TensorFlow 2.0 Practical

✓ 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