Home Page Icon
Home Page
Table of Contents for
15- Develop an autonomous Agents with Deep R Learning
Close
15- Develop an autonomous Agents with Deep R Learning
by Matthew Lamons, Rahul Kumar
Python Deep Learning Projects
1 - Building Deep Learning Environment
Building a common Deep Learning environment
Get focused and into the code!
Deep Learning environment setup in local
Download and install Anaconda
Installing Deep Learning libraries
Deep Learning environment setup in the cloud
Cloud platforms for deployment 
Prerequisites
Setup the GCP
Automating the setup process
Summary
2 - Training NN for Prediction using Regression
Introduction
Building a regression model for prediction using a multilayer perceptron - A deep neural network
Exploring MNIST dataset
Intuition and preparation
Defining regression
Defining project structure
Let's code the implementation!
Defining hyperparameters
Model definition
Build the training loop
Overfitting and Underfitting 
Building inference
The conclusion to the project
Summary
3 - Word representation using Word2VEC
Learning Word Vectors
Load all the dependencies
Prepare the Text Corpus
Defining Our Word2vec Model
Let's Train The Model
Analysing The Model
Plotting The Word Cluster Using The t-SNE Algorithm
Visualizing the Embedding Space - Plotting the model on Tensorboard
Building language model using CNN + word2vec
Exploring The CNN Model
Understanding Data Format
 Integrating word2vec with CNN
Executing the Model 
Deploy the model into production
Summary
4 - Build NLP pipeline for building chatbots
Basics of NLP pipeline
Tokenisation
Part of speech tagging
Extracting Nouns
Extracting Verbs
Dependency Parsing
Named Entity Recognition
Building conversational bots
What is TF-IDF?
Preparing dataset
Implementation
Creating Vectorizer
Process Query
Rank Results
Advance chatbots using NER
Installing Rasa
Preparing dataset
Train the model
Deploy the model
Serving chatbots
Summary
5 - Sequence-to-sequence models for building chatbots
Introducing RNNs
RNN Architectures
Implementing basic RNN
Importing all the dependencies
Preparing dataset
Hyperparameter
Defining Basic RNN cell model
Training the RNN Model
Evaluation Of the RNN Model
LSTM Architecture
Implementing LSTM Model
Defining LSTM model
Training the LSTM Model
Evaluation of the LSTM model
Sequence-to-Sequence model
Data Preparation
Defining seq2seq model
Hyperparameters
Training the seq2seq model
Evaluation of the seq2seq model
Summary
6 Generative Language model for content creation
LSTM For Text Generation
Data pre-processing
Defining The LSTM Model For Text Generation
Training The Model
Inference and Results
Generate Lyrics using Deep (Multi-layer) LSTM
Data pre-processing
Defining the model
Training the Deep Tensorflow based LSTM Model
Inference
Output
Generate Music using Multi-layer LSTM
Pre-processing data
Define model and Train
Generating music
Summary
14 - Image translation using GANs for style transfer
INTRODUCTION
Let's Code the Implementation!
Importing all the dependencies
Exploring the data
Preparing the data
Type Conversion, Centering and Scaling
Masking / Inserting Noise
Reshaping
MNIST Classifier
Defining Hyperparameters for GAN
Building the GAN Model Components
Defining the Generator
Defining the Discriminator
Defining the DCGAN
Training GAN
Plot the Training  - 1
Plot the Training - 2
Training Loop
Predictions
CNN classifier predictions on the noised and generated images
Scripts in Modular form
Module 1  - train_mnist.py
Module 2 - training_plots.py
Module 3 - GAN.py
Module 4 - train_gan.py
The conclusion to the project
Summary
15- Develop an autonomous Agents with Deep R Learning
INTRODUCTION
Let's get to the Code!
Deep Q Learning
Importing all the dependencies
Exploring the Cart-Pole game
Interacting with Cart-Pole game
Loading the Game
Resetting The Game
Playing the Game
Q - Learning
Defining Hyperparameters for DQN
Building the Model Components
Defining the Agent
Defining the Agent Action
Defining the Memory
Defining Performance Plot
Defining Replay
Training Loop
Testing the DQN model
Deep Q learning Scripts in Modular form
Module 1  - hyperparameters_dqn.py
Module 2 - agent_replay_dqn.py
Module 3 - test_dqn.py
Module 4 - train_dqn.py
Deep SARSA Learning
SARSA Learning
Importing all the dependencies
Loading the game Environment
Defining the agent
Training the agent
Testing the agent
Deep SARSA learning Script in Modular form
The conclusion to the project
Summary
Search in book...
Toggle Font Controls
Playlists
Add To
Create new playlist
Name your new playlist
Playlist description (optional)
Cancel
Create playlist
Sign In
Email address
Password
Forgot Password?
Create account
Login
or
Continue with Facebook
Continue with Google
Sign Up
Full Name
Email address
Confirm Email Address
Password
Login
Create account
or
Continue with Facebook
Continue with Google
Prev
Previous Chapter
Summary
Next
Next Chapter
INTRODUCTION
15- Develop an autonomous Agents with Deep R Learning
Add Highlight
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click
here
login for view all page.
Day Mode
Cloud Mode
Night Mode
Reset