepts and practical applications of activation functions.
-
Share and explore different perspectives on activation function usage.
Activation Function Mentorship Program
Show steps
Enhance your understanding by mentoring others on the topic of activation functions.
Show steps
-
Volunteer or participate in a mentorship program.
-
Provide guidance and support to mentees learning about activation functions.
-
Reflect on your own understanding and reinforce your knowledge through teaching.
Activation Function Impact on Model Performance
Show steps
Investigate the impact of different activation functions on the performance of a neural network model.
Show steps
-
Design and implement a neural network model with various activation functions.
-
Train and evaluate the model with different activation functions.
-
Analyze the results and discuss the impact of activation functions on model performance.
Deep Learning with Python
Show steps
Read a comprehensive text on deep learning, which typically covers activation functions in great detail.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read the chapter or section on activation functions.
- Work through the exercises or examples related to activation functions.
Activation Function Reference Sheet
Show steps
Organize your knowledge by creating a reference sheet summarizing the key aspects of different activation functions.
Show steps
Show steps
Show steps
- Gather information on activation function characteristics.
- Create a table or infographic summarizing the activation functions.
Activation Function Comparison Exercises
Show steps
Engage in practice drills to differentiate between and select appropriate activation functions for specific scenarios.
Show steps
Show steps
Show steps
- Complete exercises comparing activation function outputs.
- Practice choosing activation functions based on problem characteristics.
Five other activities
Expand to see all activities and additional details
Show all eight activities
PyTorch Activation Functions Tutorial
Show steps
Reinforce your knowledge of activation functions by following a guided tutorial on their implementation and usage in PyTorch.
Show steps
Show steps
Show steps
- Follow a PyTorch tutorial on activation functions.
- Practice implementing activation functions in PyTorch.
- Apply activation functions in a simple PyTorch neural network.
TensorFlow Activation Function Implementation
Show steps
Implement various activation functions from scratch in TensorFlow to solidify your understanding of their functionality.
Show steps
Show steps
Show steps
- Review theoretical concepts of activation functions.
- Implement the ReLU, sigmoid, and tanh activation functions in TensorFlow.
- Compare the outputs of the implemented activation functions.
- Apply the implemented activation functions in a simple neural network.
Activation Function Study Group
Show steps
Engage in discussions and knowledge-sharing with peers to deepen your understanding of activation functions.
Show steps
Show steps
Show steps
- Join or form a study group focused on activation functions.
- Discuss the theoretical concepts and practical applications of activation functions.
- Share and explore different perspectives on activation function usage.
Activation Function Mentorship Program
Show steps
Enhance your understanding by mentoring others on the topic of activation functions.
Show steps
Show steps
Show steps
- Volunteer or participate in a mentorship program.
- Provide guidance and support to mentees learning about activation functions.
- Reflect on your own understanding and reinforce your knowledge through teaching.
Activation Function Impact on Model Performance
Show steps
Investigate the impact of different activation functions on the performance of a neural network model.
Show steps
Show steps
Show steps
- Design and implement a neural network model with various activation functions.
- Train and evaluate the model with different activation functions.
- Analyze the results and discuss the impact of activation functions on model performance.
Career center
Learners who complete Deep-Dive into Tensorflow Activation Functions will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for developing and deploying machine learning models. This course, covering topics such asSwish activation functions and Mish activation functions can aid you in standing out in this role.
Postdoctoral Researcher
Postdoctoral Researcher
As a Postdoctoral Researcher, you will be expected to research and publish scientific findings. You will need to have a strong understanding of the scientific method and be able to design and conduct experiments. The Deep-Dive into Tensorflow Activation Functions course can help you build the skills you need to be successful in this role, covering topics such as residual networks (ResNets) and convolutional neural networks (CNNs)..
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and test AI systems. The Deep-Dive into Tensorflow Activation Functions course can help build your knowledge ofSwish activation functions and Mish activation functions, giving you an advantage in this role..
