Become a TensorFlow Certified Professional Developer

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Ligency Team,TFC Course

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1. Part 0 Introduction To The Course

1. Introduction to the Course.mp4

04:30

2. Contact and Questions.html

2. Part 1 Artificial Neural Networks

1. Intro.mp4

01:33

2. Get course materials.html

3. Plan of Attack.mp4

02:51

4. Functioning of the Human Neuron.mp4

16:15

5. How Neural Networks Work.mp4

12:47

6. Activation Function.mp4

08:29

7. How Neural Networks Learn.mp4

12:58

8. Gradient Descent.mp4

10:12

9. Stochastic Gradient Descent.mp4

08:44

10. Back-Propagation.mp4

05:21

11. Build an ANN with TensorFlow in 5 Steps From Scratch - Step 1.mp4

10:21

12. Build an ANN with TensorFlow in 5 Steps From Scratch - Step 2.mp4

18:36

13. Build an ANN with TensorFlow in 5 Steps From Scratch - Step 3.mp4

14:28

14. Build an ANN with TensorFlow in 5 Steps From Scratch - Step 4.mp4

11:58

15. Build an ANN with TensorFlow in 5 Steps From Scratch - Step 5.mp4

16:25

3. Part 2 Convolutional Neural Networks

1. Intro.mp4

01:17

2. Plan of Attack.mp4

03:31

3. What are Convolutional Neural Networks.mp4

15:49

4. Step 1 The Convolution Operation.mp4

16:38

5. Step 1 (Part B) ReLU Layer.mp4

06:41

6. Step 2 Pooling.mp4

14:13

7. Step 3 Flattening.mp4

01:52

8. Step 4 Full Connection.mp4

19:24

9. Summary.mp4

04:19

10. Softmax Activation Function & Cross-Entropy Loss Function.mp4

18:20

11. Build a CNN with TensorFlow in 5 Steps From Scratch - Step 1.mp4

11:35

12. Build a CNN with TensorFlow in 5 Steps From Scratch - Step 2.mp4

17:46

13. Build a CNN with TensorFlow in 5 Steps From Scratch - Step 3.mp4

17:56

14. Build a CNN with TensorFlow in 5 Steps From Scratch - Step 4.mp4

07:21

15. Build a CNN with TensorFlow in 5 Steps From Scratch - Step 5.mp4

14:55

16. Demo.mp4

23:38

4. Part 3 Recurrent Neural Networks

1. Intro.mp4

01:22

2. Plan of Attack.mp4

02:32

3. Recurrent Neural Networks.mp4

16:01

4. Vanishing Gradient Problem.mp4

14:27

5. LSTMs and How They Work.mp4

19:47

6. Practical Intuition.mp4

15:11

7. LSTM Variations.mp4

03:36

8. Build a RNN with TensorFlow in 15 steps from scratch - Step 1.mp4

06:29

9. Build a RNN with TensorFlow in 15 steps from scratch - Step 2.mp4

07:04

10. Build a RNN with TensorFlow in 15 steps from scratch - Step 3.mp4

05:57

11. Build a RNN with TensorFlow in 15 steps from scratch - Step 4.mp4

14:23

12. Build a RNN with TensorFlow in 15 steps from scratch - Step 5.mp4

10:40

13. Build a RNN with TensorFlow in 15 steps from scratch - Step 6.mp4

02:50

14. Build a RNN with TensorFlow in 15 steps from scratch - Step 7.mp4

08:42

15. Build a RNN with TensorFlow in 15 steps from scratch - Step 8.mp4

05:20

16. Build a RNN with TensorFlow in 15 steps from scratch - Step 9.mp4

03:20

17. Build a RNN with TensorFlow in 15 steps from scratch - Step 10.mp4

04:21

18. Build a RNN with TensorFlow in 15 steps from scratch - Step 11.mp4

10:31

19. Build a RNN with TensorFlow in 15 steps from scratch - Step 12.mp4

05:22

20. Build a RNN with TensorFlow in 15 steps from scratch - Step 13.mp4

16:50

21. Build a RNN with TensorFlow in 15 steps from scratch - Step 14.mp4

08:15

22. Build a RNN with TensorFlow in 15 steps from scratch - Step 15.mp4

09:36

5. Part 4 Intro to Computer Vision

1. Intro.mp4

01:23

2. Introduction to Computer Vision.mp4

06:06

3. Code to Load Training Data For a Computer Vision Task.mp4

04:32

