
Explore advanced recurrent neural networks, including LSTMs and GRUs, with TensorFlow 2.0, requiring Python and familiarity with TensorFlow or Keras, plus feedforward networks and backpropagation.
Learn how NumPy enhances Python for quantitative data analysis and scientific computing, offering high-level mathematical functions to work with numerical datasets in data science.
Explore multidimensional slicing by indexing 2d and 3d arrays, selecting single cells, slicing ranges, using ellipses, step sizes, and boolean conditionals to filter elements.
This is a preview to the exciting Recurrent Neural Networks course that will be going live soon. Recurrent Networks are an exciting type of neural network that deal with data that come in the form of a sequence. Sequences are all around us such as sentences, music, videos, and stock market graphs. And dealing with them requires some type of memory element to remember the history of the sequences, this is where Recurrent Neural networks come in.
We will be covering topics such as RNNs, LSTMs, GRUs, NLP, Seq2Seq, attention networks and much much more.
You will also be building projects, such as a Time series Prediction, music generator, language translation, image captioning, spam detection, action recognition and much more.
Building these projects will impress even the most senior machine learning developers; and will prepare you to start tackling your own deep learning projects with real datasets to show off to your colleagues or even potential employers.
Sequential Networks are very exciting to work with and allow for the creation of very intelligent applications. If you’re interested in taking your machine learning skills to the next level, then this course is for you!