Data Scientist
Data Scientist
As a Data Scientist, you will be expected to use data to solve business problems. This course covers topics such as parametric ReLU (PReLU) activation functions, ELU activation functions, and Tanh activation functions, which will help equip you with valuable knowledge for the role.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop computer vision systems. The Deep-Dive into Tensorflow Activation Functions course can aid you in this role by covering topics such as Gaussian error linear units (GELUs) and hard sigmoid activation functions.
Natural Language Processing Engineer
Natural Language Processing Engineer
The focus of a Natural Language Processing Engineer is on the design and development of natural language processing systems. This course covers topics such as scaled exponential linear units (SELUs) and log sigmoid activation functions, which can give you an advantage in this role.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. This course can aid you by covering topics such as mish activation functions and hard tanh activation functions, which are knowledge points relevant for this role.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers develop and implement deep learning models. This course may be of help, covering topics such as linear activation functions, and exponential linear unit (ELU) activation functions.
Software Engineer
Software Engineer
The primary focus of a Software Engineer is on the design, development, and maintenance of software systems. This course is expected to be of help in learning about Keras activation functions, SoftMax activation functions, and Leaky ReLU activation functions to excel in this role.
Quantitative Analyst
Quantitative Analyst
The role of a Quantitative Analyst involves developing and using mathematical and statistical models to solve problems. This course may be useful, covering topics including softplus activation functions and softsign activation functions.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course may be useful, covering topics such as maxout activation functions and swish activation functions.
Data Analyst
Data Analyst
Data Analysts uncover patterns and insights from data. The Deep-Dive into Tensorflow Activation Functions course may help build a foundation for this role, as it covers topics such as leaky rectified linear units (LReLUs).
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course may be useful, covering topics such as sigmoid activation functions and hard sigmoid activation functions.
Project Manager
Project Manager
Project Managers plan and execute projects. This course may be useful, covering topics such as exponential linear unit (ELU) activation functions and scaled exponential linear units (SELUs).
Research Scientist
Research Scientist
In the role of a Research Scientist, you will have to answer complex questions about the world around us, and design and conduct studies to find answers. The Deep-Dive into Tensorflow Activation Functions course may be useful, covering topics such as rectified linear units (ReLUs) and sigmoid activation functions.
For more career information including salaries, visit:
OpenCourser.com/course/jfz66x/deep
Reading list
We've selected nine 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
Deep-Dive into Tensorflow Activation Functions.
Save
Is an excellent introduction to deep learning theory and practice. It must-read for anyone who wants to gain a deep understanding of deep learning.
Gentle, hands-on introduction to deep learning in Python and Keras, with an emphasis on training and evaluating deep learning models. It is essential reading for anyone new to deep learning or looking to update their knowledge.
Provides a practical introduction to machine learning using Scikit-learn, Keras, and TensorFlow. It valuable resource for anyone who wants to learn how to develop and deploy machine learning models in Python.
Provides a visual introduction to deep learning. It uses clear and concise illustrations to explain the theory and application of deep learning. It valuable resource for anyone who wants to learn about deep learning without getting bogged down in the mathematics.
Practical guide to using Keras for deep learning. It provides a clear and concise explanation of the theory and application of Keras.
Save
Provides a hands-on introduction to deep learning using TensorFlow 2.0. It valuable resource for anyone who wants to learn how to develop and deploy deep learning models in Python.
Comprehensive guide to generative deep learning. It provides a clear and concise explanation of the theory and application of generative deep learning. It valuable resource for anyone who wants to learn about generative deep learning.
Practical guide to applied machine learning. It provides a clear and concise explanation of the theory and application of applied machine learning. It valuable resource for anyone who wants to learn about applied machine learning.
Is an essential guide to using PyTorch for deep learning. It provides a clear and concise explanation of the theory and application of deep learning using PyTorch.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jfz66x/deep
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Charles Ivan Niswander II
Language
English
Good to know
Suited for learners with a foundation in TensorFlow who seek deep knowledge of activation functions
Provided detailed explanations and demonstrations of activation functions, fostering a strong understanding
Delivered by an experienced instructor known for expertise in the field of machine learning
Could benefit from incorporating more hands-on exercises or projects to enhance practical application
Course duration is relatively short, limiting the depth of coverage for some activation functions
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