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Dr. Ryan Ahmed, Ph.D., MBA,
SuperDataScience Team,
Mitchell Bouchard,
and
Ligency Team
Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster. TensorFlow 2.0 is Google’s most powerful, recently released open source platform to build and deploy AI models in practice.
AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab.
Read more
Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster. TensorFlow 2.0 is Google’s most powerful, recently released open source platform to build and deploy AI models in practice.
AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab.
Read more
Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster. TensorFlow 2.0 is Google’s most powerful, recently released open source platform to build and deploy AI models in practice.
AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab.
The course provides students with practical hands-on experience in training Artificial Neural Networks and Convolutional Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to:
(1) Train Feed Forward Artificial Neural Networks to perform regression tasks such as sales/revenue predictions and house price predictions
(2) Develop Artificial Neural Networks in the medical field to perform classification tasks such as diabetes detection.
(3) Train Deep Learning models to perform image classification tasks such as face detection, Fashion classification and traffic sign classification.
(4) Develop AI models to perform sentiment analysis and analyze customer reviews.
(5) Perform AI models visualization and assess their performance using Tensorboard
(6) Deploy AI models in practice using Tensorflow 2.0 Serving
The course is targeted towards students wanting to gain a fundamental understanding of how to build and deploy models in Tensorflow 2.0. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master AI and Deep Learning techniques and can directly apply these skills to solve real world challenging problems using Google’s New TensorFlow 2.0.
Register for this course and see more details by visiting:
OpenCourser.com/course/uw3w9r/tensorflow
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What's inside
Learning objectives
- Master google’s newly released tensorflow 2.0 to build, train, test and deploy artificial neural networks (anns) models.
- Learn how to develop anns models and train them in google’s colab while leveraging the power of gpus and tpus.
- Deploy anns models in practice using tensorflow 2.0 serving.
- Learn how to visualize models graph and assess their performance during training using tensorboard.
- Understand the underlying theory and mathematics behind artificial neural networks and convolutional neural networks (cnns).
- Learn how to train network weights and biases and select the proper transfer functions.
- Train artificial neural networks (anns) using back propagation and gradient descent methods.
- Optimize anns hyper parameters such as number of hidden layers and neurons to enhance network performance.
- Apply anns to perform regression tasks such as house prices predictions and sales/revenue predictions.
- Assess the performance of trained ann models for regression tasks using kpi (key performance indicators) such as mean absolute error, mean squared error, and root mean squared error, r-squared, and adjusted r-squared.
- Assess the performance of trained ann models for classification tasks using kpi such as accuracy, precision and recall.
- Apply convolutional neural networks to classify images.
- Sample real-world, practical projects:
- Project #1: train simple ann to convert celsius temperature reading to fahrenheit
- Project #2 (exercise): train feedforward ann to predict revenue/sales
- Project #3: as a real-estate consultant, predict house prices using anns (regression task)
- Project #4 (exercise): as a business owner, predict bike rental usage (regression task)
- Project #5: develop artificial neural networks in the medical field to perform classification tasks such as diabetes detection (classification task)
- Project #6: develop ai models to perform sentiment analysis and analyze online customer reviews.
- Project #7: train lenet deep learning models to perform traffic signs classification.
- Project #8: train cnn to perform fashion classification
- Project #9: train cnn to perform image classification using cifar-10 dataset
- Project #10: deploy deep learning image classification model using tf serving
- Show more
- Show less
- Master google’s newly released tensorflow 2.0 to build, train, test and deploy artificial neural networks (anns) models.
- Learn how to develop anns models and train them in google’s colab while leveraging the power of gpus and tpus.
- Deploy anns models in practice using tensorflow 2.0 serving.
- Learn how to visualize models graph and assess their performance during training using tensorboard.
- Understand the underlying theory and mathematics behind artificial neural networks and convolutional neural networks (cnns).
- Learn how to train network weights and biases and select the proper transfer functions.
- Train artificial neural networks (anns) using back propagation and gradient descent methods.
