Deep Learning and Reinforcement Learning with Tensorflow

Deep Learning and Reinforcement Learning with Tensorflow, available at $39.99, has an average rating of 3.85, with 48 lectures, 2 quizzes, based on 11 reviews, and has 108 subscribers.

You will learn about Build a base for TensorFlow by implementing regression Solve prediction & Image classification deep learning problems with TensorFlow Utilize the power of efficient data representation using autoencoders Get to know important features of RL that are used for AI Create agents to perform complex tasks using RL Apply Deepmind’s Deep Q-network architecture to improve performance This course is ideal for individuals who are This course is aimed for AI practitioners, aspiring machine learning engineers, data science professionals familiar with Python programming and keen to use TensorFlow for their Deep Learning tasks. It is particularly useful for This course is aimed for AI practitioners, aspiring machine learning engineers, data science professionals familiar with Python programming and keen to use TensorFlow for their Deep Learning tasks.

Enroll now: Deep Learning and Reinforcement Learning with Tensorflow

Summary

Title: Deep Learning and Reinforcement Learning with Tensorflow

Price: $39.99

Average Rating: 3.85

Number of Lectures: 48

Number of Quizzes: 2

Number of Published Lectures: 48

Number of Published Quizzes: 2

Number of Curriculum Items: 50

Number of Published Curriculum Objects: 50

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Build a base for TensorFlow by implementing regression
Solve prediction & Image classification deep learning problems with TensorFlow
Utilize the power of efficient data representation using autoencoders
Get to know important features of RL that are used for AI
Create agents to perform complex tasks using RL
Apply Deepmind’s Deep Q-network architecture to improve performance

Who Should Attend

This course is aimed for AI practitioners, aspiring machine learning engineers, data science professionals familiar with Python programming and keen to use TensorFlow for their Deep Learning tasks.

Target Audiences

This course is aimed for AI practitioners, aspiring machine learning engineers, data science professionals familiar with Python programming and keen to use TensorFlow for their Deep Learning tasks.

Are you short on time to start from scratch to use deep learning to solve complex problems involving topics like neural networks and reinforcement learning? Than this course is for you!

This course is designed to help you to overcome various data science problems by using efficient deep learning models built in TensorFlow. You will begin with a quick introduction to TensorFlow essentials. Next, you start with deep neural networks for different problems and also explore the applications of Convolutional Neural Networks on two real datasets. We will than walk you through different approaches to RL. You’ll move from a simple Q-learning to a more complex, deep RL architecture and implement your algorithms using Tensorflow’s Python API. You’ll be training your agents on two different games in a number of complex scenarios to make them more intelligent and perceptive.

By the end of this course, you’ll be able to implement RL-based solutions in your projects from scratch using Tensorflow and Python. Also you will be able to develop deep learning based solutions to any kind of problem you have, without any need to learn deep learning models from scratch, rather using tensorflow and it’s enormous power.

Contents and Overview

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Hands-on Deep Learning with TensorFlow is designed to help you to overcome various data science problems by using efficient deep learning models built in TensorFlow.The course begins with a quick introduction to TensorFlow essentials. Next, we start with deep neural networks for different problems and then explore the applications of Convolutional Neural Networks on two real datasets. If you’re facing time series problem then we will show you how to tackle it using RNN. We will also highlight how autoencoders can be used for efficient data representation. Lastly, we will take you through some of the important techniques to implement generative adversarial networks. All these modules are developed with step by step TensorFlow implementation with the help of real examples.By the end of the course you will be able to develop deep learning based solutions to any kind of problem you have, without any need to learn deep learning models from scratch, rather using tensorflow and it’s enormous power.

In the second course, Hands-on Reinforcement Learning with TensorFlow will walk through different approaches to RL. You’ll move from a simple Q-learning to a more complex, deep RL architecture and implement your algorithms using Tensorflow’s Python API. You’ll be training your agents on two different games in a number of complex scenarios to make them more intelligent and perceptive.

By the end of this course, you’ll be able to implement RL-based solutions in your projects from scratch using Tensorflow and Python.

