Practical Deep Learning & Artificial Neural Nets with Python
4.9 (3 ratings)
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Practical Deep Learning & Artificial Neural Nets with Python

Apply Deep Learning concepts with Python to solve challenging tasks: Detect smiles in your camera app using Neural Nets
4.9 (3 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
27 students enrolled
Created by Packt Publishing
Last updated 3/2019
English
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 6.5 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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.
Course content
Expand all 56 lectures 06:27:51
+ Hands-On Python Deep Learning
31 lectures 02:37:23

This video provides an overview of the entire course.

Preview 02:17

This video will focus on introduction of neural networks, machine learning and deep learning algorithms.

   •  Understand neural networks

   •  Focus on machine learning and deep learning aspects

   •  Use these concepts for upcoming sections

Introduction to Deep Learning and Neural Networks
12:01

This video will teach how neural network always focusses on creating training and testing data for better evaluation.

   •  Understand the problem statement

   •  Focus on the attributes which needs to be changed

   •  Generate output for first neural network evaluation

Building Neural Network
08:14

This video focus on training and testing data and how it plays important role in implementing deep learning algorithms.

   •  Evaluate the data attributes

   •  Focus on training and testing data

   •  Check on the output

Evaluating the Neural Network
08:08

This video will focus on the problem statement with reference to clinic/ hospital dataset. This hospital includes various records and sections which needs to be evaluated.

   •  Understand the attributes

   •  Focus on the type of diseases and sections

   •  Analyze the data attributes

Preview 01:43

This video will import the necessary modules and start evaluating them.

   •  Launch Jupyter notebook, upload the csv file of hospital

   •  Evaluate the features when necessary modules are imported

   •  Correct and analyze the data in systematic manner

Analyze and Explore Your Data
10:10

This video will evaluate all features to get the visualization.

   •  Focus on the attributes to be created

   •  Evaluate and convert them in that specific order

   •  Create the respective chart for same

Feature Exploration of Our Dataset
07:07

This video will teach the use of pandas for data and time conversion with data analysis.

   •  Convert the date in specified format

   •  Append the converted data in specified manner

   •  Analyze the output

Performing Data Analysis
07:26

This video will focus on the problem statement, the need for creating image recognition module.

   •  Understand image recognition concept

   •  Focus on the problem statement

   •  Focus on evaluation

Introduction to Image Recognition
01:25

This video will focus on data exploration and the environmental setup of OpenCV library which is needed for image, facial and body recognition.

   •  Explore the data

   •  Understand the installation of OpenCV from documentation

   •  Evaluate and explore the possibilities

Environmental Setup
05:44

This video will help you create a pattern of CSV for same.

   •  The CSV will include list of images which needs to be encoded and training

   •  Evaluate and convert them in that specific order

   •  Create the respective model for same

Using Spyder IDE
08:47

This video, we will use harcascade xml for encode the image pattern.

   •  Understanding the importance of xml

   •  Encode the parameters

   •  Understand testing functionality and output

Encode the Image
06:44

This video will focus on the evaluation and create a classification model for image recognition and analyzing the output.

   •  Create a training model

   •  Focus on the image and visualize the facial parameter

   •  Analyze the output

Understanding Testing Functionality and Output
07:01

This video will focus on the problem evaluation and understand why facial recognition is needed.

   •  Understand the need of facial recognition of patients

   •  Focus where images will be stored

   •  Analyze the data attributes

Introduction to Face Recognition
01:59

This video will focus on the problem statement and understand how it is useful from security point of view.

   •  Understand the problem

   •  Analyze the enhancement of security feature

Problem Statement
01:29

This video will focus on the output.

   •  Execute the code

   •  Code will set on the web cam and capture the images

   •  All the images will be saved in datasets folder

Face Recognition File and Output
11:41

This video, will make you understand the problem statement with the mentioned dataset. In this video, we will focus on critical causalities and create a time series module of same.

   •  Understand the dataset

   •  Focus on the number of records

   •  Analyze the data attributes and environmental setup of Keras

Introduction to Keras
01:24

This video will focus on working module of LSTM.

   •  Understand the network architecture

   •  LSTM input and output structure

   •  Understand how the model can be evaluated

Feedforward Neural Network
03:00

This video will teaches us how to create LSTM model with base of recurrent neural network for classification of cats and dogs images.

   •  Focus on the model creation

   •  Encode the parameters

   •  Analyze the output

Representing Simple FeedForward Neural Network Using Keras
05:58

This video will focus on the output.

