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Become a TensorFlow Certified Professional Developer

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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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    تاریخ انتشار: ۲ آبان ۱۴۰۳
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