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