   •  Execute the code

   •  The classifier will create a separate folder structure of input and output data

   •  All the images will be saved in datasets folder

Scaling Input Images
06:16

This video will focus on the problem statement with the mentioned dataset. In this video, we will focus on critical causalities and create a time series module of same.

   •  Understand the dataset

   •  Focus on the number of records

   •  Analyze the data attributes and environmental setup of Keras

Introduction to LSTM
01:40

This video will focus on working module of LSTM.

   •  Understand the network architecture

   •  LSTM input and output structure

   •  Understand how the model can be evaluated. Implement the model with LSTM structure

LSTM Architecture
01:45

This video teaches us how to create an LSTM model with base of recurrent neural network for document characterization.

   •  Focus on the model creation

   •  Encode the parameters

   •  Analyze the output

How LSTM Network Works
05:54

This video will focus on the output.

   •  Execute the code

   •  The classifier will create a separate folder structure of input and output data

   •  The list of data will be evaluated in respective chart

Fitting Neural Network and Output
04:33

This video will focus on the problem evaluation and understand how junks of data such as case files be converted in specific format

   •  Understand the need of text summarization

   •  Focus on methodology

   •  Analyze the data attributes.

Introduction to Text Summarization
01:30

This video will focus on the problem statement with respect to text summarization

   •  Focus on the problem statement

   •  Analyze the manner in which case files are maintained.

   •  Focus on model creation

Understanding the Problem Statement
02:45

This video we will focus on creating testing and training data for better evaluation

   •  Understanding the importance of both

   •  Encode the parameters

   •  Focusing on preparation data

Training and Testing Data
03:47

This video will focus on the output

   •  Execute the code

   •  The separate folders will be created accordingly

   •  Text summarization procedure will be followed.

Preparation Data
04:10

This video will focus on dialog generation with encode and decode model

   •  Understand the need of encode and decode model

   •  Focus on methodology

   •  Analyze the data attributes.

Introduction to Encode-Decode Model
02:12

This video will focus on the problem statement with respect to dialog generation and evaluate the same

   •  Focus on the problem statement

   •  Analyze the manner in code prediction of data will be maintained

   •  Focus on model creation

Implementing Decoder and Encoder
05:11

This video will focus on the output and creation of accuracy rate

   •  Understanding the output generation

   •  Check out the accuracy rate

   •  Analyze the output.

Defining the Module and Output
05:22
Test your knowledge
6 questions
+ Real-World Python Deep Learning Projects
25 lectures 03:50:28

This video provides an overview of the entire course.

Preview 05:18

The goal of this video is to help you understand why you should learn deep learning and in what cases it's beneficial to use it.

  • Define what deep learning is and why it's different from other methods

  • Present the general types of problems that deep learning is good at solving

  • Give specific examples of problems that deep learning helps to solve

What Types of Problems Can You Solve Using Deep Learning?
04:34

The goal of this video is to show you how you can quickly install the necessary tools that we will be using throughout the course.

  • Introduce the package manager that we will be using to install all the required tools, and learn why you should use it

  • Give a quick description of tools we will be using and in the course and installing in the video

  • Show how to quickly install all the necessary tools step by step

Installing Essential DL Tools
08:16

An overview of the process of solving a time series prediction problem using deep learning methods.

  • The first step is to phrase our problem in the correct way and prepare data for working with a neural network.

  • Then we need to build our neural network in Python.

  • The last step is to train our model with our data and tweak it for best performance. Then we can just use it make a prediction.

Preview 02:37

Here we will be downloading and preparing our airline data to work without a neural network.

  • Download data from the right place in the right format

  • Phrase our problem as a regression problem and convert our data into the right format

  • Verify that all of the data is ready for our neural network

Getting and Preparing Airline Data
08:25

In this Section, we will build our first neural network in Python for making predictions.

  • Learn what MLP is

  • Build MLP in Python

  • Understand MLP’s layers

Building Your Multilayer Perceptron Model
08:15

Here you will learn how to train and test your model for best performance.

  • Understand the hyperparameters that influence training

  • Train your model and watch the important metrics

  • Tweak the parameters according to the results you get in training

Training and Testing Your Model
23:55

Let’s make predictions using our model and discover what’s next.

  • Load the saved model

  • Choose the right value for prediction

  • Make the prediction using our model

Making Predictions and What's Next?
08:47

We’re starting with a quick overview of what you will learn in this Section.

