TensorFlow Fundamentals: From Basics to Brilliant AI Project

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AI Sciences,AI Sciences Team

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1. Introduction

1. Introduction to Instructor.mp4

02:15

2. Course Outline.mp4

02:06

3. Your Honest Review.mp4

01:18

4. Links for the Courses Materials and Codes.html

2. Introduction to TensorFlow

1. Links for the Courses Materials and Codes.html

2. Module Introduction.mp4

01:39

3. TensorFlow Definition and Properties.mp4

03:56

4. Tensor Types and Tesnor Board.mp4

03:38

5. How to use TensorFlow.mp4

02:30

6. Google Colab.mp4

01:31

7. Exercise.mp4

02:20

8. Exercise Solution.mp4

14:54

9. Quiz.mp4

02:49

10. Quiz Solution.mp4

12:37

11. What is TensorFlow.html

12. What is Tensorboard.html

13. What is the purpose of TensorFlow Serving.html

14. What is a Tensor in TensorFlow.html

15. How do you run a TensorFlow operation in a session.html

16. Which of the following statements about tf.SparseTensor is true.html

17. Which of the Following Statements is True about tf.placeholder.html

18. Which of the Following is NOT a Valid Data Type in TensorFlow.html

19. Which of the Following Statements is True about TensorFlow.html

20. Which of the Following Components of a TensorFlow Tensor is an Optional Label that can be used to Identify the Tensor.html

21. What is Google Colab.html

22. What is the Purpose of Importing TensorFlow as tf in a Python Script.html

3. 03-Building your first deep learning Project

1. Links for the Courses Materials and Codes.html

2. Module Introduction.mp4

01:51

3. ANNs.mp4

04:31

4. TensorFlow Playground.mp4

04:02

5. Load TF and Data.mp4

02:25

6. Model Training and Evaluation.mp4

03:40

7. Project.mp4

02:13

8. Project Implementation.mp4

16:16

9. Quiz.mp4

01:15

10. Quiz Solution.mp4

06:33

11. What does ANN Stand for.html

12. What is the Output of a Single Neuron in an ANN.html

13. What is TensorFlow Playground.html

14. How are ANNs Related to the Human Brain.html

15. What does the Formula T(F) = T(C)95+32 Represent.html

16. What is the Purpose of the Data Tab in TF Playground.html

17. What types of Machine Learning Models can be Trained in TF Playground.html

18. What is the most Common Type of File Format used with Pandas Read Function.html

19. What happens if the File Specified in the Pandas Read Function does not Exist.html

20. Which of the Following is NOT a Step involved in Reading in a File with Pandas Read Function.html

21. When Querying the Model for a Prediction, what Input is Typically Provided.html

22. What are some Features of TensorFlow.html

4. 04-Multi-layer Deep Learning Project

1. Links for the Courses Materials and Codes.html

2. Module Introduction.mp4

03:27

3. Training and Epochs.mp4

07:01

4. Gradient Decent and Back Propagation.mp4

04:43

5. Bias Variance Trade-Off.mp4

03:37

6. Performance Metrics.mp4

06:57

7. Project-Sales Predition.mp4

20:23

8. Quiz.mp4

01:58

9. Quiz Solution.mp4

13:43

10. Which of the Following is used to update the Weights of a Neural Network during Training.html

11. Which of the Following Represents the Number of Times a Machine Learning Model sees the Entire Training Dataset.html

12. Which of the Following is Responsible for Propagating the Error Back through the Neural Network during Training.html

14. Which of the Following is used to Evaluate the Performance of a Machine Learning Model.html

15. Which of the Following is used to add a Constant value to the Output of a Neuron in a Neural Network.html

16. What is the Purpose of Evaluating the Output of a Supervised Learning Algorithm.html

17. Which of the Following Performance Metrics is used to Measure how well a Regression Model Fits the Data.html

18. What is the Purpose of Determining the Residual for each Data Point.html

19. What is the Process of Backpropagation.html

20. What is the Role of the Gradient Descent in Backpropagation.html

21. How does the Network Learn in Reinforcement Learning.html

Description

Deep Learning: From Neuron to Advanced Deep Learning Projects: Master the Core Concepts of TensorFlow & Deep Learning

What You'll Learn?