4. Code a First Computer Vision Neural Network.mp4

02:02

5. How to Use Callbacks to Control The Training.mp4

01:28

6. Part 5 Mastering Convolutions

1. Intro.mp4

00:44

2. Dive deeper into convolutions.mp4

09:38

3. Fashion classifier with more advanced convolutions.mp4

05:21

4. New dataset with same more advanced convolutions and further improvement through.mp4

03:49

7. Part 6 More Complex Images

1. Intro.mp4

00:39

2. ImageGenerator.mp4

10:28

3. ConvNet to use on complex images and how to train it with fit generator.mp4

04:37

8. Part 7 More Real-World Images

1. Intro.mp4

00:38

2. Build and train the ConvNet for Real-World Images.mp4

03:16

3. Automatic validation to test and improve the accuracy, as well as the impact of.mp4

05:26

9. Part 8 Image Augmentation

1. Intro.mp4

00:40

2. Dive deeper into image augmentation.mp4

06:45

3. Code gain the augmentation technique with ImageDataGenerator.mp4

01:41

4. Add that to the cats vs. dogs dataset.mp4

01:39

5. Do the same on the horses vs. humans dataset.mp4

01:58

10. Part 9 Transfer Learning

1. Intro.mp4

00:37

2. Concept of transfer learning.mp4

06:08

3. Transfer learning from the inception mode and use dropouts to reduce overfitting.mp4

01:47

4. Code our own model by using transferred features.mp4

02:11

11. Part 10 Multi-Class Classification

1. Intro.mp4

00:39

2. Moving from binary to multi-class classification and the Rock Paper Scissors dat.mp4

03:33

3. Train a classifier with Rock Paper Scissors and test that same classifier.mp4

05:23

12. Part 11 Computer Vision in JavaScript

1. Intro.mp4

01:04

2. Create a Convolutional Net with JavaScript.mp4

05:08

3. Visualize the Training Process.mp4

02:19

4. How to use the Sprite Sheet, and then tf.tidy() to Save Memory.mp4

06:10

13. Part 12 Reusing Existing Models in JavaScript

1. Intro.mp4

01:15

2. Pre-trained TensorFlow.js models and toxicity Classifier, including in code.mp4

06:02

3. MobileNet using TensorFlow.js and MobileNet Example In Code.mp4

04:07

4. How to convert Models to JavaScript.mp4

04:24

14. Part 13 Transfer Learning in JavaScript

1. Intro.mp4

00:40

2. How to retrain the MobileNet Model using Transfer Learning.mp4

06:54

3. How to capture the Data to train again the network.mp4

07:25

4. How to performing Inference.mp4

04:25

15. Part 14 Introduction to NLP - Tokenization and Sequences

1. Intro.mp4

00:54

2. Introduction to NLP and how word based encodings work.mp4

06:19

3. How to go from text to sequence using the tokenizer.mp4

06:50

4. How padding works, still in the process of preprocessing texts.mp4

04:26

16. Part 15 Introduction to NLP - Embeddings

1. Intro.mp4

00:35

2. Introduction to Embeddings.mp4

11:21

3. IMDB dataset to look into the details of embeddings.mp4

04:38

4. Build a classifier for the sarcasm dataset.mp4

04:36

17. Part 16 Introduction to NLP - Exploring Recurrent Models

1. Intro.mp4

00:45

2. Recurrent Models used for NLP, application and implementation of LSTMs to NLP.mp4

07:41

3. Try using a convolutional neural network for NLP.mp4

05:13

18. Part 17 Create Text With RNNs

1. Intro.mp4

00:45

2. Text generation with RNNs.mp4

06:05

3. Train RNNs on some text data to find what the next word should be in a sequence.mp4

04:31

4. Try to do poetry by using RNNs.mp4

04:18

19. Part 18 Sequences and Prediction

1. Intro.mp4

01:00

2. Understanding of time series, and how to split them into the train, validation a.mp4

10:31

3. Different metrics for evaluating performance of time series, concepts of moving.mp4

