TensorFlow Advanced Techniques Specialization

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Course content

1. A conversation with Andrew Ng
- 1. Overview of the Specialization.mp4
- 2. Overview of Course 1.mp4
2. Functional APIs
- 1. Welcome to the course.mp4
- 2. Introduction to the Functional APIs.mp4
Lab
- C1_W1_Lab_1_functional-practice.ipynb
- Files.zip
3. Using the Functional APIs
- 1. Declaring and stacking layers.mp4
- 2. Branching layers.mp4
4. Creating a Multi-Output Architecture
- 1. Creating a Multi-Output model.mp4
- 2. Multi-Output code walkthrough.mp4
Lab
- C1_W1_Lab_2_multi-output.ipynb
- Files.zip
5. Siamese Network
- 1. Siasmese network - a Multiple-Input model.mp4
- 2. Coding a Multi-Input Siamese network.mp4
- 3. Siamese network code walkthrough.mp4
Lab
- C1_W1_Lab_3_siamese-network.ipynb
- Files.zip
Files
- .hidden
- C1W1_Assignment.ipynb
- Files.zip
- utils.py
- winequality-red.csv
- winequality-white.csv
- Functional API - Quiz.pdf
1. Custom loss functions
- 1.Welcome to Week 2.mp4
- 2. Creating a custom loss function.mp4
- 3. Coding the Huber Loss function.mp4
- 4. Huber Loss code walkthrough.mp4
Lab
- C1_W2_Lab_1_huber-loss.ipynb
- Files.zip
2. Custom loss hyperparameters and classes
- 1. Adding hyperparameters to custom loss functions.mp4
- 2. Turning loss functions into classes.mp4
- 3. Huber Object Loss code walkthrough.mp4
- C1_W2_Lab_2_huber-object-loss.ipynb
3. Constrastive Loss
- 1. Constrative Loss.mp4
- 2. Coding Constrative Loss.mp4
- C1_W1_Lab_3_siamese-network.ipynb
W2_Assignment
- .hidden
- C1W2_Assignment.ipynb
- utils.py
- Custom Loss - Quiz.pdf
1. Custom lambda layers
- 1.Introduction to Lambda Layers.mp4
- 2.Introduction to Lambda Layers.mp4
- 3.Custom Functions from Lambda Layers.mp4
- 4.Exploring custom Relu with Lambda Layers .mp4
- C1_W3_Lab_1_lambda-layer.ipynb
2. Implementing Custom Layers
- 1.Architecture of a Custom Layer.mp4
- 2.Coding your own custom Dense Layer.mp4
- 3.Training a neural network with your Custom Layer.mp4
- 4.Custom Layer code walkthrough.mp4
- C1_W3_Lab_2_custom-dense-layer.ipynb
3. Activating Custom Layers
- 1.Activating your Custom Layer.mp4
- 2.Custom Layer with activation code walkthrough.mp4
- C1_W3_Lab_3_custom-layer-activation.ipynb
W3_Assignment
- .hidden
- C1W3_Assignment.ipynb
- utils.py
- Custom Layers - Quiz.pdf
1. Complex Architectures with Functional API
- 1.Intro to custom models.mp4
- 2.Complex architectures with the Functional API.mp4
- 3.Coding a Wide and Deep model.mp4
- C1_W4_Lab_1_basic-model.ipynb
2. Using the Model class to simplify complex architectures
- 1.Using the Model class to simplify architectures.mp4
- 2.Understanding Residual networks.mp4
3. Implementing ResNet
- 1.Coding a Residual network with the Model class.mp4
- 2.ResNet code walkthrough.mp4
- C1_W4_Lab_2_resnet-example.ipynb
Custom Models - Assignment
- C1W4_Assignment.ipynb
4.0.0
- cats_vs_dogs-train.tfrecord-00000-of-00008
- cats_vs_dogs-train.tfrecord-00001-of-00008
