Sentiment Analysis through Deep Learning with Keras & Python

Define machine learning practically, contrast it with deep learning, and describe supervised learning with training and test sets, regression, classification, ground truth, and deployable edge models.

Learn regression fundamentals by building a simple linear model that predicts house price from area, using data points and mean squared error to fit theta zero and theta one.

Explore classification with neural networks, moving from binary to multi-class, using sigmoid squashing for 0–1 outputs, and building deep networks with hidden layers for a modular approach and high-level decisions.

Explore sentiment analysis with deep learning in Keras and Python, recapping model fitting and error minimization, then from simple classification to deep neural networks with multiple hidden layers.

Learn to install and configure a Windows-based deep learning environment for sentiment analysis with Keras and Python, including 64-bit Windows, Anaconda, Python 3.6, virtualenv, and Jupyter notebook setup.

Learn to set up a sentiment analysis environment on macOS or Linux with Anaconda, Python 3.6, a virtual environment, and a Jupyter notebook.

Prepare image data for a keras model by loading a dataset, flattening 28×28 images to 784 vectors, normalizing pixel values to 0–1, and converting labels to one-hot encodings.

Learn how to build a simple keras sequential model with dense layers, activation functions, and a softmax output, then compile, fit, and evaluate it to improve accuracy.

Analyze sentiment with deep learning in Keras and Python by loading the idb dataset, examining data shapes, and building word-index and reverse mappings for converting between sentences and sequences.

Encode sentences into sequences of word indices using a word-to-ideas dictionary. Pad inputs with zeros and clip longer ones to a fixed maximum, such as 400, to handle unknown words.

Train a sequential model with an embedding layer to map words to vectors, then predict sentiment for reviews and tweets, demonstrating real-world applications and common negation challenges.

Learn convolutional neural networks, including filters, padding, and stride, to detect robust features in color images and classify CIFAR-10 images.

Learn to build a convolutional neural network with Keras, using Conv2D on 32x32x3 images, 16 filters, relu activations, flattening, and a 10-class dense output trained with categorical cross-entropy.

Explore max pooling with a 2x2 pool size to reduce feature map size and boost training speed, illustrating translation invariance and noting that average pooling is an alternative.

Apply dropout to neural network layers to prevent overfitting and improve generalization by randomly setting a fraction of activations to zero during training.

Learn to convert a CNN to the Keras functional API, building a 32x32x3 input CNN with conv layers, pooling, dropout, flatten, and softmax. Prepare for models using merging and concatenation.

Examine sentiment analysis with cnn and lstm architectures, embedding, 1d convolution, dropout, global max pooling, dense sigmoid output, and binary cross-entropy, then compare lstm variants.

Save and load model weights via a model checkpoint callback, saving to a checkpoints folder with epoch and validation accuracy, then reload weights to resume or deploy.

Explore essential resources for mastering deep learning with keras and python, including official documentation, blogs, and tutorials to strengthen sentiment analysis skills.