Practical Deep Learning & Artificial Neural Nets with Python

Practical Deep Learning & Artificial Neural Nets with Python, available at $64.99, has an average rating of 4.71, with 56 lectures, 2 quizzes, based on 7 reviews, and has 69 subscribers.

You will learn about Build a solid understanding of common problems can you solve with Deep Learning Build Deep Neural Networks in the healthcare domain to address applications of deep learning in it Develop a clear understanding of how Deep Learning tools work and what you need to know to use them in practice Practical ways in which Deep Learning techniques can be applied to develop solutions for image recognition Explore face recognition with Deep Learning Work with dialog generation in Deep Learning Use different Deep Learning algorithms to solve specific types of problem and learn their strengths and weaknesses, Save time by learning practical Deep Learning methods that you can immediately apply to real-world problems. This course is ideal for individuals who are Data Science Professionals, Machine Learning enthusiasts, Developers, Analysts, who would like to gain practical hands-on experience to their Deep Learning problems and build Deep-Learning applications with real-world datasets in Python, will find this course useful. It is particularly useful for Data Science Professionals, Machine Learning enthusiasts, Developers, Analysts, who would like to gain practical hands-on experience to their Deep Learning problems and build Deep-Learning applications with real-world datasets in Python, will find this course useful.

Enroll now: Practical Deep Learning & Artificial Neural Nets with Python

Summary

Title: Practical Deep Learning & Artificial Neural Nets with Python

Price: $64.99

Average Rating: 4.71

Number of Lectures: 56

Number of Quizzes: 2

Number of Published Lectures: 56

Number of Published Quizzes: 2

Number of Curriculum Items: 58

Number of Published Curriculum Objects: 58

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Build a solid understanding of common problems can you solve with Deep Learning
Build Deep Neural Networks in the healthcare domain to address applications of deep learning in it
Develop a clear understanding of how Deep Learning tools work and what you need to know to use them in practice
Practical ways in which Deep Learning techniques can be applied to develop solutions for image recognition
Explore face recognition with Deep Learning
Work with dialog generation in Deep Learning
Use different Deep Learning algorithms to solve specific types of problem and learn their strengths and weaknesses,
Save time by learning practical Deep Learning methods that you can immediately apply to real-world problems.

Who Should Attend

Data Science Professionals, Machine Learning enthusiasts, Developers, Analysts, who would like to gain practical hands-on experience to their Deep Learning problems and build Deep-Learning applications with real-world datasets in Python, will find this course useful.

Target Audiences

Data Science Professionals, Machine Learning enthusiasts, Developers, Analysts, who would like to gain practical hands-on experience to their Deep Learning problems and build Deep-Learning applications with real-world datasets in Python, will find this course useful.

Video Learning Path Overview

A Learning Path is a specially tailored course that brings together two or more different topics that lead you to achieve an end goal. Much thought goes into the selection of the assets for a Learning Path, and this is done through a complete understanding of the requirements to achieve a goal.

Deep learning is the next step to a more advanced implementation of Machine Learning. Deep Learning allows you to solve problems where traditional Machine Learning methods might perform poorly: detecting and extracting objects from images, extracting meaning from text, and predicting outcomes based on complex dependencies, to name a few.

In this practical Learning Path, you will build Deep Learning applications with real-world datasets and Python. Beginning with a step by step approach, right from building your neural nets to reinforcement learning and working with different Deep Learning applications such as computer Vision and voice and image recognition, this course will be your guide in getting started with Deep Learning concepts.

Moving further with simple and practical solutions provided, we will cover a whole range of practical, real-world projects that will help customers learn how to implement their skills to solve everyday problems.

By the end of the course, you’ll apply Deep Learning concepts and use Python to solve challenging tasks with real-world datasets.

Key Features

Get started with Deep Learning and build complex models layer by layer, with increasing complexity, in no time.
A hands-on guide covering common as well as not-so-common problems in deep learning using Python.
Explore the practical essence of Deep Learning in a relatively short amount of time by working on practical, real-world use cases.