- cats_vs_dogs-train.tfrecord-00002-of-00008
- cats_vs_dogs-train.tfrecord-00003-of-00008
- cats_vs_dogs-train.tfrecord-00004-of-00008
- cats_vs_dogs-train.tfrecord-00005-of-00008
- cats_vs_dogs-train.tfrecord-00006-of-00008
- cats_vs_dogs-train.tfrecord-00007-of-00008
- dataset_info.json
- image.image.json
- utils.py
- VGG.png
- Custom Models - Quiz.pdf
3.0.1
- dataset_info.json
- image.image.json
- mnist-test.tfrecord-00000-of-00001
- mnist-train.tfrecord-00000-of-00001
1. Built-in Callbacks
- 1.Built-in Callbacks.mp4
- C1_W5_Lab_1_exploring-callbacks.ipynb
2. Custom Callbacks
- 1.Custom Callbacks.mp4
- 2.Custom Callbacks code walkthrough.mp4
- C1_W5_Lab_2_custom-callbacks.ipynb
Labs data
- Files.zip
1. A conversation with Andrew Ng
- 1.A conversation with Andrew Ng Overview of course 2.mp4
2. Tensor Basics
- 1.What is a tensor.mp4
- 2.Creating tensors in code.mp4
- 3.Math operations with tensors.mp4
- 4.Basic Tensors code walkthrough.mp4
- C2_W1_Lab_1_basic-tensors.ipynb
3. Working with Tensors in Eager Mode
- 1.Broadcasting, operator overloading and Numpy compatibility.mp4
- 2.Evaluating variables and changing data types.mp4
4. Gradient Tape
- 1.Gradient Tape.mp4
- 2.Gradient Descent using Gradient Tape.mp4
- 3.Calculate gradients on higher order functions.mp4
- 4.Persistent=true and higher order gradients.mp4
- 5.Gradient Tape basics code walkthrough.mp4
- C2_W1_Lab_2_gradient-tape-basics.ipynb
Tensor and Gradient Tape - Assignment
- C2W1_Assignment.ipynb
- Tensors and Gradient Tape - Quiz.pdf
1. Custom Training Loops
- 1.Custom Training Loop steps.mp4
- 2.Loss and gradient descent.mp4
- 3.Define Training Loop and Validate Model.mp4
- 4.Training Basics code walkthrough.mp4
- C2_W2_Lab_1_training-basics.ipynb
2. Custom Training with TensorFlow Datasets
- 1.Training steps and data pipeline.mp4
- 2.Define the training loop.mp4
- 3.Gradients, metrics, and validation.mp4
- 4.Fashion MNIST Custom Training Loop code walkthrough.mp4
- C2_W2_Lab_2_training-categorical.ipynb
.ipynb_checkpoints
- C2W2_Assignment-checkpoint.ipynb
- C2W2_Assignment.ipynb
data
- data.csv
test_model
- saved_model.pb
variables
- variables.data-00000-of-00001
- variables.index
- Custom Training - Quiz.pdf
3.0.1
- dataset_info.json
- fashion_mnist-test.tfrecord-00000-of-00001
- fashion_mnist-train.tfrecord-00000-of-00001
- image.image.json
1. AutoGraph
- 1.Benefits of graph mode.mp4
- 2.Generating graph code.mp4
- 3.AutoGraph Basics code walkthrough.mp4
- C2_W3_Lab_1_autograph-basics.ipynb
2. Creating Graphs for Complex Code
- 1.Control dependencies and flows.mp4
- 2.Loops and tracing variables.mp4
- 3.AutoGraph code walkthrough.mp4
- C2_W3_Lab_2-graphs-for-complex-code.ipynb
Files
- C2W3_Assignment.ipynb
3.0.0
- dataset_info.json
- horses_or_humans-test.tfrecord-00000-of-00001
- horses_or_humans-train.tfrecord-00000-of-00002
- horses_or_humans-train.tfrecord-00001-of-00002
- image.image.json
resnet_50_feature_vector
- saved_model.pb
