TensorFlow Data and Deployment Specialization

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Intermediate

1.Specialization Introduction
- 1.Specialization Introduction, A Conversation with Andrew Ng.mp4
  
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2.Training and Inference using TensorFlow.js in JavaScript
- 1.Course Introduction, A Conversation with Andrew Ng.mp4
  
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- 2.A Few Words From Laurence.mp4
  
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- 3.Building the Model.mp4
  
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- 4.Training the Model.mp4
  
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- 5.First Example In Code.mp4
  
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Quiz
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3.Training Models with CSV Files
- 1.The Iris Dataset.mp4
  
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- 2.Reading the Data.mp4
  
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- 3.One-hot Encoding.mp4
  
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- 4.Designing the NN.mp4
  
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- 5.Iris Classifier In Code.mp4
  
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Quiz
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Grade quiz
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1.Creating Convolutional Neural Networks in JavaScript
- 1.Introduction, A Conversation with Andrew Ng.mp4
  
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- 2.Creating a Convolutional Net with JavaScript.mp4
  
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- 3.Visualizing the Training Process.mp4
  
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2.Using a Sprite Sheet
- 1.What is a Sprite Sheet.mp4
  
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- 2.Using the Sprite Sheet.mp4
  
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3.MNIST Classifier
- 1.Using tf.tidy() to Save Memory.mp4
  
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- 2.A Few Words From Laurence.mp4
  
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- 3.MNIST Classifier In Code.mp4
  
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Quiz
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Grade quiz
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1.Toxicity Classifier
- 1.Introduction, A Conversation with Andrew Ng.mp4
  
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- 2.A Few Words From Laurence.mp4
  
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- 3.Pre-trained TensorFlow.js Models.mp4
  
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- 4.Toxicity Classifier.mp4
  
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- 5.Toxicity Classifier In Code.mp4
  
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2.Image Classification Using MobileNet
- 1.MobileNet.mp4
  
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- 2.Using MobileNet.mp4
  
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- 3.Training Results.mp4
  
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- 4.MobileNet Example In Code.mp4
  
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3.Converting Models to JSON Format
- 1.Converting Models to JavaScript.mp4
  
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- 2.Converting Models to JavaScript In Code.mp4
  
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- 3.Linear Example In Code.mp4
  
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Quiz
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Grade quiz
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1.Retraining the MobileNet Model
- 1.Introduction, A Conversation with Andrew Ng.mp4
  
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- 2.A Few Words From Laurence.mp4
  
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- 3.Building a Simple Web Page.mp4
  
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- 4.Retraining the MobileNet Model.mp4
  
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- 5.The Training Function.mp4
  
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2.Capturing the Data
- 1.Capturing the Data.mp4
  
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- 2.The Dataset Class.mp4
  
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- 3.Training the Network with the Captured Data.mp4
  
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3.Performing Inference From the Webcam Feed
- 1.Performing Inference.mp4
  
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- 2.Rock Paper Scissors In Code.mp4
  
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Quiz
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4.Course 1 wrap up
- 1.A conversation with Andre Ng.mp4
  
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Grade quiz
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1.Course Introduction
- 1.Introduction, A conversation with Andrew Ng.mp4
  
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2.Machine Learning Models in Mobile and Embedded Systems
- 1.A few words from Laurence.mp4
  
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- 2.Features and components of mobile AI.mp4
  
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- 3.Architecture and performance.mp4
  
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- 4.Optimization Techniques.mp4
  
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3.Taking a look at the saved model format
- 1.Saving, converting, and optimizing a model.mp4
  
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- 2.Examples.mp4
  
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- 3.Quantization.mp4
  
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- 4.TF-Select.mp4
  
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- 5.Paths in Optimization.mp4
  
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4.First primer on running models on mobile devices
- 1.Running the models.mp4
  
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- 2.Transfer learning.mp4
  
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- 3.Converting a model to TFLite.mp4
  
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- 4.Transfer learning with TFLite.mp4
  
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Quiz
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Exercise 1 - Train Your Own Model and Convert It to TFLite
- Exercise_1_TF_Lite_Question-FINAL.ipynb
  
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Grade exercise
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1.Introduction
- 1.Introduction, A conversation with Andrew.mp4
  
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- 2.Installation and resources.mp4
  
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- 3.Architecture of a model.mp4
  
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- 4.Initializing the Interpreter.mp4
  
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- 5.Preparing the Input.mp4
  
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- 6.Inference and results.mp4
  
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2.Basic image classification
- 1.Code walkthrough.mp4
  
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- 2.Run the App.mp4
  
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3.Classifying camera images
- 1.Classifying camera images.mp4
  
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- 2.Initialize and prepare input.mp4
  
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4.Code walkthrough - camera image classifier
- 1.Demo of camera image classifier.mp4
  
