Learn about deep learning models and implement them using the Keras library in Python with Theano as the backend. Explore deep neural networks, activation functions, convolutional neural networks, word embeddings, recurrent neural networks, and LSTM. Develop a chatbot using Word2vec and LSTM. Gain hands-on experience with various deep learning models and practices through Keras tutorials. Understand the fundamentals of deep learning, a branch of machine learning that models high-level abstractions in data. Cover topics such as CNN implementation, RNN and LSTM networks, Word2Vec techniques, and activation functions in neural networks. Build practical skills in preprocessing text for LSTM inputs and creating deep learning chatbots.
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
