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 step-by-step 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 real-world 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 real-world 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 object-oriented 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 problem-solving 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 skill-set to their customers to make them job-ready and prepare them to crack any challenge. They have the best trainers for cutting-edge 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 real-world case studies that calibrate the learning experience of students.
Table of contents
- Chapter 1 : Course Introduction
-
Chapter 2 : Artificial Neural Networks-Introduction
- Introduction
- Deep Learning
- Understanding the Human Brain
- Perceptron
- Perceptron for Classifiers
- Perceptron in Depth
- Homogeneous Coordinate
- Example for Perceptron
- Multi-Classifier
- 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 : CNN-Keras
-
Chapter 8 : CNN-Transfer 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 : CNN-Industry Live Project: Playing with Real-World 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 : CNN-Industry 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: Part-Of-Speech Tagger
- Part-Of-Speech Tagger Case-Study (Part-1)
- Part-Of-Speech Tagger Case- Study (Part-2)
- Part-Of-Speech Tagger Case- Study (Part-3)
- Part-Of-Speech Tagger Case- Study (Part-4)
- Part-Of-Speech Tagger Case- Study (Part-5)
- Part-Of-Speech Tagger Case- Study (Part-6)
- Part-Of-Speech Tagger Case- Study (Part-7)
- Part-Of-Speech Tagger Case- Study (Part-8)
- Part-Of-Speech Tagger Case- Study (Part-9)
- 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 Real-World Projects
- Author(s):
- Release date: July 2019
- Publisher(s): Packt Publishing
- ISBN: 9781838985721
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