- Optimize anns hyper parameters such as number of hidden layers and neurons to enhance network performance.
- Apply anns to perform regression tasks such as house prices predictions and sales/revenue predictions.
- Assess the performance of trained ann models for regression tasks using kpi (key performance indicators) such as mean absolute error, mean squared error, and root mean squared error, r-squared, and adjusted r-squared.
- Assess the performance of trained ann models for classification tasks using kpi such as accuracy, precision and recall.
- Apply convolutional neural networks to classify images.
- Sample real-world, practical projects:
- Project #1: train simple ann to convert celsius temperature reading to fahrenheit
- Project #2 (exercise): train feedforward ann to predict revenue/sales
- Project #3: as a real-estate consultant, predict house prices using anns (regression task)
- Project #4 (exercise): as a business owner, predict bike rental usage (regression task)
- Project #5: develop artificial neural networks in the medical field to perform classification tasks such as diabetes detection (classification task)
- Project #6: develop ai models to perform sentiment analysis and analyze online customer reviews.
- Project #7: train lenet deep learning models to perform traffic signs classification.
- Project #8: train cnn to perform fashion classification
- Project #9: train cnn to perform image classification using cifar-10 dataset
- Project #10: deploy deep learning image classification model using tf serving
- Show more
- Show less
Syllabus
INTRODUCTION AND COURSE OUTLINE
Introduction and Welcome Message
Course Overview
EXTRA: Learning Path
Read more
Syllabus
INTRODUCTION AND COURSE OUTLINE
Introduction and Welcome Message
Course Overview
EXTRA: Learning Path
Read more
What's AI, ML and DL
Machine Learning - Big Picture
Whats new in TF2 and Google Colab
Whats New in TensorFlow 2.0
What is Google Colab
Google Colab Demo
Eager Execution
Keras API
Get the materials
BUILD YOUR FIRST SIMPLE PERCEPTRON (SINGLE NEURON) MODEL IN TF 2.0
PROJECT #1 OVERVIEW: CONVERT CELSUIS TO FAHRENHEIT
PROJECT #1 What are ANNs and How they learn?
PROJECT #1 Build our first ANN model
PROJECT #1 TF Playground
PROJECT #1 Coding Step 1 - Load TF and Data
PROJECT #1 Coding Step 2 - Model Training
PROJECT #1 Coding Step 3 - Model Evaluation
PROJECT #2 Overview
PROJECT#2: Google Colab Questions Overview
PROJECT # 2 Coding Part 1
PROJECT # 2 Coding Part 2
PROJECT # 2 Coding Part 3
BUILD A MULTI LAYER ARTIFICIAL NEURAL NETWORKS FOR REGRESSION TASKS
PROJECT #3: Overview
PROJECT #3 Regression basics
PROJECT #3 ANN in Action
PROJECT #3 Activation functions overview
PROJECT #3 MultiLayer Perceptron Network
PROJECT #3 ANN Training and Epochs Definition
PROJECT #3 Tensorflow Playground 3
PROJECT #3 Gradient Descent
PROJECT #3 Back Propagation
PROJECT #3 Bias Variance Tradeoff
PROJECT #3 Performance Metrics
PROJECT #3 Coding part 1
PROJECT #3 Coding part 2
PROJECT #3 Coding part 3
PROJECT #3 Coding part 4
PROJECT #3 Coding part 5 - Training
PROJECT #3 Coding part 6
PROJECT #4 Overview
PROJECT #4 Google Colab Overview
PROJECT #4 Coding Part 1
PROJECT #4 Coding Part 2
PROJECT #4 Coding Part 3
ARTIFICIAL NEURAL NETWORKS FOR CLASSIFICATION TASKS
PROJECT #5 Project Overview sentiment
PROJECT #5 Tokenization and Count Vectorizer
PROJECT #5 Confusion Matrix
PROJECT #5 Load Dataset
PROJECT #5 Data Visualization
PROJECT #5 Data Tokenization
PROJECT #5 Model Building and Training
PROJECT #5 Model Evaluation
PROJECT #6 Project Overview
PROJECT #6 Google Colab Project Questions Overview
PROJECT #6 Google Colab Project Questions Overview 2
PROJECT #6 Project Coding Solution Part 1
PROJECT #6 Project Coding Solution Part 2
DEEP LEARNING FOR IMAGE CLASSIFICATION
PROJECT #7 Overview
PROJECT #7 CNN Entire Network Overview
PROJECT #7 Feature Detectors
PROJECT #7 RELU
