TensorFlow Hub: Deep Learning, Computer Vision and NLP

Explore TensorFlow Hub through hands-on projects in image classification, object detection, style transfer, image extension with GANs, and action recognition in videos; prepare for next lecture on implementations.

Learn to implement image classification in Google Colab using GPU acceleration and TensorFlow Hub prebuilt models. Explore CNN architectures and image sizes, with datasets like COCO and ImageNet.

Load the five-class flower dataset in TensorFlow, create training and validation splits, and prepare a 512 by 512 batched dataset for EfficientNet.

Learn how to prepare and normalize image data for TensorFlow Hub, applying rescaling 0–1 and image augmentation—rotation, translation, zoom, and flips—for training, validation, and testing.

Train a neural network with a pre-trained TensorFlow hub model to classify five flower species, using dropout and five training epochs with loss and accuracy visualization.

learn how to preprocess images for classification, including normalization and batch handling, visualize pixel data and labels, and use argmax to interpret predictions with sunflowers and roses as examples.

Explore object detection in images using TensorFlow Hub, detecting more than 80 images, and learn to set up a Colab workflow with CPU and GPU options.

Load and visualize a color image in Google Colab, prepare it for TensorFlow, and run an object detector to identify over 80 items such as cars, people, and backpacks.

Detect objects with a pretrained TensorFlow Hub model, visualize boxes and labels, and filter results by confidence to compare detections such as cars, people, and traffic lights.

Explore style transfer using TensorFlow Hub to apply a painting's style to a content image with convolutional neural networks and transfer learning, via a simple Google Colab workflow.

Prepare content and style images for style transfer by loading them in Google Colab, resizing with preserved aspect ratio. Visualize the content and style images for neural network input.

Learn style transfer with TensorFlow Hub by loading a prebuilt module to apply a style image to a content image, then visualize results in a normalized batch output.

Explore image extension with a prebuilt generative adversarial network to complete missing image regions, using TensorFlow Hub to load, preprocess, and run the model on sample images.

Finish implementing the GAN for image extension and load and test TensorFlow Hub modules to compare masked and completed outputs across boundless half, boundless quarter, and boundless three quarters.

Explore implementing action recognition in videos using a pre-built neural network in Google Colab, install OpenCV, load kinetics label map, and fetch videos to detect multiple actions.

Develop a load video function to download videos to the temporary folder, manage the cache path, and prepare data for action recognition.

Learn to load and save videos with OpenCV, visualize frames, resize to 224 by 224, normalize to 0 to 1, and prepare frames via mp4 conversion for action recognition.

Load a TensorFlow Hub action recognition module, feed 251 frames of video, and derive probabilities to identify top actions from a 400-action kinetics dataset.

Explore a plan of attack for three TensorFlow Hub projects: sentiment analysis on movie reviews, emotion classification, information retrieval for similar documents, and audio classification of outdoor sounds.

Perform text classification on IMDb reviews using a train/test split, building a neural network with TensorFlow and TensorFlow Hub in a Google Colab environment.

Build and train a custom neural network in TensorFlow using an embedding layer to convert text into numbers, then evaluate with 84% test accuracy.

Demonstrates text classification of movie reviews with a neural network, converting logits to probabilities using sigmoid, applying a 0.5 threshold, and evaluating results with TensorFlow Hub embeddings.

This lecture introduces a case study on information retrieval and guides you to connect a notebook to Google Drive and install TensorFlow Hub and TensorFlow Text with simple neighbors.

Explore the squared dataset for information retrieval and question answering, load and parse the json, extract sentences and contexts, and prepare qa pairs for algorithm development.

Implement a QA data extraction workflow to parse data, root tag, and paragraph tags, extract questions and first answers, and prepare unique question–answer pairs for information retrieval.

Build an information retrieval index with TensorFlow hub and the multilingual universal sentence encoder. Encode sentences into 512-dimensional embeddings and index them for fast nearest neighbor lookups.

Visualize information retrieval by converting questions to embeddings, indexing sentences, and returning the ten nearest results using the nearest function from the Simple Neighbors Library, with questions and answers displayed.

Explore audio classification using a TensorFlow Hub model trained on the audio set in Google Colab, listing 521 classes, loading a cat wav file, and normalizing data for inference.

Feed audio data into a TensorFlow Hub model, obtain 521-class probabilities across multiple frames, compute mean scores, and identify the top audio predictions.

Explore the biological fundamentals of human neural networks, including more than 100 billion neurons, their interconnections, and how electrical signals flow through dendrites, cell bodies, and axons to drive learning.

Learn how a perceptron processes inputs with weights and a sum function, uses a step activation to produce binary outputs, and trains on and xor with weight updates.

Explore multilayer perceptrons with a hidden layer, weight connections, and feedforward computation using a sigmoid activation to classify non-linearly separable problems.

Calculate the loss function by comparing neural network predictions with expected outputs. See how weight updates reduce error and enable learning with the sigmoid activation.

explore gradient descent for neural networks: learn how partial derivatives guide weight updates to minimize error, reach global minima, and understand local minima, with sigmoid derivative as a calculation example.

Compute the delta parameter for backpropagation using the sigmoid activation and partial derivative to guide weight updates from output to hidden layers.

Update weights in a multilayer neural network using backpropagation, propagating from the output to the input layer with gradients, delta, momentum, and a learning rate to minimize error.

Explore bias units and how they shift activations across neural network layers. Compare mse and rmse as error metrics and cover batch, stochastic, and mini-batch gradient descent.

Explore convolutional neural networks for computer vision tasks like object detection and facial recognition, and learn how they automatically extract impactful features for autonomous cars.

Understand the convolution operation in cnn models by multiplying a 7x7 image with a 3x3 feature detector to form a feature map, sliding across the image and applying activation.

Learn how pooling, especially max pooling, reduces feature maps to the most important pixels, enabling robust dog recognition across diverse scenes in a convolutional neural network.

Flatten the max-pooled Pwds feature map into a vector to feed a convolutional neural network's dense layer with two hidden layers and sigmoid output for digit classification.

Explore how the dense neural network classifies digits 1, 3, and 9 by learned weights driving output neuron activations, after flattening cnn features and applying max pooling.

Recap the course coverage of TensorFlow Hub across deep learning, computer vision, and natural language processing, including image classification, video action recognition, style transfer, and text classification with information retrieval.

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