TENSORFLOW 2.0 PRACTICAL, 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
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