This course was last updated on 2/2021.
We analyzed factors such as the rating (4.4/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.
✓ 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
• PC with internet connection
• Data Scientists who want to apply their knowledge on Real World Case Studies
• AI Developers
• AI Researchers
The course costs $17.99. And currently there is a 82% discount on the original price of the course, which was $99.99. So you save $82 if you enroll the course now.
YES, TensorFlow 2.0 Practical has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.
Dr. Ryan Ahmed, Ph.D., MBA has created 44 courses that got 27,789 reviews which are generally positive. Dr. Ryan Ahmed, Ph.D., MBA has taught 295,730 students and received a 4.6 average review out of 27,789 reviews. Depending on the information available, we think that Dr. Ryan Ahmed, Ph.D., MBA is an instructor that you can trust.
Professor & Best-selling Instructor, 250K+ students
Ryan Ahmed is a best-selling Udemy instructor who is passionate about education and technology. Ryan’s mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master’s of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business.
Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. Ryan has taught several courses on Science, Technology, Engineering and Mathematics to over 280,000+ students globally. He has over 25 published journal and conference research papers on state estimation, AI, Machine learning, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA.
Ryan is a Stanford 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 also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA.
* McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world.
