Machine Learning Projects with TensorFlow 2.0

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Dr. Vlad Sebastian Ionescu

4:20:08

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1.Regression Task Airbnb Prices in New York

01.Course Overview.mp4

06:08

02.Setting Up TensorFlow 2.0.mp4

03:16

03.Getting Started with TensorFlow 2.0.mp4

08:55

04.Analyzing the Airbnb Dataset and Making a Plan.mp4

03:50

05.Implementing a Simple Linear Regression Algorithm.mp4

13:04

06.Implementing a Multi Layer Perceptron (Artificial Neural Network).mp4

11:38

07.Improving the Network with Better Activation Functions and Dropout.mp4

05:41

08.Adding More Metrics to Gain a Better Understanding.mp4

05:10

09.Putting It All Together in a Professional Way.mp4

07:08

2.Classification Task Build Real World Apps - Who Will Win the Next UFC

10.Collecting Possible Kaggle Data.mp4

04:08

11.Analysis and Planning of the Dataset.mp4

05:03

12.Introduction to Google Colab and How It Benefits Us.mp4

09:28

13.Setting Up Training on Google Colab.mp4

09:56

14.Some Advanced Neural Network Approaches.mp4

15:46

15.Introducing a Deeper Network.mp4

05:30

16.Inspecting Metrics with TensorBoard.mp4

09:56

17.Inspecting the Existing Kaggle Solutions.mp4

03:07

3.Natural Language Processing Task - How to Generate Our Own Text

18.Introduction to Natural Language Processing.mp4

02:22

19.NLP and the Importance of Data Preprocessing.mp4

07:41

20.A Simple Text Classifier.mp4

06:43

21.Text Generation Methods.mp4

01:35

22.Text Generation with a Recurrent Neural Network.mp4

13:44

23.Refinements with Federated Learning.mp4

08:59

4.Reinforcement Learning Task - How to Become Best at Pacman

24.Introduction to Reinforcement Learning.mp4

02:54

25.OpenAI Gym Environments.mp4

09:31

26.The Pacman Gym Environment That We Are Going to Use.mp4

06:07

27.Reinforcement Learning Principles with TF-Agents.mp4

01:47

28.TF-Agents for Our Pacman Gym Environment.mp4

12:49

29.The Agents That We Are Going to Use.mp4

02:30

30.Selecting the Best Approaches and Real World Applications.mp4

10:38

5.Transfer Learning Task - How to Build a Powerful Image Classifier

31.Introduction to Transfer Learning in TensorFlow 2.mp4

02:28

32.Picking a Kaggle Dataset to Work On.mp4

11:40

33.Picking a Base Model Suitable for Transfer Learning with Our Dataset.mp4

08:05

34.Implementing our Transfer Learning approach.mp4

07:24

35.How Well Are We Doing and Can We Do Better.mp4

13:44

36.Conclusions and Future Work.mp4

01:43

Description

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. Codefiles are uploaded here: https://github.com/PacktPublishing/Machine-Learning-Projects-with-TensorFlow-2.0

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Machine Learning

TensorFlow

Dr. Vlad Sebastian Ionescu

Instructor's Courses

Vlad is a university lecturer with a Ph.D. in Machine Learning and a freelance Software Engineer. He has over 10 years of computer science teaching experience in various roles: school teacher, private tutor, internship mentor, university TA, and lecturer. Over the years, Vlad has worked with most cutting-edge technologies in areas such as frontend development, database design and administration, backend programming, and machine learning

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.