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Quiz
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5.Object detection
- 1.Initialize model and prepare inputs.mp4
  
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- 2.Inference and results.mp4
  
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6.Code walkthrough of an object detection app
- 1.Demo of the object detection App.mp4
  
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- 2.Code for the inference and results.mp4
  
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Quiz
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1.Introduction
- 1.Introduction, A conversation with Andrew Ng.mp4
  
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- 2.A few words from Laurence.mp4
  
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- 3.What is Swift?.mp4
  
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- 4.TensorFlowLiteSwift.mp4
  
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- 5.Cats vs Dogs App.mp4
  
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- 6.Taking the initial steps.mp4
  
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- 7.Scaling the image.mp4
  
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- 8.More steps in the process.mp4
  
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2.Next steps
- 1.Looking at the App in Xcode.mp4
  
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- 2.What have we done so far and how do we continue?.mp4
  
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- 3.Using the App.mp4
  
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- 4.App architecture.mp4
  
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- 5.Model details.mp4
  
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- 6.Initial steps.mp4
  
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- 7.Final steps.mp4
  
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3.Classification and detection
- 1.Looking at the code for the image classification App.mp4
  
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- 2.Object classification intro.mp4
  
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- 3.TFL detect App.mp4
  
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- 4.App architecture.mp4
  
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- 5.Initial steps.mp4
  
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- 6.Final steps.mp4
  
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- 7.Looking at the code for the object detection model.mp4
  
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Quiz
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1.Introduction
- 1.Introduction, A conversation with Andrew Ng.mp4
  
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- 2.A few words from Laurence.mp4
  
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- 3.Devices.mp4
  
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2.Example: Raspberry Pi
- 1.Starting to work on a Raspberry Pi.mp4
  
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- 2.How do we start?.mp4
  
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- 3.Image classification.mp4
  
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- 4.The 4 step process.mp4
  
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- 5.Object detection.mp4
  
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- 6.Back to the 4 step process.mp4
  
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3.Raspberry pi demo
- 1.Raspberry Pi demo.mp4
  
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4.Microcontrollers
- 1.Microcontrollers.mp4
  
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- 2.Closing words by Laurence.mp4
  
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Quiz
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5.Course 2 Wrap up
- 1.A conversation with Andrew Ng.mp4
  
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1.Data Pipelines with TensorFlow Data Services
- 1.A conversation with Andrew Ng.mp4
  
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2.Introduction
- 1.Introduction.mp4
  
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- 2.Popular Datasets.mp4
  
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- 3.Data Pipelines.mp4
  
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- 4.Extract, Transform and Load.mp4
  
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- 5.Versioning Datasets.mp4
  
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- 6.Looking at the Notebook.mp4
  
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3.Using TFDS
- 1.Using TFDS in Keras to Train Fashion MNIST .mp4
  
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- 2.Horses or Humans in TFDS.mp4
  
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4.Week1 Quiz and Closing Words
- 1.Week 1 Wrap Up.mp4
  
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Quiz
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Programming Assignment: TFDS with Rock, Paper and Scissors
- TFDS-V2-Week1.ipynb
  
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1.Introduction
- 1.Introduction.mp4
  
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2.Splits and Slices
- 1.Introduction to Splits API.mp4
  
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- 2.Splits API Notebook Walkthrough.mp4
  
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3.TFRecord
- 1.File Structure in TensorFlow Datasets.mp4
  
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- 2.Feature Descriptors.mp4
  
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- 3.TFRecord Colab Walkthrough.mp4
  
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4.Week 2 Quiz and Closing Words
- 1.Week 2 Wrap Up.mp4
  
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Quiz
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Programming Assignment: Transfer Learning and Splits API
- TFDS-V2-Week2.ipynb
  
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1.Programming APIs and Column Types
- 1.A Conversation with Andrew Ng.mp4
  
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- 2.Introduction.mp4
  
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- 3.Input Data.mp4
  
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- 4.Basic Mechanics.mp4
  
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- 5.Numeric and Bucketized Columns.mp4
  
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- 6.Vocabulary and Hashed Columns, Feature Crossing.mp4
  
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- 7.Embedding Columns.mp4
  
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2.Going over the Notebook
- 1.Introduction.mp4
  
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- 2.Reading: link to the notebook.mp4
  
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3.Extracting and Loading Data to Pipelines
- 1.Introduction.mp4
  
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- 2.Numpy, Pandas and Images.mp4
  
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- 3.CSV.mp4
  
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- 4.Text and TFRecord.mp4
  
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- 5.Generators.mp4
  
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4.Looking at the Code
- 1.Introduction.mp4
  
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- 2.Notebook walkthrough.mp4
  
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5.Loading Data Present Outside TFDS
- 1.Introduction.mp4
  
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- 2.Using Numpy and Pandas.mp4
  
