Deep-Dive into Tensorflow Activation Functions from Coursera

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.

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

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

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

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Read a comprehensive text on deep learning, which typically covers activation functions in great detail.

View Deep Learning with Python, Second Edition on Amazon

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Read the chapter or section on activation functions.
Work through the exercises or examples related to activation functions.

Activation Function Reference Sheet

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Organize your knowledge by creating a reference sheet summarizing the key aspects of different activation functions.

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Gather information on activation function characteristics.
Create a table or infographic summarizing the activation functions.

Activation Function Comparison Exercises

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Engage in practice drills to differentiate between and select appropriate activation functions for specific scenarios.

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Complete exercises comparing activation function outputs.
Practice choosing activation functions based on problem characteristics.

Five other activities

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PyTorch Activation Functions Tutorial

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Reinforce your knowledge of activation functions by following a guided tutorial on their implementation and usage in PyTorch.

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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

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Implement various activation functions from scratch in TensorFlow to solidify your understanding of their functionality.

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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

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Engage in discussions and knowledge-sharing with peers to deepen your understanding of activation functions.

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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

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Enhance your understanding by mentoring others on the topic of activation functions.

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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

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Investigate the impact of different activation functions on the performance of a neural network model.

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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

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.

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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)..

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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..

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Statistician

Statisticians collect, analyze, interpret, and present data. This course may be useful, covering topics such as maxout activation functions and swish activation functions.

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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).

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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.

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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).

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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.

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