About the Authors

Salil Vishnu Kapur is a Data Science Researcher at the Institute for Big Data Analytics, Dalhousie University. He is extremely passionate about Machine Learning, Deep Learning, Data mining and Big Data Analytics. Currently working as a Researcher at Deep Vision and prior to that worked as a Senior Analyst at Capgemini for around 3 years with these technologies. Prior to that Salil was an intern at IIT Bombay through the FOSSEE Python TextBook Companion Project and presently with the Department of Fisheries and Transport Canada through Dalhousie University.

Satwik Kansal is a Software Developer with more than 2 years experience in the domain of Data Science. He’s a big open source and Python aficionado, currently the top-rated Python developer in India, and an active Python blogger. Satwik likes writing in-depth articles on various technical topics related to Data Science, Decentralized Applications, and Python. Apart from working full time as a software engineer, you may find him guest blogging for IBM DeveloperWorks and Learndatasci, freelancing, participating in Hackathons, or attending tech-conferences.

Course Curriculum

Chapter 1: Hands-on Deep Learning with TensorFlow

Lecture 1: The Course Overview

Lecture 2: TensorFlow for Building Deep Learning Models

Lecture 3: Basic Syntaxes, Function Optimization, Variables, and Placeholders

Lecture 4: TensorBoard for Visualization

Lecture 5: Start by Loading the Imported Dataset

Lecture 6: Building the Layers of the Neural Network in TensorFlow

Lecture 7: Optimizing the Softmax Cross Entropy Function

Lecture 8: Using DNN Predicting Whether Breast Cancer Cells Are Benign or Not

Lecture 9: Importing the Two Datasets Using TensorFlow and Sklearn API

Lecture 10: Writing the TensorFlow Code to Add Convolutional and Pooling Layers

Lecture 11: Using tf.train.AdamOptimizer API to Optimize CNN

Lecture 12: Implementing CNN to Create a Face Recognition System

Lecture 13: Understanding the RNN and the Need for LSTM

Lecture 14: Implementing RNN

Lecture 15: Monthly Riverflow Prediction of Turtle River in Ontario

Lecture 16: Implement LSTM Project to Predict Decimal Number of Given Binary Representation

Lecture 17: Encoder and Decoder for Efficient Data Representation

Lecture 18: TensorFlow Code Using Linear Autoencoder to Perform PCA on a 4D Dataset

Lecture 19: Using Stacked Autoencoders for Representation on MNIST Dataset

Lecture 20: Build a Deep Autoencoder to Reduce Latent Space of LFW Face Dataset

Lecture 21: Generator and Discriminator the Basics of GAN

Lecture 22: Downloading and Setting Up the (Microsoft Research Asia) Geolife Project Dataset

Lecture 23: Coding the Generator and Discriminator Using TensorFlow

Lecture 24: Training GANs to Create Synthetic GPS Based Trajectories

Chapter 2: Hands-on Reinforcement Learning with TensorFlow

Lecture 1: The Course Overview

Lecture 2: Introduction to Reinforcement Learning

Lecture 3: Common RL Tasks and the Reinforcement Process

Lecture 4: Setting Up Environments Using Open AI’s Gym Framework

Lecture 5: The Taxi-v2 Environment

Lecture 6: Operating Taxi-v2 Using a Dumb Agent

Lecture 7: Introducing Reinforcement Q-Learning

Lecture 8: Implementing Q-Learning

Lecture 9: Q-Learning Agent in Action

Lecture 10: The Cartpole Environment

Lecture 11: Introducing Q-Networks

Lecture 12: TensorFlow Basics

Lecture 13: Implementing Q-Network

Lecture 14: Q-Network Agent in Action

Lecture 15: Introducing Deep Q-Networks

Lecture 16: The DQN Training Algorithm

Lecture 17: Implementing DQN

Lecture 18: DQN in Action

Lecture 19: Dueling Double DQN

Lecture 20: Logging, Saving, and Visualizing

Lecture 21: Structuring the Code Base

Lecture 22: Debugging and Some Nice Practices in TensorFlow

Lecture 23: TensorFlow on Multiple Devices

Lecture 24: Next Steps

Instructors

Packt Publishing
Tech Knowledge in Motion

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3 stars: 5 votes
4 stars: 4 votes
5 stars: 2 votes

Frequently Asked Questions

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