Become a TensorFlow Certified Professional Developer

سرفصل های دوره

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

1. Part 0 Introduction To The Course

1. Introduction to the Course

2. Contact and Questions.html

2. Part 1 Artificial Neural Networks

1. Intro

2. Get course materials.html

3. Plan of Attack

4. Functioning of the Human Neuron

5. How Neural Networks Work

6. Activation Function

7. How Neural Networks Learn

8. Gradient Descent

9. Stochastic Gradient Descent

10. Back-Propagation

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

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

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

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

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

3. Part 2 Convolutional Neural Networks

1. Intro

2. Plan of Attack

3. What are Convolutional Neural Networks

4. Step 1 The Convolution Operation

5. Step 1 (Part B) ReLU Layer

6. Step 2 Pooling

7. Step 3 Flattening

8. Step 4 Full Connection

9. Summary

10. Softmax Activation Function & Cross-Entropy Loss Function

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

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

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

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

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

16. Demo

4. Part 3 Recurrent Neural Networks

1. Intro

2. Plan of Attack

3. Recurrent Neural Networks

4. Vanishing Gradient Problem

5. LSTMs and How They Work

6. Practical Intuition

7. LSTM Variations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

5. Part 4 Intro to Computer Vision

1. Intro

2. Introduction to Computer Vision

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

4. Code a First Computer Vision Neural Network

5. How to Use Callbacks to Control The Training

6. Part 5 Mastering Convolutions

1. Intro

2. Dive deeper into convolutions

3. Fashion classifier with more advanced convolutions

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

7. Part 6 More Complex Images

1. Intro

2. ImageGenerator

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

8. Part 7 More Real-World Images

1. Intro

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

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

9. Part 8 Image Augmentation

1. Intro

2. Dive deeper into image augmentation

3. Code gain the augmentation technique with ImageDataGenerator

4. Add that to the cats vs. dogs dataset

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

10. Part 9 Transfer Learning

1. Intro

2. Concept of transfer learning

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

4. Code our own model by using transferred features

11. Part 10 Multi-Class Classification

1. Intro

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

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

12. Part 11 Computer Vision in JavaScript

1. Intro

2. Create a Convolutional Net with JavaScript

3. Visualize the Training Process

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

13. Part 12 Reusing Existing Models in JavaScript

1. Intro

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

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

4. How to convert Models to JavaScript

14. Part 13 Transfer Learning in JavaScript

1. Intro

2. How to retrain the MobileNet Model using Transfer Learning

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

4. How to performing Inference

15. Part 14 Introduction to NLP - Tokenization and Sequences

1. Intro

2. Introduction to NLP and how word based encodings work

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

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

16. Part 15 Introduction to NLP - Embeddings

1. Intro

2. Introduction to Embeddings

3. IMDB dataset to look into the details of embeddings

4. Build a classifier for the sarcasm dataset

17. Part 16 Introduction to NLP - Exploring Recurrent Models

1. Intro

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

3. Try using a convolutional neural network for NLP

18. Part 17 Create Text With RNNs

1. Intro

2. Text generation with RNNs

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

4. Try to do poetry by using RNNs

19. Part 18 Sequences and Prediction

1. Intro

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

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

20. Part 19 Predicting Sequences With Machine Learning

1. Intro

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

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

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

21. Part 20 Using RNNs With Sequences

1. Intro

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

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

4. How to use the LSTM with the same sequences

22. Part 21 Real-World Time Series

1. Intro

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

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

4. Will make predictions

23. Real TensorFlow Certification Exam 1

1. Lesson 1

2. Lesson 2

3. Lesson 3

4. Lesson 4

5. Lesson 5

24. Real TensorFlow Certification Exam 2

1. Lesson 1

2. Lesson 2

3. Lesson 3

4. Lesson 4

5. Lesson 5

25. Real TensorFlow Certification Exam 3

1. Lesson 1

2. Lesson 2

3. Lesson 3

4. Lesson 4

5. Lesson 5

26. Course Extra Introduction to TensorFlow Lite

1. Quick Update.html

2. Intro

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

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

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

27. Course Extra TF Lite and Android

1. Intro

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

3. How to initialize the Interpreter

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

28. Course Extra TF Lite and iOS

1. Intro

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

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

29. Course Extra TF Lite and Micro Systems

1. Intro

2. Introduction to TF Lite with Micro Systems

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

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

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