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- 3.Image Data.mp4
  
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- 4.CSV Data.mp4
  
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- 5.Text Data.mp4
  
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6.Week 3 quiz
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Programming Assignment: Classify Structured Data
- TFDS-V2-Week3.ipynb
  
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1.Tuning and Performance Improvements in your Pipeline
- 1.A conversation with Andrew Ng.mp4
  
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- 2.Introduction.mp4
  
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- 3.ETL.mp4
  
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- 4.What Happens When You Train a Model.mp4
  
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2.Methodologies to Improve Performance
- 1.Introduction.mp4
  
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- 2.Caching.mp4
  
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- 3.Parallelism APIs.mp4
  
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- 4.Autotuning.mp4
  
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- 5.Parallelizing Data Extraction.mp4
  
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3.Best Practices
- 1.Best Practices for Code Improvements.mp4
  
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- 2.A Few Words by Laurence.mp4
  
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Quiz
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4.[Optional] Publishing your Datasets
- 1.A conversation with Andrew Ng.mp4
  
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- 2.Introduction.mp4
  
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- 3.How to Start Using a Dataset.mp4
  
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- 4.Implementation.mp4
  
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- 5.File Access and Possible Problems in Data.mp4
  
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- 6.Publishing the Dataset.mp4
  
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5.[Optional] Colab
- 1.Introduction.mp4
  
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- 2.Going Through the Colab- Part 1.mp4
  
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- 3.Going Through the Colab - Part 2.mp4
  
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- 4.Closing Words.mp4
  
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Quiz
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6.Course 3 wrap up
- 1.A conversation with Andrew Ng.mp4
  
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Lab Adding a Dataset of your Own to TFDS
- TFDS-V2-Week4-Optional.ipynb
  
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Programming Assignment: Parallelization with TFDS
- TFDS-V2-Week4.ipynb
  
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1.TF serving as another deployment option for the model and ways to install it
- 1.Introduction, A conversation with Andrew Ng.mp4
  
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- 2.Introduction.mp4
  
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- 3.Serving.mp4
  
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- 4.Installing TF Serving.mp4
  
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- 5.TensorFlow Serving summary.mp4
  
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2.Building a model and deploying to TF Serving
- 1.Setup for serving.mp4
  
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- 2.Serving.mp4
  
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3.Passing data to and from the model
- 1.Predictions.mp4
  
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- 2.Passing data to serving.mp4
  
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- 3.Getting the predictions back.mp4
  
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- 4.Running the colab.mp4
  
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4.Looking into a more complex model using the Fashion MNIST dataset
- 1.Complex model.mp4
  
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Quiz
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Ungraded Assignment - Serving with MNIST
- C4_W1_Assignment.ipynb
  
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1.TF Hub
- 1.Introduction, A conversation with Andrew Ng.mp4
  
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2.Text based models
- 2.Word embeddings.mp4
  
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- 3.Experimenting with embeddings.mp4
  
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- 4.Colab.mp4
  
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3.Image classification
- 1.Classify cats and dogs.mp4
  
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- 2.Transfer learning.mp4
  
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Quiz
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TensorFlow Hub assignment
- TF_Serving_Week_2_Exercise_Question.ipynb
  
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1.Overview of Tensorboard
- 1.Introduction, A conversation with Andrew Ng.mp4
  
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Tensorboard Assignment
- TF_Serving_Week_3_Exercise_Question.ipynb
  
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branches
- config
  
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- description
  
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- HEAD
  
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hooks
- applypatch-msg.sample
  
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- commit-msg.sample
  
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- fsmonitor-watchman.sample
  
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- post-update.sample
  
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- pre-applypatch.sample
  
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- pre-commit.sample
  
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- pre-push.sample
  
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- pre-rebase.sample
  
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- pre-receive.sample
  
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- prepare-commit-msg.sample
  
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- update.sample
  
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- index
  
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info
- exclude
  
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logs
- HEAD
  
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heads
- main
  
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origin
- HEAD
  
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pack
- pack-1422b27acf9bb82308a71e3a7335b0ecfaf458f1.idx
  
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- pack-1422b27acf9bb82308a71e3a7335b0ecfaf458f1.pack
  
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- packed-refs
  
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heads
- main
  
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origin
- HEAD
  
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tags
- .gitignore
  
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- .gitmodules
  
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Misc
- mnist_images.png
  
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- README.md
  
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assignment
- C1_W1_Assignment.html
  
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data
- wdbc-test.csv
  
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- wdbc-train.csv
  
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ungraded_labs
- C1_W1_Lab_1_FirstHTML.html
  
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- C1_W1_Lab_2_iris_classifier.html
  
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data
- iris.csv
  
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assignment
- C1_W2_Assignment.js
  
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- fashion-data.js
  
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- fashion-mnist.html
  
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ungraded_lab
- C1_W2_Lab_1_mnist.html
  
