Deep Learning: Recurrent Neural Networks with Python [Video]
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-
Free ChapterIntroduction
-
Applications of RNN
-
Deep Neural Network (DNN) Overview
- Why PyTorch
- PyTorch Installation and Tensors Introduction
- Automatic Differentiation PyTorch New
- Why DNNs in Machine Learning
- Representational Power and Data Utilization Capacity of DNN
- Perceptron
- Perceptron Exercise
- Perceptron Exercise Solution
- Perceptron Implementation
- DNN Architecture
- DNN Architecture Exercise
- DNN Architecture Exercise Solution
- DNN Forward Step Implementation
- DNN Why Activation Function Is Required
- DNN Why Activation Function Is Required Exercise
- DNN Why Activation Function Is Required Exercise Solution
- DNN Properties of Activation Function
- DNN Activation Functions in PyTorch
- DNN What Is Loss Function
- DNN What Is Loss Function Exercise
- DNN What Is Loss Function Exercise Solution
- DNN What Is Loss Function Exercise 02
- DNN What Is Loss Function Exercise 02 Solution
- DNN Loss Function in PyTorch
- DNN Gradient Descent
- DNN Gradient Descent Exercise
- DNN Gradient Descent Exercise Solution
- DNN Gradient Descent Implementation
- DNN Gradient Descent Stochastic Batch Minibatch
- DNN Gradient Descent Summary
- DNN Implementation Gradient Step
- DNN Implementation Stochastic Gradient Descent
- DNN Implementation Batch Gradient Descent
- DNN Implementation Minibatch Gradient Descent
- DNN Implementation in PyTorch
- DNN Weights Initializations
- DNN Learning Rate
- DNN Batch Normalization
- DNN Batch Normalization Implementation
- DNN Optimizations
- DNN Dropout
- DNN Dropout in PyTorch
- DNN Early Stopping
- DNN Hyperparameters
- DNN PyTorch CIFAR10 Example
-
RNN Architecture
- Introduction to Module
- Fixed Length Memory Model
- Fixed Length Memory Model Exercise
- Fixed Length Memory Model Exercise Solution Part 01
- Fixed Length Memory Model Exercise Solution Part 02
- Infinite Memory Architecture
- Infinite Memory Architecture Exercise
- Infinite Memory Architecture Solution
- Weight Sharing
- Notations
- ManyToMany Model
- ManyToMany Model Exercise 01
- ManyToMany Model Solution 01
- ManyToMany Model Exercise 02
- ManyToMany Model Solution 02
- ManyToOne Model
- OneToMany Model Exercise
- OneToMany Model Solution
- OneToMany Model
- ManyToOne Model Exercise
- ManyToOne Model Solution
- Activity Many to One
- Activity Many to One Exercise
- Activity Many to One Solution
- ManyToMany Different Sizes Model
- Activity Many to Many Nmt
- Model Summary
- Deep RNNs
- Deep RNNs Exercise
- Deep RNNs Solution
-
Gradient Descent in RNN
-
RNN Implementation
- Automatic Differentiation
- Automatic Differentiation PyTorch
- Language Modeling Next Word Prediction Vocabulary Index
- Language Modeling Next Word Prediction Vocabulary Index Embeddings
- Language Modeling Next Word Prediction RNN Architecture
- Language Modeling Next Word Prediction Python 1
- Language Modeling Next Word Prediction Python 2
- Language Modeling Next Word Prediction Python 3
- Language Modeling Next Word Prediction Python 4
- Language Modeling Next Word Prediction Python 5
- Language Modeling Next Word Prediction Python 6
-
Sentiment Classification Using RNN
-
Vanishing Gradients in RNN
-
TensorFlow
-
Project 1: Book Writer
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Project 2: Stock Price Prediction
-
Further Reading and Resources
About this
video
With the exponential growth of user-generated data, there is a strong need to move beyond standard neural networks in order to perform tasks such as classification and prediction. Here, architectures such as RNNs, Gated Recurrent Units (GRUs), and Long Short Term Memory (LSTM) are the go-to options. Hence, for any deep learning engineer, mastering RNNs is a top priority.
This course begins with the basics and will gradually equip you with not only the theoretical know-how but also the practical skills required to successfully build, train, and implement RNNs. This course contains several exercises on topics such as gradient descents in RNNs, GRUs, LSTM, and so on. This course also introduces you to implementing RNNs using TensorFlow.
The course culminates in two exciting and realistic projects: creating an automatic book writer and a stock price prediction application. By the end of this course, you will be equipped with all the skills required to confidently use and implement RNNs in your applications.
The code bundle for this course is available at https://github.com/AISCIENCES/mastering_recurrent_neural_networks
- Publication date:
- February 2021
- Publisher
- Packt
- Duration
- 15 hours 35 minutes
- ISBN
- 9781801079167