variables
- variables.data-00000-of-00001
- variables.index
- AutoGraph - Quiz.pdf
1. Overview of Distribution Strategies
- 1.Intro to distribution strategies.mp4
- 2.Types of distribution strategies.mp4
2. Mirrored Strategy
- 1.Converting code to the Mirrored Strategy.mp4
- 2.Mirrored Strategy code walkthrough.mp4
- C2_W4_Lab_1_basic-mirrored-strategy.ipynb
3. Multiple GPU Mirrored Strategy
- 1.Custom Training for Multiple GPU Mirrored Strategy.mp4
- 2.Multi GPU Mirrored Strategy code walkthrough.mp4
- C2_W4_Lab_2_multi-GPU-mirrored-strategy.ipynb
4. TPU Strategy
- 1.TPU Strategy.mp4
- 2.TPU Strategy code walkthrough.mp4
- C2_W4_Lab_3_using-TPU-strategy.ipynb
5. Other Distributed Strategies
- 1.Other Distributed Strategies.mp4
- C2_W4_Lab_4_one-device-strategy.ipynb
Files
- C2W4_Assignment.ipynb
2.1.1
- dataset_info.json
- features.json
- image.image.json
- oxford_flowers102-test.tfrecord-00000-of-00002
- oxford_flowers102-test.tfrecord-00001-of-00002
- oxford_flowers102-train.tfrecord-00000-of-00001
- oxford_flowers102-validation.tfrecord-00000-of-00001
resnet_50_feature_vector
- saved_model.pb
variables
- variables.data-00000-of-00001
- variables.index
- AutoGraph - Quiz.pdf
3.0.1
- dataset_info.json
- image.image.json
- mnist-test.tfrecord-00000-of-00001
- mnist-train.tfrecord-00000-of-00001
1. Concepts in Computer Vision
- 1.Welcome to Course 3.mp4
- 2.Classification and Object Detection Intro.mp4
- 3.Segmentation Intro.mp4
2. Transfer Learning
- 1.Why Transfer Learning.mp4
- 2.What is Transfer Learning.mp4
- 3.Options in Transfer Learning.mp4
- C3_W1_Lab_1_transfer_learning_cats_dogs.ipynb
3. Advanced Transfer learning
- 1.Transfer Learning with ResNet50.mp4
- 2.ResNet50 in code.mp4
- C3_W1_Lab_2_Transfer_Learning_CIFAR_10.ipynb
4. Object Localization and Detection
- 1.Network architecture for Object Localization.mp4
- 2.Evaluating Object Localization.mp4
- C3_W1_Lab_3_Object_Localization.ipynb
Graded Quiz
- C3W1_Assignment.ipynb
- Introduction and Concepts of Computer Vision - Quiz.pdf
1. Object Detection
- 1.Object Detection and Sliding Windows.mp4
- 2.R-CNN.mp4
- 3.Fast R-CNN.mp4
- 4.Faster R-CNN.mp4
2. Object Detection in TensorFlow
- 1.Getting the Model from TensorFlow Hub.mp4
- 2.Running the Model on an Image.mp4
- C3_W2_Lab_1_Simple_Object_Detection.ipynb
- C3_W2_Lab_2_Object_Detection.ipynb
3. Object Detection APIs
- 1.Installation and overview of APIs.mp4
- 2.Visualization with APIs.mp4
4. Retraining with the Object Detection API
- 1.Loading a RetinaNet Model.mp4
- 2.Loading Weights.mp4
- 3.Data Prep and Training Overview.mp4
- 4.Custom Training Loop Code.mp4
Graded Quiz
- C3W2_Assignment.ipynb
- Object Detection - Quiz.pdf
1. Image Segmentation Overview
- 1.Image Segmentation Overview.mp4
- 2.Popular Image Segmentation Architectures.mp4
- 3.FCN Architecture Details.mp4
- 4.Upsampling Methods.mp4
- 5.Encoder in Code.mp4
- 6.Decoder in Code.mp4
- 7.Evaluation with IoU and Dice Score.mp4
