TensorFlow 2.0 Practical

TensorFlow 2.0 Practical, available at $89.99, has an average rating of 4.74, with 91 lectures, based on 867 reviews, and has 7947 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Scientists who want to apply their knowledge on Real World Case Studies or AI Developers or AI Researchers It is particularly useful for Data Scientists who want to apply their knowledge on Real World Case Studies or AI Developers or AI Researchers.

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Summary

Title: TensorFlow 2.0 Practical

Price: $89.99

Average Rating: 4.74

Number of Lectures: 91

Number of Published Lectures: 85

Number of Curriculum Items: 91

Number of Published Curriculum Objects: 85

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

• Data Scientists who want to apply their knowledge on Real World Case Studies
• AI Developers
• AI Researchers

Target Audiences

• Data Scientists who want to apply their knowledge on Real World Case Studies
• AI Developers
• AI Researchers