THE ULTIMATE TENSORFLOW 2.0 PRACTICAL COURSE

THE ULTIMATE TENSORFLOW 2.0 PRACTICAL COURSE, Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 10 practical projects

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Created by Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Mitchell Bouchard

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What you'll 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