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TENSORFLOW 2.0 PRACTICAL

TENSORFLOW 2.0 PRACTICAL

TENSORFLOW 2.0 PRACTICAL
TENSORFLOW 2.0 PRACTICAL, Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 10 practical projects
HOT & NEW
Created by Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Mitchell Bouchard
English
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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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