Deep Learning with Real-World Projects [Video]
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-
Free ChapterCourse Introduction
-
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
-
ANN - Feed Forward Network
-
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
-
Regularization
-
Convolution Neural Networks
-
CNN-Keras
-
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
-
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
-
CNN-Industry Live Project: Find Medical Abnormalities and Save a Life
-
Recurrent Neural Networks: Introduction
-
Recurrent Neural Networks: LSTM
-
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)
-
Text Generation Using RNN
-
Prerequisite - Python Fundamentals
-
Prerequisite - NumPy
-
Prerequisite - Pandas
-
Prerequisite - Some Fun with Math
-
Prerequisite - Data Visualization
-
Prerequisite - Simple Linear Regression
-
Prerequisite - Gradient Descent
-
Prerequisite - Classification: KNN
-
Prerequisite - Logistic Regression
-
Prerequisite - Advanced Machine Learning Algorithms
About this
video
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.
All the codes and supporting files for this course are available at: https://github.com/packtpublishing/deep-learning-with-real-world-projects
- Publication date:
- July 2019
- Publisher
- Packt
- Duration
- 34 hours 31 minutes
- ISBN
- 9781838985721
Latest Reviews
(1 reviews total)