Video description
Want to become a good data scientist? Then this is the right course for you.
This course has been designed by IIT professionals who have mastered mathematics and data science. We will be covering complex theory, algorithms, and coding libraries in a very simple way that can be easily grasped by any beginner.
We will walk you stepbystep into the world of deep learning. Each video seeks to improve your understanding of the challenging field of Deep Learning from a beginner to an advanced level.
We will cover artificial neural networks, feedforward networks, backpropagation, regularization, convolution neural networks, practical on CNN, two realworld projects for CNN, transfer learning, recurrent neural networks, advanced RNN, a case study on NLP (Natural Language Processing), generate automatic programming code, and build a solid foundation for Python and machine learning.
We will be solving a few realworld projects during this course and their complete solutions are also provided so that students can easily implement what has been taught.
By the end of the course, you will be able to use Python’s deep learning algorithms in real life.
What You Will Learn
 Learn to use Matplotlib for data visualization
 Learn to use Seaborn for statistical plots
 Learn to use NumPy and Pandas for data analysis
 Learn all the mathematics required to understand deep learning algorithms
 Learn all statistical principles and become a deep learning expert
 Learn end to end data science solutions
Audience
A beginner in Python or any other objectoriented programming language should find this course helpful. Those who are already working on analytics and machine learning models and looking to leverage deep learning technologies to improve their problemsolving capacity should benefit from this course. Working professionals looking for a career transition to data science roles will be able to upskill.
About The Author
Geekshub Pvt. Ltd.: Geekshub is an online education company in the field of big data and analytics. Their aim as a team is to provide the best skillset to their customers to make them jobready and prepare them to crack any challenge. They have the best trainers for cuttingedge technologies such as machine learning, deep learning, Natural Language Processing (NLP), reinforcement learning, and data science. Their instructors are people who graduated from IIT, MIT and Standford. They are passionate about teaching the topics using curated realworld case studies that calibrate the learning experience of students.
Publisher resources
Table of contents
 Chapter 1 : Course Introduction

Chapter 2 : Artificial Neural NetworksIntroduction
 Introduction
 Deep Learning
 Understanding the Human Brain
 Perceptron
 Perceptron for Classifiers
 Perceptron in Depth
 Homogeneous Coordinate
 Example for Perceptron
 MultiClassifier
 Neural Networks
 Input Layer
 Output Layer
 Sigmoid Function
 Understanding MNIST
 Assumptions in Neural Networks
 Training in Neural Networks
 Understanding Notations
 Activation Functions
 Chapter 3 : ANN  Feed Forward Network

Chapter 4 : Backpropagation
 Introduction
 Introducing Loss Function
 Backpropagation Training  Part 1
 Backpropagation Training  Part 2
 Backpropagation Training  Part 3
 Backpropagation Training  Part 4
 Backpropagation Training  Part 5
 Sigmoid Function
 Backpropagation Training  Part 6
 Backpropagation Training  Part 7
 Backpropagation Training  Part 8
 Backpropagation Training  Part 9
 Backpropagation Training  Part 10
 Pseudocode
 SGD
 Finding Global Minima
 Training for Batches
 Chapter 5 : Regularization
 Chapter 6 : Convolution Neural Networks
 Chapter 7 : CNNKeras

Chapter 8 : CNNTransfer Learning
 Introduction
 AlexNet
 GoogleNet
 ResNet  Part 1
 ResNet  Part 2
 Transfer Learning  Part 1
 Transfer Learning  Part 2
 Transfer Learning  Part 3
 Transfer Learning  Part 4
 Transfer Learning  Part 5
 Transfer Learning  Part 6
 Case Study  Part 1
 Case Study  Part 2
 Case Study  Part 3
 Analysis  Part 1
 Analysis  Part 2

Chapter 9 : CNNIndustry Live Project: Playing with RealWorld Natural Images
 Introduction
 Working with Flower Images: Case Study  Part 1
 Working with Flower Images: Case Study  Part 2
 Working with Flower Images: Case Study  Part 3
 Working with Flower Images: Case Study  Part 4
 Working with Flower Images: Case Study  Part 5
 Working with Flower Images: Case Study  Part 6
 Working with Flower Images: Case Study  Part 7
 Working with Flower Images: Case Study  Part 8
 Working with Flower Images: Case Study  Part 9
 Working with Flower Images: Case Study  Part 10
 Working with Flower Images: Case Study  Part 11
 Working with Flower Images: Case Study  Part 12
 Working with Flower Images: Case Study  Part 13
 Working with Flower Images: Case Study  Part 14
 Chapter 10 : CNNIndustry Live Project: Find Medical Abnormalities and Save a Life
 Chapter 11 : Recurrent Neural Networks: Introduction
 Chapter 12 : Recurrent Neural Networks: LSTM

Chapter 13 : Recurrent Neutral Networks: PartOfSpeech Tagger
 PartOfSpeech Tagger CaseStudy (Part1)
 PartOfSpeech Tagger Case Study (Part2)
 PartOfSpeech Tagger Case Study (Part3)
 PartOfSpeech Tagger Case Study (Part4)
 PartOfSpeech Tagger Case Study (Part5)
 PartOfSpeech Tagger Case Study (Part6)
 PartOfSpeech Tagger Case Study (Part7)
 PartOfSpeech Tagger Case Study (Part8)
 PartOfSpeech Tagger Case Study (Part9)
 Chapter 14 : Text Generation Using RNN
 Chapter 15 : Prerequisite  Python Fundamentals
 Chapter 16 : Prerequisite  NumPy
 Chapter 17 : Prerequisite  Pandas
 Chapter 18 : Prerequisite  Some Fun with Math
 Chapter 19 : Prerequisite  Data Visualization
 Chapter 20 : Prerequisite  Simple Linear Regression
 Chapter 21 : Prerequisite  Gradient Descent
 Chapter 22 : Prerequisite  Classification: KNN
 Chapter 23 : Prerequisite  Logistic Regression
 Chapter 24 : Prerequisite  Advanced Machine Learning Algorithms
Product information
 Title: Deep Learning with RealWorld Projects
 Author(s):
 Release date: July 2019
 Publisher(s): Packt Publishing
 ISBN: 9781838985721
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