TensorFlow (10 credits)


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This course aims to provide a practical approach to machine learning and deep learning using TensorFlow. TensorFlow is an open-source software library created by the Google Brain to make the computing load easier and faster for machine/deep learning applications. This course brings the student hands-on experience building his/her own predictive models, state-of-the-art image classifiers, and deep neural networks using TensorFlow and Keras. Furthermore, the student will gain hands-on experience in designing, training, and evaluating variations of neural networks, and learn the techniques required for working with large real-world datasets.

Learning outcomes

  • Get familiar with TensorFlow and Keras, and their capabilities
  • Learn how to set up and get started with TensorFlow in the Google Colab environment
  • Gain hands-on experience building and training neural network models with Keras using Sequential and Functional APIs
  • Gain hands-on experience evaluating neural networks and making predictions using them
  • Learn how to implement callbacks in TensorFlow
  • Get familiar with L1 and L2 regularizations and how to employ them to avoid overfitting
  • Learn how to use early stopping, dropout, and batch normalization techniques to avoid overfitting
  • Get familiar with convolutional neural networks (CNNs), different layers of CNN, and popular CNN architectures
  • Gain hands-on experience implementing CNN in TensorFlow for computer vision tasks
  • Learn how to analyze the performance of CNN after training
  • Understand the concept of transfer learning and how transfer learning models are made
  • Learn how to implement transfer learning models in TensorFlow
  • Get familiar with TensorFlow Hub and how to use it for transfer learning applications

Table of contents

Chapter 1: Beginning with This Course

  • 1.1. Our Approach in This Course
  • 1.2. About TechClass AI Department
  • 1.3. Your Expectations, Goals, and Knowledge

Chapter 2: Introduction

  • 2.1. What is TensorFlow?
  • 2.2. TensorFlow 1.x vs. TensorFlow 2.x
  • 2.3. Getting Started with Google Colab
  • 2.4. Setting up TensorFlow

Chapter 3: Python Overview

  • 3.1. Variables and Operators
  • 3.2. Data Structures
  • 3.3. Conditional Statements
  • 3.4. Loops
  • 3.5. Exercise

Chapter 4: Building Models using Keras

  • 4.1. What is Keras?
  • 4.2. Machine Learning Models in General
  • 4.3. Basic Neural Network Sequential Model
  • 4.4. Fitting, Evaluation, and Prediction
  • 4.5. Functional API Models
  • 4.6. Handwritten Digit Recognition using Neural Network
  • 4.7. Callbacks

Chapter 5: Convolutional Neural Networks

  • 5.1. Why CNN?
  • 5.2. CNN Layers
  • 5.3. Typical CNN Architecture
  • 5.4. Implementing CNN in TensorFlow: Sequential API
  • 5.5. Implementing CNN in TensorFlow: Functional API
  • 5.6. Popular CNN Architectures for Computer Vision
  • 5.7. Training and Evaluating CNN for CIFAR10 Dataset

Chapter 6: Handling Overfitting

  • 6.1. What is Overfitting?
  • 6.2. Regularization: Basics
  • 6.3. L1 and L2 Regularizations
  • 6.4. Quiz
  • 6.5. Early Stopping
  • 6.6. Dropout
  • 6.7. Batch Normalization

Chapter 7: Saving and Loading Models

  • 7.1. Introduction
  • 7.2. Whole-model Saving
  • 7.3. Whole-model Loading
  • 7.4. Saving the Architecture
  • 7.5. Loading the Architecture
  • 7.6. Saving Model Weights
  • 7.7. Loading Model Weights

Chapter 8: Transfer Learning

  • 8.1. What is Transfer Learning?
  • 8.2. How to Use Transfer Learning?
  • 8.3. Loading Pre-trained Models in Keras
  • 8.4. Pre-trained Model as Classifier
  • 8.5. Pre-trained Model as Standalone Feature Extractor
  • 8.6. Pre-trained Model as Integrated Feature Extractor
  • 8.7. TensorFlow Hub

Chapter 9: Final Tasks

  • 9.1. Project
  • 9.2. Self-study Essay
  • 9.3. Congrats! You did it!

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