TensorFlow 2.0 Practical Advanced

Explore the basics of artificial neural networks and convolutional neural networks, covering dense feed-forward networks, feature detectors, kernels, relu, pooling, gradient descent, and backpropagation.

Explore a pre-trained convolutional neural network that classifies digits through convolutional and downsampling layers, flattening, dropout regularization, and a dense output for ten classes.

Discover supervised, unsupervised, and reinforcement learning for neural networks, including data labeling, ground truth, epochs, and training, validation, and testing splits to prevent overfitting.

Explore building and training a dense, fully connected neural network in the dancefloor playground to classify two classes, with learning rate, relu activation, and weight updates.

Apply gradient descent to minimize the loss in feed-forward and convolutional neural networks by updating weights and biases using the learning rate and calculated gradients to reach the global minimum.

Learn back propagation to train neural networks by performing forward propagation, calculating errors, and updating weights with gradients and learning rate, while using adaptive learning rates to avoid overshoot.

Explore the bias-variance tradeoff in regression by comparing linear and high-order polynomial models, assessing training versus testing performance to identify an optimal model with good generalization.

Explore confusion matrices, true positives, true negatives, false positives, and false negatives, and key performance indicators—accuracy, misclassification rate, precision, recall, and type one and type two errors in imbalanced data.

Walks through the project 1 solution notebook for TensorFlow 2.0 practical advanced, loading data, inspecting train and test shapes, and visualizing sample images with labels from CIFAR-10.

learn practical advanced image classification with tensorflow 2.0 and keras, covering data normalization, categorical formatting, cnn architecture, training, and model evaluation.

Explore transfer learning, reusing pretrained neural networks as a starting point to train new tasks, reducing training time by repurposing weights and tweaking features.

Learn to reuse pre trained weights from image net by freezing convolutional layers as general feature extractors, while training new dense layers for a specific task like cats and dogs.

Explore transfer learning strategies in TensorFlow 2.0 practical advanced, including freezing the first layers of a CNN and training dense layers, or retraining entire network with a small learning rate.

Explore ImageNet, an open source dataset of about 1.5 million images across 1000 classes, used as a benchmark for transfer learning with pretrained networks.

Harness transfer learning by loading a pre trained network from Image Net Dataset and attaching a new dense classifier to classify cats and dogs, with freezing and fine tuning.

Explore transfer learning by importing a pretrained model with imagenet weights, preprocessing inputs, and evaluating predictions on sample images using keras applications in Colab.

Apply transfer learning by building a base ResNet-50 model with ImageNet weights, exclude the top, add a custom classifier for a two-class task, and retrain.

Use transfer learning in TensorFlow 2.0 practical advanced to build a two-class cat and dog classifier with a base model, global average pooling, and a dense head.

Transfer learning with a net 50 base model, adding trainable dense layers for a cat vs dog classifier. Train on about 200 images across five epochs, achieving around 98 percent accuracy.

Apply transfer learning with dense flow 2.0 via TensorFlow Hub to load a pre-trained mobile model and perform image classification on the flowers dataset.

Evaluate a pre-trained mobile net with tf-hub, preprocess and predict image batches, map predictions to image net labels, and assess transfer learning on a flowers dataset.

Apply transfer learning with a mobile net feature extractor and a dense classification head to retrain a model for five flower classes, achieving high accuracy after targeted training.

Explore autoencoders with encoder–decoder networks, bottlenecks, and representation learning by training on identical input–output data to compress and reconstruct images in an unsupervised setting.

Learn how autoencoders use an encoder–decoder with a bottleneck to produce encoded inputs and reconstruct them, minimizing reconstruction error with regularization and, when linear, resembling PCA.

Compare linear autoencoders with principal component analysis to perform dimensionality reduction, highlighting equivalent behavior with linear activation, reconstruction error trade-offs, and regularization for better generalization.

Explore autoencoders applications such as image denoising, image compression, image search, and anomaly detection, using encoder-decoder networks to reconstruct noisy inputs and detect anomalies via reconstruction loss.

Variational autoencoders turn discrete latent spaces into continuous distributions by encoding mean and standard deviation. They enable sampling and smoother image generation, comparing to GANs for adding glasses to faces.

Train an autoencoder with noisy and clean image pairs to perform noise removal, learning a bottleneck code that compresses correlated data in the digits dataset using TensorFlow 2.0.

Apply autoencoders to denoise images by adding controlled noise, normalize data, and create noisy training and testing datasets, then visualize results and set up TensorFlow 2.0 workflows.

Build an autoencoder with encoder and decoder layers using convolutions and upsampling to reconstruct 28x28 images from noisy data, trained with binary entropy and the Adam optimizer.