08:16

20. Part 19 Predicting Sequences With Machine Learning

1. Intro.mp4

00:58

2. How ML is applied to time series and preparation the features and labels.mp4

04:30

3. How to feed a windowed dataset into a neural network, as well as application and.mp4

07:51

4. Training a deep neural network, tuning it, and making prediction.mp4

04:35

21. Part 20 Using RNNs With Sequences

1. Intro.mp4

00:54

2. How RNNs are used with sequences and what must be the shape of the inputs.mp4

08:31

3. Output a sequence, Lambda layers to improve the performance and the learning rat.mp4

02:36

4. How to use the LSTM with the same sequences.mp4

04:02

22. Part 21 Real-World Time Series

1. Intro.mp4

00:36

2. Use of convolutions for real-world time series and Bi-directional LSTMs for real.mp4

04:37

3. Work on real data about sunspots and train and tune the model.mp4

08:05

4. Will make predictions.mp4

04:35

23. Real TensorFlow Certification Exam 1

1. Lesson 1.mp4

12:01

2. Lesson 2.mp4

09:18

3. Lesson 3.mp4

16:08

4. Lesson 4.mp4

08:01

5. Lesson 5.mp4

10:32

24. Real TensorFlow Certification Exam 2

1. Lesson 1.mp4

10:35

2. Lesson 2.mp4

07:18

3. Lesson 3.mp4

24:07

4. Lesson 4.mp4

10:49

5. Lesson 5.mp4

07:59

25. Real TensorFlow Certification Exam 3

1. Lesson 1.mp4

09:58

2. Lesson 2.mp4

11:12

3. Lesson 3.mp4

20:11

4. Lesson 4.mp4

12:56

5. Lesson 5.mp4

09:51

26. Course Extra Introduction to TensorFlow Lite

1. Quick Update.html

2. Intro.mp4

01:34

3. TensorFlow Lite features and components (incl. architecture and performance), op.mp4

09:01

4. How to save, convert, and optimize a model, as well as introduction to TF-Select.mp4

13:22

5. How to convert a model to TFLite and how to do Transfer Learning with TFLite.mp4

05:02

27. Course Extra TF Lite and Android

1. Intro.mp4

00:39

2. Introduction to TF Lite with Android and architecture of a model in Android.mp4

08:25

3. How to initialize the Interpreter.mp4

04:34

4. How to prepare the Input and how to do inference and get results.mp4

28. Course Extra TF Lite and iOS

1. Intro.mp4

00:48

2. Introduction to TF Lite with iOS, Swift and TF Lite Swift.mp4

10:06

3. Initializing the interpreter, preparing the inputs, doing inference and getting.mp4

03:16

29. Course Extra TF Lite and Micro Systems

1. Intro.mp4

00:52

2. Introduction to TF Lite with Micro Systems.mp4

06:42

3. How to start working on a Raspberry Pi and illustrate this with Image classifica.mp4

13:15

4. Initializing the interpreter, preparing the inputs, doing inference and getting.mp4

03:22

Description

Join the best training ground for AI mastery and gain the skills you need to become a TensorFlow Certified Developer.

What You'll Learn?

Understand Deep Learning Fundamentals
Construct three different deep learning models using TensorFlow and Keras
Classify images using convolutional neural networks (CNNs) in TensorFlow.
Apply image augmentation and transfer learning to enhance model performance.
Utilize strategies to prevent overfitting, including augmentation and dropout.
Process text through tokenization and sentence vector representation.
Apply Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTM) networks to NLP tasks
Create Device-Based Models with TensorFlow Lite

Who is this for?

Data Scientists who simply want to learn how to use TensorFlow at an advanced level.

Data Scientists who want to pass the TensorFlow Developer Certification.

AI Practitioners who want to build more powerful AI models using TensorFlow.

