TensorFlow 2.0 Practical

Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 10 practical projects

Created by: Dr. Ryan Ahmed, Ph.D., MBA
Best-Selling Professor, 400K+ students, 250K+ YT Subs

Created by: SuperDataScience Team
Helping Data Scientists Succeed

Created by: Ligency Team
Helping Data Scientists Succeed

What Will I Learn?

• 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

Requirements

• PC with internet connection

Target audience

• Data Scientists who want to apply their knowledge on Real World Case Studies
• AI Developers
• AI Researchers