TensorFlow 2.0 Practical

What you'll learn :

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Master Google’s newly released TensorFlow 2.0 to build, train, test and deploy Artificial Neural Networks (ANNs) models. Deploy ANNs models in practice using TensorFlow 2.0 Serving. Understand the underlying theory and mathematics behind Artificial Neural Networks and Convolutional Neural Networks (CNNs). Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods. Apply ANNs to perform regression tasks such as house prices predictions and sales/revenue predictions. Assess the performance of trained ANN models for classification tasks using KPI such as accuracy, precision and recall. Sample real-world, practical projects: Project #2 (Exercise): Train Feedforward ANN to predict Revenue/sales Project #4 (Exercise): As a business owner, predict Bike rental usage (Regression Task) Project #6: Develop AI models to perform sentiment analysis and analyze online customer reviews. Project #8: Train CNN to perform fashion classification Project #10: Deploy deep learning image classification model using TF serving Learn how to develop ANNs models and train them in Google’s Colab while leveraging the power of GPUs and TPUs. Learn how to visualize models graph and assess their performance during training using Tensorboard. Learn how to train network weights and biases and select the proper transfer functions. Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance. 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. Apply Convolutional Neural Networks to classify images. Project #1: Train Simple ANN to convert Celsius temperature reading to Fahrenheit Project #3: As a real-estate consultant, predict house prices using ANNs (Regression Task) Project #5: Develop Artificial Neural Networks in the medical field to perform classification tasks such as diabetes detection (Classification task) Project #7: Train LeNet Deep Learning models to perform traffic signs classification. Project #9: Train CNN to perform image classification using Cifar-10 dataset