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- data.js
  
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- script.js
  
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assignment
- C1_W3_Assignment.ipynb
  
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data
- cats_and_dogs_filtered.zip
  
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ungraded_labs
- C1_W3_Lab_1_toxicity_classifier.html
  
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- C1_W3_Lab_2_mobilenet.html
  
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- C1_W3_Lab_3A_linear_to_JavaScript.ipynb
  
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- C1_W3_Lab_3B_linear.html
  
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image
- cat.jpg
  
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- coffee.jpg
  
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- pig.jpg
  
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output_files
- group1-shard1of1.bin
  
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- model.json
  
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assignment
- C1_W4_Assignment.js
  
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- rps-dataset.js
  
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- rpsls.html
  
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- webcam.js
  
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ungraded_lab
- C1_W4_Lab_1_retrain.html
  
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- index.js
  
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- rps-dataset.js
  
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- webcam.js
  
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C2_Device-based-TF-lite
- README.md
  
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assignment
- C2_W1_Assignment.ipynb
  
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- fileNameOnCoursera.md
  
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ungraded_labs
- C2_W1_Lab_1_Linear_Regression.ipynb
  
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- C2_W1_Lab_2_Transfer_Learning.ipynb
  
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assignment_optional
- C2_W2_Assignment_Solution.ipynb
  
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- C2_W2_Assignment.ipynb
  
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cats_vs_dogs
- .gitignore
  
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app
- .gitignore
  
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- build.gradle
  
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- proguard-rules.pro
  
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main
- AndroidManifest.xml
  
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assets
- converted_model.tflite
  
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- Classifier.kt
  
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view
- ImageClassifierActivity.kt
  
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drawable
- ic_launcher_background.xml
  
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- img0.jpg
  
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- img1.jpg
  
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- img2.jpg
  
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- img17.jpg
  
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- img22.jpg
  
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drawable-v24
- ic_launcher_foreground.xml
  
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drawable-xxhdpi
- ic_action_info.png
  
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- ic_launcher.png
  
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layout
- activity_image_classifier.xml
  
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mipmap-anydpi-v26
- ic_launcher_round.xml
  
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- ic_launcher.xml
  
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mipmap-hdpi
- ic_launcher_round.png
  
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- ic_launcher.png
  
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mipmap-mdpi
- ic_launcher_round.png
  
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- ic_launcher.png
  
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mipmap-xhdpi
- ic_launcher_round.png
  
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- ic_launcher.png
  
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mipmap-xxhdpi
- ic_launcher_round.png
  
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- ic_launcher.png
  
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mipmap-xxxhdpi
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main
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customview
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CatVsDogClassifierSample.xcodeproj
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Pods-CatVsDogClassifierSample
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TensorFlowLiteC
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TensorFlowLiteSwift
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Base.lproj
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Pods-ImageClassification
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TensorFlowLiteC
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TensorFlowLiteSwift
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TensorFlowLiteSwift
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Base.lproj
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Model
- .gitignore
  
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ModelDataHandler
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ObjectDetection.xcodeproj
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xcschemes
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ObjectDetection.xcworkspace
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lmoroney.xcuserdatad
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Pods-ObjectDetection
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TensorFlowLiteC
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TensorFlowLiteSwift
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TensorFlowLiteSwift
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Sources
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- QuantizationParameters.swift
  
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RunScripts
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assignment_optional
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hyperparameter_tuning
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image_classification
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object_detection
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transfer_learning
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C3_Data-pipelines
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assignment
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ungraded_labs
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assignment
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ungraded_labs
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assignment
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data
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data
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A1
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A2
- C3_W4_A2_Assignment_Optional_Solution.ipynb
  
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imdb_crop
- imdb.mat
  
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0.1.0
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image
- __init__.py
  
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scripts
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url_checksums
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C4_Advanced-deployment
- README.md
  
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assignment
- C4_W1_Assignment_Solution.ipynb
  
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- C4_W1_Assignment.ipynb
  
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ungraded_labs
- C4_W1_Lab_1_tfserving_hello_world.ipynb
  
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assignment
- C4_W2_Assignment.ipynb
  
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- fileNameOnCoursera.md
  
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ungraded_labs
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assets
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variables
- variables.data-00000-of-00001
  
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- variables.index
  
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tf2-preview_mobilenet_v2_classification_4
- saved_model.pb
  
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variables
- variables.data-00000-of-00001
  
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- variables.index
  
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tf2-preview_mobilenet_v2_feature_vector_4
- saved_model.pb
  
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variables
- variables.data-00000-of-00001
  
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- variables.index
  
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saved_model
- saved_model.pb
  
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variables
- variables.data-00000-of-00001
  
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- variables.index
  
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assignment
- C4_W3_Assignment.ipynb
  
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data
- test_images.npy
  
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- train_labels.npy
  
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images
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ungraded_lab
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