PROJECT #7 Pooling and Downsampling
PROJECT #7 Performance Improvement
PROJECT #7 Coding part 1 Import Data
PROJECT #7 Coding part 2 Visualization
PROJECT #7 Coding part 3 Train model
PROJECT #7 Coding part 4 - Evaluate model
PROJECT #8 Project Overview
PROJECT #8 LeNet Architecture
PROJECT #8 Coding part 1
PROJECT #8 Coding part 2
PROJECT #8 Coding part 3
PROJECT #9 Overview
PROJECT #9 Questions Overview
PROJECT #9 Solution Part 1
PROJECT #9 Solution Part 2
MODEL DEPLOYMENT USING TF SERVING
TF Serving Coding Part 1
TF Serving Coding Part 2
TF Serving Coding Part 3
Tensorboard Example 1
Tensorboard Example 2
Distributed Strategy
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Good to know
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Know what's good ,
what to watch for , and
possible dealbreakers
Provides a solid introduction to the latest version of TensorFlow, TF2.0, for students who are new to the area
Covers fundamental concepts of AI and deep learning, including topics like regression tasks, image processing, and natural language processing
Involves hands-on implementation using Python and Google Colab, providing students with practical experience
Introduces TensorBoard for visualizing models and assessing performance
Offers projects designed to address real-world challenges using AI techniques
Covers deployment techniques for trained models using TensorFlow Serving
Save this course
Save TensorFlow 2.0 Practical to your list so you can find it easily later:
Save
Save
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 TensorFlow 2.0 Practical with these
activities:
Read 'Deep Learning with Python'
Show steps
This book provides a comprehensive overview of deep learning concepts and techniques, complementing the materials covered in the course.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read and understand the chapters relevant to the course topics.
-
Take notes and highlight key concepts.
-
Attempt the exercises and coding examples provided in the book.
Solve TensorFlow exercises
Show steps
Solving TensorFlow exercises provides a low-stakes environment to practice and refine skills in using TensorFlow for machine learning.
Browse courses on
TensorFlow
Show steps
-
Find a resource or platform offering TensorFlow exercises.
-
Attempt to solve the exercises independently.
-
Review solutions and identify areas for improvement.
Practice solving machine learning coding problems
Show steps
Engaging in practice drills will reinforce the concepts and techniques learned in the course, improving problem-solving abilities.
Browse courses on
Machine Learning
Show steps
-
Identify a platform or resource offering machine learning coding problems.
-
Choose problems that align with the topics covered in the course.
-
Attempt to solve the problems independently.
-
Review solutions and identify areas for improvement.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Build a convolutional neural network using Keras
Show steps
Following tutorials on building convolutional neural networks using Keras will provide hands-on experience in implementing a common deep learning architecture.
Browse courses on
Keras
Show steps
-
Identify a suitable Keras tutorial on convolutional neural networks.
-
Follow the tutorial step-by-step, implementing the concepts in your own code.
-
Experiment with different datasets and models.
-
Evaluate the performance of your models.
Implement image classification using PyTorch
Show steps
Following tutorials on implementing image classification using PyTorch will complement and enhance the knowledge gained in the course.
Browse courses on
PyTorch
Show steps
-
Identify a suitable PyTorch tutorial on image classification.
-
Follow the tutorial step-by-step, implementing the concepts in your own code.
-
Experiment with different datasets and models.
-
Evaluate the performance of your models.