• A Solid Grasp of TensorFlow Basics
• Hands-on Experience in Building Deep Learning Models
• Knowledge of Model Training, Evaluation, and Optimization
• The Ability to Translate Concepts into Practical Code
• Confidence to Explore More Complex AI and Machine Learning Projects

Who is this for?

• Aspiring Data Scientists

• Machine Learning Enthusiasts

• Developers Interested in AI

• Anyone Curious About Deep Learning and TensorFlow

What You Need to Know?

No prior experience with TensorFlow is required, but a basic understanding of machine learning concepts and Python will be helpful.

More details

Description
Course Description:
Welcome to the world of deep learning with TensorFlow! In this comprehensive course, "Fundamentals of TensorFlow," you'll embark on a journey through the essentials of one of the most powerful and widely used open-source libraries for machine learning and neural networks. Whether you're an aspiring data scientist, a developer, or someone intrigued by the world of artificial intelligence, this course will equip you with the knowledge and skills to harness the capabilities of TensorFlow effectively.
Module 1: Course Overview In this module, we'll lay the foundation for your learning journey by providing you with an overview of the course and what you can expect to achieve by the end of it. You'll be introduced to the structure of the course, the topics covered in each module, and the learning outcomes.
Module 2: Introduction to TensorFlow Here, we delve into the core concepts of TensorFlow. You'll gain a solid understanding of what TensorFlow is and how it serves as a backbone for developing machine learning and deep learning models. We'll explore the various products offered by TensorFlow and demystify the concept of tensorboard, an essential tool for visualizing model performance.
Module 3: Building Your First Deep Learning Project Get ready to dive into hands-on implementation! In this module, we start by building your first deep learning project using TensorFlow. You'll begin with the basics by creating a single neuron implementation, followed by the exploration of artificial neural networks (ANNs). Through TensorFlow Playground, you'll experiment with model configurations and see how they impact learning. We'll cover the entire pipeline, from loading data to model training and evaluation, ensuring you're well-versed in every step of the process.
Module 4: Multi-layer Deep Learning Project Take your skills to the next level as we delve deeper into the intricacies of multi-layer deep learning projects. You'll learn about training and epochs, the critical role of gradient descent, and the mechanics of backpropagation. We'll discuss the bias-variance tradeoff and explore performance metrics that help you assess your models effectively. The coding part of this module will empower you to put theory into practice and build a multi-layer deep learning project from scratch.
By the end of this course, you'll have not only a theoretical understanding but also the practical experience of building and evaluating deep learning models using TensorFlow. Whether you're aiming to enhance your data science expertise, boost your machine learning skills, or expand your AI capabilities, "Fundamentals of TensorFlow" on Skillshare is your gateway to mastery in this exciting field.
Who Should Enroll:
Aspiring Data Scientists
Machine Learning Enthusiasts
Developers Interested in AI
Anyone Curious About Deep Learning and TensorFlow
Prerequisites: No prior experience with TensorFlow is required, but a basic understanding of machine learning concepts and Python will be helpful.
What You'll Gain:
A Solid Grasp of TensorFlow Basics
Hands-on Experience in Building Deep Learning Models
Knowledge of Model Training, Evaluation, and Optimization
The Ability to Translate Concepts into Practical Code
Confidence to Explore More Complex AI and Machine Learning Projects
Don't miss out on this opportunity to unlock the power of TensorFlow and step into the world of deep learning. Enroll now and start your journey towards becoming a proficient TensorFlow practitioner!
Who this course is for:
â€¢ Aspiring Data Scientists
â€¢ Machine Learning Enthusiasts
â€¢ Developers Interested in AI
â€¢ Anyone Curious About Deep Learning and TensorFlow

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We are a group of experts, PhDs and Practitioners of Artificial Intelligence, Computer Science, Machine Learning, and Statistics. Some of us work in big companies like Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.We decided to produce a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of Machine Learning, Statistics, Artificial Intelligence, and Data Science. Initially, our objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory and without a long reading. Today we also publish a more complete course on some topics for a wider audience.Our courses have had phenomenal success. Our Courses have helped more than 100,000 students to master AI and Data Science.

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