Overview What You Will Learn Curriculum Instructor

Course Overview

TensorFlow is a trendy free and open-source machine learning framework developed by Google. To use the power of deep learning in your application, you as a developer can use TensorFlow to bring Artificial Intelligence(AI) features to the application. These features could be natural language processing, Convolutional Neural Networks (CNN) for image processing, prediction of continuous variables, and other machine learning techniques. 

This course will teach you to build different AI models with specific goals and enhance your knowledge of AI. We will practice the basics, including installation, building, training, and evaluating our simple TensorFlow model. 

By the end of the course, you will be well-versed in TensorFlow. You will learn how to build and train deep learning models and validate models followed through an image processing project.

What You Will Learn

  • Understand what are Tensors
  • How to Install TensorFlow 2
  • Get equipped with the Basics of TensorFlow and Basic Computations
  • How to develop a model structure
  • Create custom models and importing datasets
  • How to build
  • train
  • and evaluate the ANN Model
  • Transfer learning and fine tuning
  • Visualize and Save Models

Program Curriculum

  • Starting with TensorFlow
  • TensorFlow 1.x vs. TensorFlow 2.x
  • Introduction to Google Colab
  • TensorFlow Documentation
  • TensorFlow Installation
  • $7 Million Cybersecurity Scholarship by EC-Council
  • Chapter 1 Quiz

  • The Keras API
  • The Dataset Class in TensorFlow
  • Deep Learning Layers in TensorFlow
  • Building Neural Networks Using the Sequential Class
  • Building Neural Networks Using the Functional Approach
  • Compiling a Model Using a Loss Function and an Optimizer
  • Training a Model in TensorFlow
  • Chapter 2 Quiz

  • Model Validation
  • Making Predictions with Your Model
  • Model Regularization
  • Batch Normalization
  • Callbacks
  • Chapter 3 Quiz

  • Components of a TensorFlow Model
  • Saving and Loading Models Using Callbacks
  • Saving and Loading Model Weights Using save_weights Method
  • Saving and Loading Entire TensorFlow Models Manually
  • Different Model Formats in TensorFlow
  • Chapter4 Quiz

  • Problem Statement
  • Dataset Used and How to Get It
  • Exploring the Dataset
  • Accessing the Dataset from Your Colab Notebook
  • Building Data Pipelines Using Generators
  • Creating the Model’s Architecture
  • Compiling the Model and Fitting the Data to It
  • Adding Some Useful Callbacks
  • Running the Training and Dealing with Potential Problems
  • Evaluating the Model
  • How to Make Predictions on a Single Image?
  • Potential Improvements
  • Chapter 5 Quiz
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Instructor

Nour Islam Mokhtari

Nour Mokhtari is a machine learning engineer with a focus on computer vision applications. He has experience in developing and maintaining deep learning pipelines. He has worked on several artificial intelligence projects, mostly focused on applying deep learning research to real-world industry projects. He has also created video courses on Udemy. His goal is to help his students learn and acquire real-world and industry-focused experience. He aims to build courses that can make your learning experience smooth and focused on the practical aspects of things.

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