Evaluate a trained autoencoder on noisy test images to produce clean 28 by 28 outputs, demonstrating effective denoising of unseen data.

Learn to build color image autoencoders with TensorFlow 2.0 practical advanced, compressing images using an encoder bottleneck and decoder to produce reconstructed images, exploring unsupervised learning on traffic sign data.

Preprocess the traffic sign data, build a convolutional encoder, train autoencoders, compare two architectures, and visualize reconstructed images to observe bottlenecks and the effect of upsampling.

Maximize activations across selected CNN layers in TensorFlow 2.0 to iteratively modify input images, transforming edges into high-level features and dreamlike art using the deep dream algorithm.

Demystify the dream concept by applying it to a fixed, pre-trained network in Google Colab, turning an input image into activations from a chosen layer via gradient descent.

Feed a trained neural network an image, select a layer, and apply gradient descent to increase activations, creating dream like visualizations of the network's detected features.

Build a deep dream pipeline with TensorFlow 2.0, load a pre-trained Inception v3 on ImageNet, select mixed3 and mixed5, and maximize activations on a sample image after preprocessing.

Feed a color image into a pre-trained inception model, preprocess with Keras, expand to batch, visualize mixed3 and mixed5 activations, and compute deep dream loss to create artwork.

Maximize activations by calculating a loss from selected layers of an Inception network, using gradient ascent. Sum per-layer losses into a single score and test different four-layer selections.

Explore the intuition behind generative adversarial networks, where a generator and a discriminator compete to produce images indistinguishable from real data, using gradient descent and a collaborative learning loop.

Explore the generator and discriminator networks in a gan, focusing on random noise inputs, fake versus real images, and their binary classification roles.

Learn to train a GAN by alternating discriminator and generator updates: use random noise, real and fake images, binary classification, and gradient descent feedback to improve realism.

Explore cutting-edge generative adversarial networks in TensorFlow 2.0 practical advanced, learning applications from 2D and 3D image generation to text-to-image and super-resolution.

Generate new images with gan models by training a generator and a discriminator, defining a loss function, and training them from scratch on fashion mnist data.

Build a generator network that converts random noise into 28x28 images using transpose convolutions and upsampling, with batch normalization and leaky relu activations, to fool the discriminator.

Build a GAN discriminator with sequential conv layers to classify real versus fake images, using dropout and a single output neuron for binary probability.

Define the generator and discriminator losses using binary cross entropy. Discriminator learns real versus fake, generator aims to fool it into ones; set up optimizers and checkpoints.

Discover the intuition behind recurrent neural networks, their memory through feedback loops for temporal data, and key applications in text classification, text prediction, NLP, and image recognition.

Explore recurrent neural network architecture with memory of past states fed back as input, unfold the network to visualize time steps and long short term memory.

Explore how recurrent neural networks handle sequences and time, enabling many-to-many, one-to-many, and many-to-one mappings for tasks like language translation, image captioning, and sentiment analysis.

Explore recurrent neural networks mathematics, including internal state updates and output updates, showing how past states influence current outputs and enabling sequence tasks like translation and captioning.

Discover how long short term memory networks address the vanishing gradient problem in recurrent neural networks by using forget, input, and output gates and a memory cell, with TensorFlow 2.0.

Train an lstm network to predict the next character in a text sequence using tensorflow 2.0, emphasizing data preparation, text-to-number mapping, and Shakespeare dataset.

Import libraries and load the Shakespeare dataset, then encode characters to numbers, map vocab to indices, train the lsd network to predict future characters, and decode back to text.

Create training samples by dividing the dataset into 100-character sequences and shifting the output by one character, focusing on data preparation for the lsm network.

Build and train a recurrent neural network with a 65-token vocabulary, 256 embedding dimensions, and 1024 lstm units using keras in tensorflow 2.0, then generate meaningful text after training.

Train and deploy a fashion image classifier on a server with TensorFlow 2.0 serving, saving model versions and testing predictions from 28x28 grayscale images across 10 classes.

Save the trained model in the saved model format with a versioned directory structure for deployment via TensorFlow Serving.

Save and serve your model with TensorFlow 2.0 model server, configuring the export path, port, and model name, and expose input and output shapes.

Demonstrate how to use TensorBoard to visualize training progress, graph architecture, and device placement in TensorFlow 2.0, with log-based monitoring of accuracy and loss across epochs.

Explore building and visualizing a convolutional neural network in TensorFlow 2.0 with TensorBoard, using the fashion dataset, 6 filters of 5x5, pooling, and dense layers, benchmarking against a dense model.

Discover how TensorFlow 2.0 distributed strategy lets you run a single model across multiple GPUs (or CPUs) using mirrored strategy with just two lines of code.