Machine Learning Projects with TensorFlow 2.0

Video description

Build and train models for real-world machine learning projects using Tensorflow 2.0

About This Video

  • Make use of the amazing new feature of TensorFlow 2 called 'Eager Execution' which makes it easier to learn and use
  • Upgrade your skills by building real-world Machine Learning projects
  • Build, test and deploy different ML models and learn more modern techniques such as Reinforcement Learning and Transfer Learning

In Detail

TensorFlow is the world's most widely adopted framework for Machine Learning and Deep Learning. TensorFlow 2.0 is a major milestone due to its inclusion of some major changes making TensorFlow easier to learn and use such as "Eager Execution". It will support more platforms and languages, improved compatibility and remove deprecated APIs.

This course will guide you to upgrade your skills in Machine Learning by practically applying them by building real-world Machine Learning projects.

Each section should cover a specific project on a Machine Learning task and you will learn how to implement it into your system using TensorFlow 2. You will implement various Machine Learning techniques and algorithms using the TensorFlow 2 library. Each project will put your skills to test, help you understand and overcome the challenges you can face in a real-world scenario and provide some tips and tricks to help you become more efficient. Throughout the course, you will cover the new features of TensorFlow 2 such as Eager Execution. You will cover at least 3-4 projects. You will also cover some tasks such as Reinforcement Learning and Transfer Learning.

By the end of the course, you will be confident to build your own Machine Learning Systems with TensorFlow 2 and will be able to add this valuable skill to your CV.

Audience

This course is for developers, data scientists and ML engineers who now want to enhance their skill set in Machine Learning using TensorFlow by building real-world projects.

This course will appeal to someone who has a basic understanding of ML concepts, Python and TensorFlow.

Publisher resources

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Table of contents

  1. Chapter 1 : Regression Task Airbnb Prices in New York
    1. Course Overview
    2. Setting Up TensorFlow 2.0
    3. Getting Started with TensorFlow 2.0
    4. Analyzing the Airbnb Dataset and Making a Plan
    5. Implementing a Simple Linear Regression Algorithm
    6. Implementing a Multi Layer Perceptron (Artificial Neural Network)
    7. Improving the Network with Better Activation Functions and Dropout
    8. Adding More Metrics to Gain a Better Understanding
    9. Putting It All Together in a Professional Way
  2. Chapter 2 : Classification Task Build Real World Apps: Who Will Win the Next UFC?
    1. Collecting Possible Kaggle Data
    2. Analysis and Planning of the Dataset
    3. Introduction to Google Colab and How It Benefits Us
    4. Setting Up Training on Google Colab
    5. Some Advanced Neural Network Approaches
    6. Introducing a Deeper Network
    7. Inspecting Metrics with TensorBoard
    8. Inspecting the Existing Kaggle Solutions
  3. Chapter 3 : Natural Language Processing Task: How to Generate Our Own Text
    1. Introduction to Natural Language Processing
    2. NLP and the Importance of Data Preprocessing
    3. A Simple Text Classifier
    4. Text Generation Methods
    5. Text Generation with a Recurrent Neural Network
    6. Refinements with Federated Learning
  4. Chapter 4 : Reinforcement Learning Task: How to Become Best at Pacman
    1. Introduction to Reinforcement Learning
    2. OpenAI Gym Environments
    3. The Pacman Gym Environment That We Are Going to Use
    4. Reinforcement Learning Principles with TF-Agents
    5. TF-Agents for Our Pacman Gym Environment
    6. The Agents That We Are Going to Use
    7. Selecting the Best Approaches and Real World Applications
  5. Chapter 5 : Transfer Learning Task: How to Build a Powerful Image Classifier
    1. Introduction to Transfer Learning in TensorFlow 2
    2. Picking a Kaggle Dataset to Work On
    3. Picking a Base Model Suitable for Transfer Learning with Our Dataset
    4. Implementing our Transfer Learning approach
    5. How Well Are We Doing and Can We Do Better
    6. Conclusions and Future Work

Product information

  • Title: Machine Learning Projects with TensorFlow 2.0
  • Author(s): Vlad Sebastian Ionescu
  • Release date: April 2020
  • Publisher(s): Packt Publishing
  • ISBN: 9781838980252