Data Science and Machine Learning Series: Recurrent Neural Networks

Data Science and Machine Learning Series: Recurrent Neural Networks
Data Science and Machine Learning Series: Recurrent Neural Networks
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2h 13m | 306 MB

Follow along with machine learning expert Advait Jayant through a combination of lecture and hands-on and master Recurrent Neural Networks (RNNs).

The following nine topics will be covered in this Data Science and Machine Learning course:

  • Introducing RNNs. Become comfortable with Recurrent Neural Networks (RNNs) during this first topic in the Data Science and Machine Learning Series. From Wikipedia: A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition, or speech recognition. Also learn about sequence models and named entity recognition in this session.
  • RNN Cell. Master the Recurrent Neural Network (RNN) cell during this second topic in the Data Science and Machine Learning Series.
  • RNN Architectures. Become competent with the different types of Recurrent Neural Network (RNN) architectures during this third topic in the Data Science and Machine Learning Series. Follow along with Advait and generate a nested dictionary.
  • Forward Propagation. Perform forward propagation using a Recurrent Neural Network (RNN) during this fourth topic in the Data Science and Machine Learning Series.
  • Backpropagation Through Time (BPTT). Perform Backpropagation Through Time (BPTT) within the Recurrent Neural Network (RNN) during this fifth topic in the Data Science and Machine Learning Series.
  • Word Embeddings and Embedding Layers. Work with word embeddings and embedding layers during this sixth topic in the Data Science and Machine Learning Series.
  • Building a RNN in Keras. Build a Recurrent Neural Network (RNN) in Keras during this seventh topic in the Data Science and Machine Learning Series. Follow along with Advait and work with a large movie dataset and perform movie reviews.
  • Checkpoints and Early Stopping. Create checkpoints and early stopping using a Recurrent Neural Network (RNN) in Keras during this eighth topic in the Data Science and Machine Learning Series.
  • Word2Vec Model. Build a Word2Vec model and use pre-trained word vectors during this ninth topic in the Data Science and Machine Learning Series.
Table of Contents

1 Introducing RNNs
2 RNN Cell
3 RNN Architectures
4 Forward Propagation
5 Backpropagation Through Time (BPTT)
6 Word Embeddings and Embedding Layers
7 Building a RNN in Keras
8 Checkpoints and Early Stopping
9 Word2Vec Model