Attend a machine learning workshop
Show steps
Attending a workshop allows for exposure to industry experts and hands-on learning, supplementing the knowledge gained in the course.
Browse courses on
Machine Learning
Show steps
-
Identify and register for a machine learning workshop aligned with your interests.
-
Attend the workshop and actively participate in discussions and activities.
-
Network with other participants and professionals in the field.
Develop a machine learning project
Show steps
Creating a project allows for the practical application of skills and knowledge, solidifying understanding and fostering creativity.
Browse courses on
Machine Learning
Show steps
-
Identify a problem or use case that can be addressed using machine learning.
-
Gather and prepare the necessary data.
-
Choose and implement appropriate machine learning algorithms and models.
-
Train and evaluate the models.
-
Document and present the project outcomes.
Mentor a junior learner in machine learning
Show steps
Mentoring others not only benefits the mentee but also reinforces understanding and clarifies concepts for the mentor.
Browse courses on
Mentoring
Show steps
-
Identify a junior learner who could benefit from your guidance in machine learning.
-
Establish regular communication and provide support.
-
Share your knowledge, experience, and resources.
-
Encourage questions and provide constructive feedback.
Read 'Deep Learning with Python'
Show steps
This book provides a comprehensive overview of deep learning concepts and techniques, complementing the materials covered in the course.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read and understand the chapters relevant to the course topics.
- Take notes and highlight key concepts.
- Attempt the exercises and coding examples provided in the book.
Solve TensorFlow exercises
Show steps
Solving TensorFlow exercises provides a low-stakes environment to practice and refine skills in using TensorFlow for machine learning.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find a resource or platform offering TensorFlow exercises.
- Attempt to solve the exercises independently.
- Review solutions and identify areas for improvement.
Practice solving machine learning coding problems
Show steps
Engaging in practice drills will reinforce the concepts and techniques learned in the course, improving problem-solving abilities.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a platform or resource offering machine learning coding problems.
- Choose problems that align with the topics covered in the course.
- Attempt to solve the problems independently.
- Review solutions and identify areas for improvement.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Build a convolutional neural network using Keras
Show steps
Following tutorials on building convolutional neural networks using Keras will provide hands-on experience in implementing a common deep learning architecture.
Browse courses on
Keras
Show steps
Show steps
Show steps
- Identify a suitable Keras tutorial on convolutional neural networks.
- Follow the tutorial step-by-step, implementing the concepts in your own code.
- Experiment with different datasets and models.
- Evaluate the performance of your models.
Implement image classification using PyTorch
Show steps
Following tutorials on implementing image classification using PyTorch will complement and enhance the knowledge gained in the course.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Identify a suitable PyTorch tutorial on image classification.
- Follow the tutorial step-by-step, implementing the concepts in your own code.
- Experiment with different datasets and models.
- Evaluate the performance of your models.
Attend a machine learning workshop
Show steps
Attending a workshop allows for exposure to industry experts and hands-on learning, supplementing the knowledge gained in the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify and register for a machine learning workshop aligned with your interests.
- Attend the workshop and actively participate in discussions and activities.
- Network with other participants and professionals in the field.
Develop a machine learning project
Show steps
Creating a project allows for the practical application of skills and knowledge, solidifying understanding and fostering creativity.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a problem or use case that can be addressed using machine learning.
- Gather and prepare the necessary data.
- Choose and implement appropriate machine learning algorithms and models.
- Train and evaluate the models.
- Document and present the project outcomes.
Mentor a junior learner in machine learning
Show steps
Mentoring others not only benefits the mentee but also reinforces understanding and clarifies concepts for the mentor.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify a junior learner who could benefit from your guidance in machine learning.
- Establish regular communication and provide support.
- Share your knowledge, experience, and resources.
- Encourage questions and provide constructive feedback.
