Keras: Practical AI Projects & Deep Learning using Keras

Learn how to use Google Colab to run Python notebooks with GPU or TPU support, create code and text blocks, mount drive, and upload data for learning projects using IMDb.

load the IMDb dataset from Keras, set a vocab size of 10,000, download the training and test data, and explore the word index and tokenized text.

Pad IMDb reviews to a fixed 500-word length using zero padding, after analyzing max and min lengths, and prepare a Keras model with a TPU strategy.

Build a basic lstm model with an embedding layer to analyze sentiment and measure accuracy. Configure tpu for faster training and plan to boost accuracy by adding more layers.

Build a sequential model with an embedding layer, a 100-unit lstm, and a sigmoid dense output. Train on a tpu with batch size 64 for three epochs using validation set.

Build sentiment analysis model with a 100-unit lstm, embedding, and sigmoid output; train 3 epochs with batch 64, plot accuracy and loss with matplotlib, and evaluate on 25k test set.

Explore building and evaluating a one-layer LSTM model in keras for sentiment analysis. Learn text preprocessing with pad_sequences and text to sequences, and predict class probabilities on IMDb reviews.

Design a complex text sentiment model using embedding, conv1d, max pooling, dropout, and an LSTM with 100 units, train it, and compare training and validation performance.

Build a complex Keras model with embedding, dropout, convolution, and LSTM; train and evaluate its accuracy, and save the trained model for future use.

Develop a binary image classification project in Keras using a pre-trained VGG16 model from ImageNet, with CNN layers, max pooling, image preprocessing and augmentation, and transfer learning.

Explore Google Colab, a cloud platform to write Jupyter notebooks and run code on Google Compute Engine with GPU acceleration. Create notebooks and execute code with shift enter.

Upload and prepare a 4000-image dogs and cats dataset in Google Drive for a Keras image classification project, then mount Drive in Colab, unzip, and organize training and validation folders.

Unzip and organize the cats-dogs dataset by creating training, validation, and test folders, then count images with the OS library to verify the dataset.

Learn how to leverage a pretrained Vgg16 model in keras with transfer learning to classify cats and dogs from a limited dataset, using imagenet features and colab workflow.

Load test image, preprocess it (image to array, expand dims, rescale to 0–1). Visualize intermediate layer outputs with an activation model, showing how 64 filters in block one learn features.

Extend the VGG16 base with added fully connected layers and compile and train a binary cat-vs-dog classifier using flow from directory with image augmentation, rescaling, rmsprop 0.001, and early stopping.

Train a model built on top of the Vgg16 pre-trained model with 224x224 images, achieve about 92% validation accuracy over 50 epochs, and plot training versus validation accuracy and loss.

Evaluate test performance with a Keras test generator reading 224×224 images for a binary classifier, report about 49% accuracy on 1000 test images, and visualize sample predictions.

Fine-tune a pre-trained vgg16 base on ImageNet by unfreezing the last four layers and training with added top layers to improve accuracy through transfer learning.

Build an advanced facial recognition app with Keras, using pre-trained models, learning image processing best practices, face detection, and svm-based identity prediction from face embeddings.

Train a deep neural network with Keras backend, learn image preprocessing, and build a CNN that tags fashion images with bounding boxes using pre-trained models.

Learn how to preprocess image data for Keras models by reading and decoding image files to RGB, converting to floats, and rescaling pixel values using the Keras image data generator.

Learn to detect faces and draw bounding boxes using the multitask cascaded convolutional networks with TensorFlow, and set up with Keras and OpenCV for a fashion image tagging case study.

Learn to load and inspect color image data in a TensorFlow/Keras workflow, including reading with plt.imread and using a pre-trained MTCNN Mdcn model for face detection.

Use a pre-trained empty cnn detector (mtcnn) to identify faces in an image, returning bounding boxes and facial key points for each face; the example detects three faces.

Load the image, get the current axis, and draw blue, non-filled bounding boxes for each detected face using matplotlib patches in the draw box function.

Draw bounding boxes on faces and visualize key points such as eyes, nose, and mouth by plotting circles on detected face coordinates with matplotlib, updating the output.

Learn to read images from a test folder, build complete image paths, and use a CNN-based MDC engine to detect faces by drawing bounding boxes and keypoints.

Learn to extract faces from images using bounding boxes, compute x1,y1 and x2,y2, and render each face in subplots using a draw_faces function and detector.

Explore the fashion dataset by examining train and validation folders and extracting 160 by 160 color faces using the extract_face function to prepare data for a classifier.

Load faces by implementing load_faces to traverse a directory, extract faces, and return a numpy array for training a supervised deep neural network using folder names as labels.

Learn to load a dataset from folders by iterating subdirectories, extracting faces with a load_faces function, labeling data by subfolder name, and returning numpy arrays.

Load dataset from fashion data set train folders to extract face images into x_train and y_train with 160 by 160 RGB, then save as fashion_data_set.npz and prepare x_val and y_val.

Learn to generate face embeddings from a prepared dataset using a pre-trained facenet keras model. The lecture covers loading data, standardizing pixels, expanding dimensions, and producing 128-dimensional embeddings with model.predict.

Generate face embeddings for each face image using get_embedding(model, image), producing embedding vectors. Save these embeddings as numpy arrays using savez_compressed to prepare them for classification.

Load saved face embeddings from a compressed npz file, normalize with an L2 normalizer for unit-length vectors used in euclidean distance classification, and prepare train and validation data.

Encode string labels with label encoder, train an SVC classifier with a linear kernel, and evaluate accuracy on training and validation data in a face recognition workflow.

Train an SVM model on a fashion dataset, generate embeddings, and test a random image to predict its label with a confidence score.

Integrate pre-trained FaceNet into a Keras sequential model, freeze its weights, add dense layers, and train with image data generators on 160 by 160 inputs for five-class softmax output.

Learn to build a fashion site face tagging system with bounding boxes and FaceNet embeddings plus an SVM classifier, and master threshold tuning for real-world reliability.