Practical Deep Learning & Artificial Neural Nets With Python

Last updated 3/2019MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 2.84 GB | Duration: 6h 28m

Apply Deep Learning concepts with Python to solve challeg tasks: Detect smiles in your camera app using Neural Nets

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 by learning practical Deep Learning methods that you can immediately apply to real-world problems.

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 OverviewA 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. Bning 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 challeg tasks with real-world datasets.Key FeaturesGet started with Deep Learning and build complex models layer by layer, with increasing complexity, in no .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 by working on practical, real-world use cases.Author BiosRadhika 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 Acad 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.

Overview

Section 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 Perfog 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

Section 2: Real-World Python Deep Learning Projects

Lecture 32 The Course Overview

Lecture 33 What Types of Problems Can You Solve Using Deep Learning?

Lecture 34 Installing Essential DL Tools

Lecture 35 Based on Past Data, Predicting the Number of Airline Passengers

Lecture 36 Getting and Preparing Airline Data

Lecture 37 Building Your Multilayer Perceptron Model

Lecture 38 Training and Testing Your Model

Lecture 39 Making Predictions and What's Next?

Lecture 40 End Goal – Label a Given Tweet (Short Text) as Negative or Positive

Lecture 41 Dataset Overview

Lecture 42 Preparing Data for Sennt Analysis

Lecture 43 What Are Word Embeddings and Why They Are Important When Working with CNNs?

Lecture 44 Building Your CNN Model for Text Classification

Lecture 45 Training and Testing Your Model

Lecture 46 Detecting Mean Tweets Using Your Model and What’s Next?

Lecture 47 Detect Whether an Image Contains a Smile with High Accuracy

Lecture 48 Getting and Preparing Data for Smile Detection

Lecture 49 Building Your CNN Model for Smile Detection

Lecture 50 Training and Testing Your Model

Lecture 51 Detecting Smiles with Your Model and What’s Next?

Lecture 52 Predict the Closing Stock Price of a Given Company for the Next Day

Lecture 53 Getting and Preparing Stock Prices Data

Lecture 54 Building Your LSTM Model for Price Prediction

Lecture 55 Training and Testing Your Model

Lecture 56 Detecting Closing Stock Price with Your Model and What’s Next?

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.

HomePage:

https://www.udemy.com/course/practical-deep-learning-artificial-neural-nets-with-python/