Career center
Learners who complete TensorFlow 2.0 Practical will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds and deploys machine learning models to solve real-world problems. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Machine Learning Engineer, such as machine learning, artificial intelligence, and deep learning. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, develops, and deploys artificial intelligence systems. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become an Artificial Intelligence Engineer, such as artificial intelligence, machine learning, and deep learning. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
Data Analyst
Data Analyst
A Data Analyst analyzes data to help businesses make better decisions. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Data Analyst, such as data analysis, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
Data Scientist
Data Scientist
A Data Scientist analyzes data to detect patterns and trends in order to help make business decisions. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Data Scientist, such as data analysis, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
Marketing Analyst
Marketing Analyst
A Marketing Analyst analyzes marketing data to help businesses make better decisions. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Marketing Analyst, such as marketing analysis, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst analyzes data to help businesses make better decisions. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Quantitative Analyst, such as quantitative analysis, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
Game Developer
Game Developer
A Game Developer designs, develops, and deploys video games. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Game Developer, such as game development, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
Robotics Engineer
Robotics Engineer
A Robotics Engineer designs, develops, and deploys robots. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Robotics Engineer, such as robotics, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
Research Scientist
Research Scientist
A Research Scientist conducts research in a variety of fields. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Research Scientist, such as research, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer designs, develops, and deploys computer vision systems. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Computer Vision Engineer, such as computer vision, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer designs, develops, and deploys natural language processing systems. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Natural Language Processing Engineer, such as natural language processing, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
Healthcare Analyst
Healthcare Analyst
A Healthcare Analyst analyzes healthcare data to help healthcare providers make better decisions. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Healthcare Analyst, such as healthcare analysis, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst analyzes data to help businesses make better decisions. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become an Operations Research Analyst, such as operations research, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and deploys software applications. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Software Engineer, such as software development, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to help businesses make better decisions. The TensorFlow 2.0 Practical course can help you build a foundation in the skills needed to become a Financial Analyst, such as financial analysis, machine learning, and artificial intelligence. By learning how to build and deploy machine learning models using TensorFlow 2.0, you can gain the skills needed to succeed in this field.
For more career information including salaries, visit:
OpenCourser.com/course/uw3w9r/tensorflow
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
TensorFlow 2.0 Practical.
Provides a comprehensive introduction to deep learning, covering the basics of neural networks, convolutional neural networks, recurrent neural networks, and more. It great resource for anyone who wants to learn more about deep learning and its applications.
Provides a practical introduction to machine learning, using Python and popular libraries like Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning.
Provides a collection of recipes for solving common problems in TensorFlow 2.0. It covers a wide range of topics, including data preprocessing, model training, and deployment.
Provides a collection of recipes for solving common problems in TensorFlow 2.0. It covers a wide range of topics, including data preprocessing, model training, and deployment.
Save
Is valuable for supplementing the course, especially for learners who need more exposure to Python as a programming language, software development in Python, and those who want a deeper understanding of machine learning. It offers a comprehensive and practical treatment of machine learning with TensorFlow 2.0, covering a wide range of topics including data preprocessing, model training, evaluation, and deployment.
Provides a comprehensive introduction to artificial neural networks and machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive introduction to deep learning for computer vision. It covers a wide range of topics, including image classification, object detection, and image segmentation.
Provides a comprehensive overview of machine learning. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of machine learning.
Save
Provides a comprehensive introduction to artificial intelligence using Python. It valuable resource for learners who want to gain a broad understanding of the field of artificial intelligence.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uw3w9r/tensorflow
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Effort
12 total hours
Via
Udemy
Instructors
Dr. Ryan Ahmed, Ph.D., MBA
SuperDataScience Team
Mitchell Bouchard
Ligency Team
Language
English
Good to know
Provides a solid introduction to the latest version of TensorFlow, TF2.0, for students who are new to the area
Covers fundamental concepts of AI and deep learning, including topics like regression tasks, image processing, and natural language processing
Involves hands-on implementation using Python and Google Colab, providing students with practical experience
Introduces TensorBoard for visualizing models and assessing performance
Offers projects designed to address real-world challenges using AI techniques
Covers deployment techniques for trained models using TensorFlow Serving
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