The Complete Recurrent Neural Network with Python Course

Explore the recurrent neural network and its memory of the previous state for time series data like stock prices and handwriting recognition, and contrast it with convolutional networks.

Learn how long short-term memory networks (LSTM) remember past states to capture long-range dependencies and improve sequence understanding in neural networks.

Develop a Bitcoin prediction model using a sequential LSTM with one unit to forecast four days ahead, including data import and model compilation.

Develop a bitcoin price predictor using a recurrent neural network in python, covering data loading, train-test split, preprocessing, scaling, and training with batch size and epochs.

Learn how a recurrent neural network with Python predicts bitcoin prices using normalization, reshaping inputs, and plotting real versus predicted values. Note the caveat: predictions rely on a stationary environment.

Build and train a 50-neuron recurrent neural network in Python using a Keras sequential model to predict Apple stock prices from a prepared dataset with 60 previous instances.

Train a 50-neuron recurrent neural network to predict Apple stock price from 60 previous days, part 2, and visualize real versus predicted prices with very low loss on test data.

Compare apple stock price prediction with a 100-neuron network to a 50-neuron setup, showing improved accuracy but longer compute time, and plan regularization for future videos.

Explore stock price prediction for Microsoft using a recurrent neural network, and assess how added regularization affects model performance by comparing predictions with and without regularization, and analyzing results.

Experiment with a Python-based recurrent neural network for Microsoft stock price prediction using 100 neurons, compare results to 50 and 25 neuron configurations, and analyze model accuracy.

Explore natural language processing and sentiment analysis by classifying documents based on opinions and emotions, and understand how sentiment mining informs reviews and recommendations.

Import pandas as pd, rename a data column for convenience, and create a sentiment column. Confirm the dataset has 50,000 rows and two columns, with sentiment labels 1 or 0.

continue the movie sentiment analysis project part 2 by building a bag-of-words representation: create a vocabulary of unique tokens and encode each document into a feature vector by token frequencies.

Demonstrates building a bag-of-words sentiment analysis pipeline for movie reviews, including vocabulary creation, transforming text into feature vectors with fit_transform, and analyzing frequency within documents.

Explore unigram and bigram models for text data and build tf-idf features using term frequency and inverse document frequency, applying a transformer to produce tf-idf representations.

This lecture demonstrates a movie sentiment analysis pipeline, preprocessing text with tf-idf features, removing hashed emails and punctuation using regular expressions, and applying a Python preprocessor to reviews.

Tokenize movie reviews by splitting text into words, then apply stop-word removal and stemming to reduce words to roots. Prepare a tokenized, stemming-aware pipeline for movie sentiment analysis projects.

Train a logistic regression model for movie sentiment analysis using 25,000 training and 25,000 testing documents, with tokenization, tf-idf features, and cross-validation to optimize parameters.

Continue building a movie sentiment analysis classifier using tf-idf features and logistic regression. Achieve about 90% test accuracy on 50,000 movie reviews and explore memory-efficient training with mini-batch loading.

Learn to build a movie sentiment analysis workflow by tokenizing text, removing stop words, streaming documents, encoding with a hashing trick, and training a logistic regression classifier on text features.

Continue the movie sentiment analysis project in the complete recurrent neural network with Python course, training on minibatches and exploring word vectors, achieving 87% accuracy.

Explore Latent Dirichlet Allocation in a movie sentiment analysis project: set topic count manually, limit features to 5000, apply fit_transform, and compare results.

Complete the movie sentiment analysis project by training, running LDA to derive ten topics, and extracting top picks, including horror movies, family films, and movies based on books.

Learn to build a simple RNN with an embedding layer for the IMDb project using a sequential model. Convert sentences to vectors via a vocabulary index and embedding dimensions.

Load the IMDB dataset with a 10,000-word vocabulary, exclude the top 20 common words, and prepare training and testing sets for an RNN to classify positive or negative reviews.

Train the recurrent network for the IMDb project part 3 using TensorFlow, configure loss, epochs, and validation data, and document the training process in a blog.

Continue the IMDB project by analyzing model results, plotting performance, and evaluating classification with accuracy, confusion matrix, and threshold optimization for balanced metrics.

Build and optimize a short term memory network for the IMDB project, tuning the threshold to maximize TPR and minimize NPR, achieving about 84.12% accuracy.

An introduction to the MNIST project Part 1 using TensorFlow, loading and normalizing data, building a neural network with dense layers and activation functions, and debugging the model.

Continue the mnist project by using a photo encoder to treat a 28x28 image as a sequence of rows for recurrent processing. Train with binary cross-entropy and no embedding layer.

Continue with the mnist project part 3 by plotting results after about 147 epochs, testing with the test set, and analyzing 2d vectors and predictions from a no supervision model.

Continue building a bidirectional encoder–decoder for the MNIST project, import TensorFlow, connect the encoder and decoder, and run training in this RNN course lecture.

In the MNIST project final part, we run and compare models, show improved detail from vectors to sequences, and explore a second sequence-to-sequence encoder–decoder setup for hand written numbers.

Keep practicing to excel in deep learning and set your own goals to become a successful deep learning engineer, using data from Cargill and UCI Rice Bowl to build models.