Last updated 3/2019
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.84 GB | Duration: 6h 28m
Apply Deep Learning concepts with Python to solve challenging tasks: Detect smiles in your camera app using Neural Nets
Apply Deep Learning concepts with Python to solve challenging 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 time 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. 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 FeaturesGet 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 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 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.
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 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
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 Sentiment 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.
https://www.udemy.com/course/practical-deep-learning-artificial-neural-nets-with-python/
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