Author Bios

Radhika Datarhas more than 6 years’ experience in Software Development and Content Writing. She is well versed with frameworks such as Python, PHP, and Java and regularly provides training on them. She has been working with Educba and Eduonix as a Training Consultant since June 2016 and has been an Academic writer with TutorialsPoint since Sept 2015.
Jakub Konczykhas enjoyed and done programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share it with others. He first discovered Machine Learning when he was trying to predict the real estate prices in one of the early stage start-ups he was involved in. He failed miserably but then discovered a much more practical way to learn Machine Learning that he shares in this course.

Course Curriculum

Chapter 1: Hands-On Python Deep Learning

Lecture 1: Course Overview

Lecture 2: Introduction to Deep Learning and Neural Networks

Lecture 3: Building Neural Network

Lecture 4: Evaluating the Neural Network

Lecture 5: Ohio Clinic Data Set

Lecture 6: Analyze and Explore Your Data

Lecture 7: Feature Exploration of Our Dataset

Lecture 8: Performing Data Analysis

Lecture 9: Introduction to Image Recognition

Lecture 10: Environmental Setup

Lecture 11: Using Spyder IDE

Lecture 12: Encode the Image

Lecture 13: Understanding Testing Functionality and Output

Lecture 14: Introduction to Face Recognition

Lecture 15: Problem Statement

Lecture 16: Face Recognition File and Output

Lecture 17: Introduction to Keras

Lecture 18: Feedforward Neural Network

Lecture 19: Representing Simple FeedForward Neural Network Using Keras

Lecture 20: Scaling Input Images

Lecture 21: Introduction to LSTM

Lecture 22: LSTM Architecture

Lecture 23: How LSTM Network Works

Lecture 24: Fitting Neural Network and Output

Lecture 25: Introduction to Text Summarization

Lecture 26: Understanding the Problem Statement

Lecture 27: Training and Testing Data

Lecture 28: Preparation Data

Lecture 29: Introduction to Encode-Decode Model

Lecture 30: Implementing Decoder and Encoder

Lecture 31: Defining the Module and Output

Chapter 2: Real-World Python Deep Learning Projects

Lecture 1: The Course Overview

Lecture 2: What Types of Problems Can You Solve Using Deep Learning?

Lecture 3: Installing Essential DL Tools

Lecture 4: Based on Past Data, Predicting the Number of Airline Passengers

Lecture 5: Getting and Preparing Airline Data

Lecture 6: Building Your Multilayer Perceptron Model

Lecture 7: Training and Testing Your Model

Lecture 8: Making Predictions and What's Next?

Lecture 9: End Goal – Label a Given Tweet (Short Text) as Negative or Positive

Lecture 10: Dataset Overview

Lecture 11: Preparing Data for Sentiment Analysis

Lecture 12: What Are Word Embeddings and Why They Are Important When Working with CNNs?

Lecture 13: Building Your CNN Model for Text Classification

Lecture 14: Training and Testing Your Model

Lecture 15: Detecting Mean Tweets Using Your Model and What’s Next?

Lecture 16: Detect Whether an Image Contains a Smile with High Accuracy

Lecture 17: Getting and Preparing Data for Smile Detection

Lecture 18: Building Your CNN Model for Smile Detection

Lecture 19: Training and Testing Your Model

Lecture 20: Detecting Smiles with Your Model and What’s Next?

Lecture 21: Predict the Closing Stock Price of a Given Company for the Next Day

Lecture 22: Getting and Preparing Stock Prices Data

Lecture 23: Building Your LSTM Model for Price Prediction

Lecture 24: Training and Testing Your Model

Lecture 25: Detecting Closing Stock Price with Your Model and What’s Next?

Instructors

Packt Publishing
Tech Knowledge in Motion

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4 stars: 4 votes
5 stars: 3 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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