  • Define the project’s goals, including our end goal

  • Define the steps to get there

  • Explain what you will learn in each step

End Goal – Label a Given Tweet (Short Text) as Negative or Positive
02:26

Next, we will have a look at our dataset to understand how we can use it.

  • Which dataset we will be using and why

  • How our dataset is organized

  • Understanding the dataset format

Dataset Overview
05:14

Learn how to prepare text to work with text classification using deep learning methods.

  • Choose the right dataset and load it into memory

  • Clean up the data

  • Encode the data

Preparing Data for Sentiment Analysis
15:00

Understand how we can use a CNN network designed to work with images to work with text.

  • The main idea behind word embeddings

  • How to turn text into word embeddings

  • How we can use word embeddings with our CNN network

What Are Word Embeddings and Why They Are Important When Working with CNNs?
07:41

In this video, you will learn how to build a CNN network that can perform sentiment analysis.

  • Learn how to use the Embedding layer and what word embeddings are

  • Discover the main parts of every CNN model—Convolution Layer and Pooling layer—and how they work together

  • Learn how to define the last layer for the classification problem

Building Your CNN Model for Text Classification
11:54

Next, you will learn how to train and test your text classification model.

  • The key parameters to set up before training your model

  • Understand the main metrics that will help you pick up the best model

  • Two ways to get better results with your text classification model

Training and Testing Your Model
12:17

In this last video, you will learn how to use your model to judge a tweet and learn ways to move forward.

  • Load the model and tokenizer saved during training

  • Read the tweets for analysis and encode them using the tokenizer

  • Use two methods to judge whether a tweet is negative or not

Detecting Mean Tweets Using Your Model and What’s Next?
15:55

Get a quick overview of this project and see the steps to build it.

  • Clarify our goals for this project

  • Go through the steps to complete the project

  • Briefly describe what we will be doing in each step

Detect Whether an Image Contains a Smile with High Accuracy
01:51

In this video, we will be preparing our input images to work with CNN.

  • Download images and put them in the right folders

  • Convert each into a two-dimensional array and optimize them for best results

  • Split the dataset into training and testing parts

Getting and Preparing Data for Smile Detection
13:36

You will learn the basics of building a CNN for image classification.

  • Learn how to set up an input convolutional layer and output layer

  • Understanding intuitively how CNNs work

  • Learn the role of the rest of the layers in a CNN, choosing the right loss function and optimizer to detect multiple classes

Building Your CNN Model for Smile Detection
16:52

In this video, you will find the best parameters to train your CNN model.

  • Quickly train a model with different parameters of number of epochs and batch size

  • Recognize which set of parameters is the best

  • Train the final model with the best parameters

Training and Testing Your Model
07:12

Here, we will be using our model to detect smiles in a totally new dataset and discuss what we can do to improve the accuracy of our model.

  • Get familiar with our new dataset, run our prediction script, and understand the output metrics

  • Examine the photos that were classified incorrectly

  • Discuss some ways to improve the accuracy of our model

Detecting Smiles with Your Model and What’s Next?
15:59

Get a high-level overview of the project.

  • Clearly define the project’s goals

  • Break down the project into steps

  • Get a quick overview of each step

Predict the Closing Stock Price of a Given Company for the Next Day
01:29

Learn how to get the free stock prices data and prepare it for forecasting using LSTM.

  • Download the historical stock prices data for a given company

  • Remove the unnecessary columns from the dataset

  • Convert the dataset into a supervised learning problem

Getting and Preparing Stock Prices Data
09:56

Create and compile an LSTM model for time series forecasting.

  • Create the main model container

  • Add and configure an LSTM layer

  • Add the last Dense layer for price prediction

Building Your LSTM Model for Price Prediction
07:16

Find the optimal training parameters for our LSTM model.

  • Train the model with initial parameters and chart the training metrics

  • Interpret the training chart

  • Train the model with optimal parameters

Training and Testing Your Model
04:50

Learn how to use our LSTM model to predict stock prices using historical stock metrics. Explore future improvements.

  • Prepare historical metrics to be used for prediction

  • Reshape the data so that it can be used for prediction using LSTM

  • Make a stock price prediction, compare it with the actual price, and calculate the prediction error

Detecting Closing Stock Price with Your Model and What’s Next?
10:53
Test your knowledge
5 questions
Requirements
  • To pick up this course, you need to have Python programming skills. Developers, analysts, and data scientists who have a basic Machine Learning knowledge and want to now explore the possibilities of Deep Learning will feel perfectly comfortable in understanding the topics presented in this Course.
Description

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 Datar has 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 Konczyk has 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.

Who this course is 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.