TensorFlow 2.0 Practical (Udemy.com)
Master Tensorflow 2.0, Googles most powerful Machine Learning Library, with 10 practical projects
Created by: Dr. Ryan Ahmed, Ph.D., MBA
Produced in 2021
What you will learn
- Master Googles 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 Googles 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 p
Quality Score
Overall Score : 88 / 100
Course Description
0 is finally here to make it happen much faster! TensorFlow 2.
0 is Googles 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 ServingThe 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 Googles New TensorFlow 2.
0.
Who this course is for:
Data Scientists who want to apply their knowledge on Real World Case StudiesAI DevelopersAI Researchers
Instructor Details
- 4.4 Rating
81 Reviews
Dr. Ryan Ahmed, Ph.D., MBA
Ryan Ahmed is abest-selling Udemy instructorwhois passionate about education and technology. Ryan's mission is to make quality education accessible and affordable to everyone.Ryanholds a Ph.D.degree in Mechanical Engineeringfrom McMaster*University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Masters of Applied Sciencedegree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and anMBA in Finance from the DeGroote School of Business.
Ryanheld several engineering positions at Fortune 500 companies globally such as SamsungAmericaand Fiat-Chrysler Automobiles (FCA) Canada.Ryan has taught several courses on Science, Technology, Engineering and Mathematics to over 200,000+ students globally.He has over 15 published journal and conference research papers on state estimation, AI, Machine learning, battery modeling and EVcontrols. Heisthe co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA.
Ryan isaStanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is alsothe programCo-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC17) in Chicago, IL, USA.
* McMaster University is one of only four Canadian universities consistently rankedin the to
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- Video Duration:
11.5h - Price: 11.99 Track Price 11.99 Track Price
- Provider: Udemy
- Year:
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