- C3_W3_Lab_1_VGG16_FCN8_CamVid.ipynb
2. U-Net
- 1.U-Net Overview.mp4
- 2.U-Net Code Encoder.mp4
- 3.U-Net Code Decoder.mp4
- C3_W3_Lab_2_OxfordPets_UNet.ipynb
3. Instance Segmentation
- 1.Instance Segmentation.mp4
- C3_W3_Lab_3_Mask_RCNN_ImageSegmentation.ipynb
Graded Quiz
- C3W3_Assignment.ipynb
- Image Segmentation - Quiz.pdf
1. Intro to Visualization and Interpretation
- 1.Why Interpretation Matters.mp4
- 2.Class Activation Maps.mp4
- 3.Fashion MNIST Class Activation Map code walkthrough.mp4
- C3_W4_Lab_1_FashionMNIST_CAM.ipynb
- C3_W4_Lab_2_CatsDogs_CAM.ipynb
2. Saliency
- 1.Saliency.mp4
- C3_W4_Lab_3_Saliency.ipynb
3. Gradients and Class Activation Maps
- 1.GradCAM.mp4
- C3_W4_Lab_4_GradCam.ipynb
4. Improving a model with interpretation
- 1.ZFNet.mp4
Graded Quiz
- C3W4_Assignment.ipynb
- Visualization and Interpretation - Quiz.pdf
1. Style Transfer Intro
- 1.Welcome to Course 4.mp4
- 2.Style Transfer Intro.mp4
- 3.Style Transfer Conceptual Overview.mp4
- 4.Pre-Processing Inputs.mp4
- 5.Extracting Style and Content Features.mp4
- 6.Total Loss and Content Loss.mp4
- 7.Style Loss.mp4
- 8.Update the Generated Image.mp4
- 9. Optional - Gram Matrix.mp4
- 10.Optional - Einstein Notation.mp4
- 11.Optional - Einsum in Code.mp4
- C4_W1_Lab_1_Neural_Style_Transfer.ipynb
2. Total Variation Loss
- 1.Total Variation Loss .mp4
- C4_W1_Lab_1_Neural_Style_Transfer (1).ipynb
3. Fast Neural Style Transfer
- 1.Fast Neural Style Transfer.mp4
- C4_W1_Lab_2_Fast_NST.ipynb
Graded Quiz
- C4W1_Assignment.ipynb
- Style Transfer - Quiz.pdf
1. What are AutoEncoders
- 1.Introduction.mp4
- 2.First AutoEncoder.mp4
- 3.MNIST AutoEncoder.mp4
- C4_W2_Lab_1_FirstAutoEncoder.ipynb
- C4_W2_Lab_2_MNIST_Autoencoder.ipynb
2. Deep AutoEncoders
- 1.MNIST Deep AutoEncoder.mp4
- 2.Convolutional AutoEncoder.mp4
- 3.Denoising with an AutoEncoder.mp4
- C4_W2_Lab_3_MNIST_DeepAutoencoder.ipynb
- C4_W2_Lab_4_FashionMNIST_CNNAutoEncoder.ipynb
- C4_W2_Lab_5_FashionMNIST_NoisyCNNAutoEncoder.ipynb
Graded Quiz
- AutoEncoders - Quiz.pdf
- C4W2_Assignment.ipynb
1. Variational AutoEncoders
- 1.Variational AutoEncoders Overview.mp4
- 2.VAE Architecture and Code.mp4
- 3.Sampling Layer and Encoder.mp4
- 4.Decoder.mp4
- 5.Loss Function and Model Definition.mp4
- 6.Train the VAE Model.mp4
- C4_W3_Lab_1_VAE_MNIST.ipynb
Graded Quiz
- C4W3_Assignment.ipynb
- Variational AutoEncoders - Quiz.pdf
1. What are GANs
- 1.Introduction.mp4
- 2.First GAN Architecture.mp4
- 3.First GAN Training Loop.mp4
- C4_W4_Lab_1_First_GAN.ipynb
2. Deep GANs
- 1.DCGANs.mp4
- 2.Face Generator.mp4
- 3.Face Generator Discriminator.mp4
- 4.Conclusions.mp4
- C4_W4_Lab_2_First_DCGAN.ipynb
- C4_W4_Lab_3_CelebA_GAN_Experiments.ipynb
Graded Quiz
- C4W4_Assignment.ipynb
- GANs - Quiz.pdf

C1_W3_Lab_3_custom-layer-activation.ipynb

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TensorFlow Advanced Techniques Specialization

C1_W3_Lab_3_custom-layer-activation.ipynb

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