Deep Learning - Recurrent Neural Networks with TensorFlow

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Deep Learning - Recurrent Neural Networks with TensorFlow

• Course Info
• Curriculum
• Instructors

Description

What you'll learn

• Identify the fundamental concepts and structures of Recurrent Neural Networks
• Implement autoregressive linear models and RNNs for time series prediction in TensorFlow
• Assess the performance of RNN models in real-world applications, including stock return prediction and image classification
• Develop and fine-tune RNN models for complex tasks, such as text classification and long-distance sequence prediction

Course Curriculum

Welcome

• Introduction
• Outline

Recurrent Neural Networks (RNNs), Time Series, and Sequence Data

• Sequence Data
• Forecasting
• Autoregressive Linear Model for Time Series Prediction
• Proof That the Linear Model Works
• Recurrent Neural Networks (Elman Unit Part 1)
• Recurrent Neural Networks (Elman Unit Part 2)
• RNN Code Preparation
• RNN for Time Series Prediction
• Paying Attention to Shapes
• GRU and LSTM (Part 1)
• GRU and LSTM (Part 2)
• A More Challenging Sequence
• Demo of the Long-Distance Problem
• RNN for Image Classification (Theory)
• RNN for Image Classification (Code)
• Stock Return Predictions Using LSTMs (Part 1)
• Stock Return Predictions Using LSTMs (Part 2)
• Stock Return Predictions Using LSTMs (Part 3)
• Other Ways to Forecast
• Suggestion Box

Natural Language Processing (NLP)

• Embeddings
• Code Preparation (NLP)
• Text Preprocessing
• Text Classification with LSTMs

Instructors

Dhanvika Y

Experienced instructor passionate about teaching and sharing knowledge.