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TechMaster - Deep Learning with TensorFlow
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Course
TechMaster - Deep Learning with TensorFlow
Build skills and get hands-on experience using TensorFlow. Explore its main functions and operations, and investigate different types of deep architecture, including convolutional networks, recurrent networks, and autoencoders.
Kick-start your deep learning career with critical skills in this core technology.
Flexible Schedule
Beginner Level
Mentor Support
This course is part of a program:
It is not possible to enroll for individual courses on this program. If you wish to take this course, please enroll for the full program.
TensorFlow is a popular deep learning framework for businesses seeking to maximise the value of their data. Created by Google, it was tailored for machine learning and is now deemed to be one of the best libraries for implementing deep learning. Individuals with strong skills in TensorFlow, therefore, are in high demand.
During this course, you will be introduced to the basic concepts of TensorFlow. You will explore its main functions, operations, and the execution pipeline. Starting with a Hello World example, you will first investigate how TensorFlow can be used in curve fitting, regression, classification, and the minimization of error functions.
You will then learn how to apply TensorFlow for backpropagation to tune weights and biases while neural networks are being trained. Plus, you will be introduced to different types of deep architecture, including convolutional networks, recurrent networks, and autoencoders.
For individuals keen to build a career in the fast-growing field of deep learning, Deep Learning with TensorFlow is the ideal place to start.
This IBM certified course comprises five purposely designed modules that take you on a carefully defined learning journey.
It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 3-4 hours per week, you will complete the course in 5 weeks. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And dont worry, youre not alone! You will be encouraged to stay connected with your learning community and mentors through the course discussion space.
The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include videos, reading material, and online exam questions.
As part of our mentoring service you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions. Mentoring services will vary across packages.
Once you have successfully completed the course, you will earn your IBM Certificate.
After completing this course, you will be able to:
- Explain foundational TensorFlow concepts such as the main functions, operations, and execution pipelines.
- Describe how TensorFlow can be used in curve fitting, regression, classification, and the minimization of error functions.
- Understand different types of deep architectures such as convolutional networks, recurrent networks, and autoencoders.
- Apply TensorFlow for backpropagation to tune weights and biases while the neural networks are being trained.
This course is suitable for learners with both technical and non-technical backgrounds.
It is particularly useful for people working in, or aspiring to work in, the following roles:
- Machine learning engineers
- Data scientists
- Business intelligence analysts
- Data quality analysts
Course Outline
Welcome! (2:06)
Prerequisites and Recommended skills
Learning Objectives
Syllabus
Grading Scheme
Change Log
Copyrights and Trademarks
Learning Objectives
Intro to TensorFlow (7:00)
Lab: Hello world
TensorFlow 2.x and Eager Execution (4:21)
Lab: Linear Regression with TensorFlow
Lab: Logistic Regression with TensorFlow
Intro to Deep Learning (2:39)
Deep Neural Networks (11:48)
Graded Review Questions
Learning Objectives
Intro to CNNs (4:37)
CNNs for Classification (4:09)
CNN Architecture (13:05)
Lab: Underestanding Convolutions
Lab: CNN with TensorFlow
Graded Review Questions
Learning Objectives
The Sequential Problem (3:06)
The RNN Model (5:28)
The LSTM Model (5:25)
Lab: Basics of LSTM
Applying RNNs to Language Modelling (7:38)
Lab: Language Modelling with LSTM
Graded Review Questions
Learning Objectives
Intro to RBMs (4:29)
Training RBMs (4:16)
Lab: Restricted Boltzmann Machines
Lab: Collaborative Filtering with RBM
Graded Review Questions
Intro to Autoencoders (4:51)
Autoencoder Structure(4:10)
Lab: Autoencoders
Graded Review Questions
Course Summary (2:18)
Instructions
Final Exam
Retake Exam
Feedback
Certificate
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FAQs
You will learn about TensorFlow's fundamental concepts, primary functions, operations, and execution pipeline. During the course, you'll discover how to utilize TensorFlow for curve fitting, regression, classification, and error function minimization. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the neural networks are trained. You will also get a chance to explore deep architectures, such as convolutional networks, recurrent networks, and autoencoders.
In order to reduce the effort involved in creating diverse neural network models, TensorFlow provides pre-built functions within its sophisticated offering. It also provides valuable infrastructure and hardware, which distinguishes it as one of the major libraries in the deep learning domain.
TensorFlow is a deep learning-focused open-source library. It is used in the domain of machine learning. And its worth noting that though TensorFlow was created with deep learning in mind, it was originally designed for huge numerical computations.
Yes, Deep Learning with TensorFlow has options for mentoring. Check the payment plans available for more details.
As part of our mentoring service, you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space to ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions.
Yes. This course is suitable for learners with both technical and non-technical backgrounds. However, having good knowledge of Python programming language and neural networks is an added advantage.
Yes. Deep Learning with TensorFlow course includes hands-on labs that you can complete during the course.
Yes. This course is 100% online. All you need is a good connection to the internet to access the course materials.
You will be able to access the course materials through your dashboard as soon as you enroll in this course.
Type of certificate
IBM Certificate
About this course
05 Modules
05 Skills
Includes
Discussion space
05 Hands-on labs
03 Exercises
28 Videos
05 Review questions
01 Final exam
Create
Recurrent neural network
Restricted Boltzmann machine
Deep belief network
Exercises to explore
TensorFlow working
Linear & logistic regression with TensorFlow
Create a recurrent neural network
Language modeling
Recommendation system
Deep belief network
This course has been created by
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