Download Udemy – TensorFlow 2.0 Practical 2022-1

Description

TensorFlow 2.0 Practical course. The artificial intelligence (AI) revolution has arrived and TensorFlow 2.0 is finally here to make it much faster! TensorFlow 2.0 is Google’s most powerful open source platform recently released for building and deploying AI models in action. Artificial intelligence technology is experiencing exponential growth and is widely used in healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide practical knowledge to students in the field of construction, training, testing and deployment of artificial neural networks and deep learning models using TensorFlow 2.0 and Google Colab. This course provides students with hands-on hands-on experience in teaching artificial neural networks and convolutional neural networks using real-world datasets using TensorFlow 2.0 and Google Colab. This course covers several techniques in a hands-on, projects including but not limited to:

(1) feed forward artificial neural networks to perform regression tasks such as sales/revenue forecasting and house price forecasting;

(ii) development of artificial neural networks in the medical field to perform classification tasks such as diabetes diagnosis;

(3) training deep learning models to perform image classification tasks such as face recognition, fashion classification, and traffic sign classification.

(4) Create artificial intelligence models to perform sentiment analysis and analyze customer reviews.

(5) Visualize AI models and evaluate their performance using Tensorboard

(6) Implement AI models in practice using the Tensorflow 2.0 service

This course is aimed at students who want to gain a basic understanding of how to build and deploy models in Tensorflow 2.0. Basic knowledge of programming is recommended. However, these topics will be covered extensively during the introductory course lectures. Therefore, this course has no prerequisites and is open to any student who has basic knowledge of programming. Students enrolled in this course will master artificial intelligence and deep learning techniques and can directly apply these skills to solve challenging real-world problems using Google’s new TensorFlow 2.0.

What you will learn in the TensorFlow 2.0 Practical course

Master the newly released TensorFlow 2.0 to build, train, test, and deploy artificial neural network (ANN) models.
Learn how to develop and train ANN models in Google’s Colab while harnessing the power of GPUs and TPUs.
Run ANN models in practice using the TensorFlow 2.0 service.
Learn how to graph models and evaluate their performance during practice using Tensorboard.
Understand the theory and basic mathematics behind artificial neural networks and convolutional neural networks (CNN).
Learn how to train network weights and biases and choose appropriate transfer functions.
Training artificial neural networks (ANN) using back propagation and gradient descent methods.
Optimizing the parameters of artificial neural networks such as the number of hidden layers and neurons to increase the performance of the network.
Use artificial neural networks to perform regression tasks such as housing price forecasting and sales/revenue forecasting.
Evaluate the performance of trained ANN models for regression tasks using KPIs (Key Performance Indicators) such as Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error, R-Squared, and Adjusted R-Squared.
Evaluate the performance of trained ANN models for classification tasks using KPIs such as accuracy, precision, and recall.
Use convolutional neural networks to classify images.
Examples of real and practical projects:
Project #1: Training a simple ANN to convert Celsius to Fahrenheit
Project #2 (Exercise): Training ANN Feedforward for Revenue/Sales Forecasting
Project #3: As a real estate agent, predict house prices using ANN (regression function).
Project #4 (Exercise): As a business owner, predict bicycle usage (regression task)
Project No. 5: Development of artificial neural networks in the medical field to perform classification tasks such as diabetes diagnosis (classification task)
Project #6: Create AI models to perform sentiment analysis and analyze online customer reviews.
Project No. 7: Training LeNet deep learning models to perform traffic sign classification.
Project #8: Train CNN to perform fashion classification
Project #9: Train a CNN to perform image classification using the Cifar-10 dataset
Project #10: Implement deep learning image classification model using TF service