Tech enthusiasts who are passionate about AI and want to gain real-world practical experience with TensorFlow.

What You Need to Know?

Basic knowledge of programming is recommended.

Some experience in Machine Learning is also preferable but not required.

More details

Description
In this course you will learn everything you need to know to master the TensorFlow Developer Certification.

We will start by studying Deep Learning in depth so that you can understand how artificial neural networks work and learn. And while covering the Deep Learning theory we will also build together three different Deep Learning models in TensorFlow and Keras, from scratch, step by step, and coding every single line of code together.

Then, we will move on to Computer Vision, where you will learn how to classify images using convolutions with TensorFlow. You will also learn some techniques such as image augmentation and transfer learning to get even more performance in your computer vision tasks. And we will practice all this on real-world image data, while exploring strategies to prevent overfitting, including augmentation and dropout.
Then, you will learn how to use JavaScript, in order to train and run inference in a browser, handle data in a browser, and even build an object classification and recognition model using a webcam.
Then you will learn how to do Natural Language Processing using TensorFlow. Here we will build natural language processing systems, process text including tokenization and representing sentences as vectors, apply RNNs, GRUs, and LSTMs in TensorFlow, and train LSTMs on existing text to create original poetry and more.
And finally, you will also learn how to build Device-based Models with TensorFlow Lite. In this last part we will prepare models for battery-operated devices, execute models on Android and iOS platforms, and deploy models on embedded systems like Raspberry Pi and microcontrollers.
Who this course is for:
The course is targeted towards AI practitioners, aspiring data scientists, Tech enthusiasts, and consultants wanting to pass the TensorFlow Developer Certification. Hereâ€™s a list of who is this course for:
Data Scientists who simply want to learn how to use TensorFlow at an advanced level.
Data Scientists who want to pass the TensorFlow Developer Certification.
AI Practitioners who want to build more powerful AI models using TensorFlow.
Tech enthusiasts who are passionate about AI and want to gain real-world practical experience with TensorFlow.
Course Prerequisites:
Basic knowledge of programming is recommended. Some experience in Machine Learning is also preferable. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to anyone with basic programming knowledge. Students who enrol in this course will master data science fundamentals and directly apply these skills to solve real world challenging business problems.
*Terms & Conditions of Exam Guarantee:
Ligency Ventures Pty Ltd, U.K provides the following guarantee for the TensorFlow Developer Professional Certificate Course:
If you take your TensorFlow Developer Certificate exam within 30 days of enrolling and completing this course 100% and you sit the exam and receive a score above zero, but below the minimum score required to pass the exam, then Ligency Ventures Pty Ltd, U.K will pay for your second exam attempt provided the following conditions are met: you paid at least $1 for this course and it was not refunded, AND before sitting the exam, you diligently watched and followed along with all of the tutorials in the course (completed all case studies and have all codes under your Google Colab account), AND you completed all practical activities including but not limited to challenges within the sections, quizzes, homework exercises and all provided practice exams.
Ligency Ventures Pty Ltd may request evidence of fulfilling the above conditions, thereby it's important that you save your work when taking the course and doing the practical assignments.
Who this course is for:
Data Scientists who simply want to learn how to use TensorFlow at an advanced level.
Data Scientists who want to pass the TensorFlow Developer Certification.
AI Practitioners who want to build more powerful AI models using TensorFlow.
Tech enthusiasts who are passionate about AI and want to gain real-world practical experience with TensorFlow.

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Ligency Team

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Hi there,We are the Ligency PR and Marketing team. You will be hearing from us when new courses are released, when we publish new podcasts, blogs, share cheatsheets and more!We are here to help you stay on the cutting edge of Data Science and Technology.See you in class,Sincerely,The Real People at Ligency

TFC Course

Instructor's Courses

Hello! I’m a TensorFlow Developer specializing in designing and deploying advanced deep learning models. With a passion for AI and machine learning, I create comprehensive courses to help you master TensorFlow and achieve the TensorFlow Developer Certification. Join me to unlock the full potential of deep learning and transform your skills for the future of AI.

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