This course covers learning outcomes and goals related to deep learning models and their implementation using the Keras library in Python with Theano as the backend. The course teaches about deep neural networks, activation functions, convolutional neural networks, word embeddings, recurrent neural networks, LSTM, and creating a chatbot using Word2vec and LSTM. The teaching method includes tutorials and practical implementations of various deep learning models. The intended audience for this course is individuals interested in deep learning concepts and their application using Python and Keras.
Overview
Syllabus
What is Deep Learning ? - Course Introduction.
Convolutional Neural Networks (CNN / Convnets).
Convolutional Neural Networks (CNN) Implementation with Keras - Python.
Recurrent Neural Networks (RNN) and Long Short Term Memory Networks (LSTM).
Recurrent Neural Networks (LSTM / RNN) Implementation with Keras - Python.
Word2Vec - Skipgram and CBOW.
Word2Vec with Gensim - Python.
Deep Learning Chatbot using Keras and Python - Part I (Pre-processing text for inputs into LSTM).
Deep Learning Chatbot using Keras and Python - Part 2 (Text/word2vec inputs into LSTM).
Activation Functions in Neural Networks (Sigmoid, ReLU, tanh, softmax).
Taught by
The Semicolon
