Advanced Deep Learning with Keras

Watch Advanced Deep Learning with Keras

  • 2017
  • 1 Season

Advanced Deep Learning with Keras is an online course offered by Packt Publishing that aims to provide advanced knowledge and techniques to master deep learning using Keras. This course is designed for professionals who have a basic knowledge of deep learning and Keras and want to dive into more complex concepts to enhance their skills.

The course is divided into six modules, each focusing on different topics related to deep learning with Keras. The first module, "Advanced Keras", covers the advanced features of Keras, including custom layers, models, and callbacks. It also provides a comprehensive understanding of how to save and load models along with practical examples.

The second module, "Generative Adversarial Networks", covers the foundational concepts of GANs and its practical applications. It also includes an in-depth explanation of how GANs work, different types of GANs, and how to build GANs using Keras.

The third module, "Autoencoders", covers the concept of autoencoders and its practical applications. It also includes an explanation of how autoencoders work, different types of autoencoders, and how to build them using Keras.

The fourth module, "Recurrent Neural Networks", focuses on the foundations of RNNs and its practical applications. It covers different types of RNNs, including LSTM, GRU, and Bi-directional RNNs, and how to build them using Keras.

The fifth module, "Convolutional Neural Networks", covers the foundational concepts of CNNs and its practical applications. It includes an explanation of different types of CNNs, including VGG, ResNet, and Inception, and how to build them using Keras.

The final module, "Deployment", focuses on the deployment of deep learning models on various platforms. It covers the practical aspects of deployment, including how to convert and optimize models to be deployed on mobile and web platforms using TensorFlow.js and TensorFlow Lite.

Throughout the course, learners will have the opportunity to work on practical projects that reinforce the concepts learned in each module. These projects cover a wide range of applications, including image classification, face detection, and natural language processing.

In addition to the content provided in each module, learners will also have access to a comprehensive set of resources, including course notes, code examples, and quizzes. The quizzes are designed to help learners consolidate their knowledge and test their understanding of the topics covered in each module.

Overall, Advanced Deep Learning with Keras is a comprehensive course that provides a deep understanding of advanced topics in deep learning using Keras. The course is designed for professionals who want to enhance their skills and take their deep learning expertise to the next level. With practical projects, in-depth explanations, and a wealth of resources, this course is an excellent choice for anyone looking to master the latest techniques in deep learning with Keras.

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Seasons
Techniques to Improve GANs
36. Techniques to Improve GANs
December 27, 2017
GANs are still at a research stage and so it is important that we learn empirical techniques to make GANs work better.
Deep Convolutional Generative Adversarial Networks (DCGAN)
35. Deep Convolutional Generative Adversarial Networks (DCGAN)
December 27, 2017
This video explains what a DCGAN is, and why is it so popular.
Run Our First GAN
34. Run Our First GAN
December 27, 2017
This video demonstrates how to train and run a basic generative adversarial network.
An Introduction to Generative Adversarial Networks (GAN)
33. An Introduction to Generative Adversarial Networks (GAN)
December 27, 2017
This video is an introduction to GAN.
Natural Language Processing
32. Natural Language Processing
December 27, 2017
This video explains what natural language is processing and what purpose does it solve.
Hyper Parameter Search
31. Hyper Parameter Search
December 27, 2017
We have used the term hyper parameter search a number of times in the past videos; here we formally introduce the term and how to effectively use it in Keras
Transfer Learning
30. Transfer Learning
December 27, 2017
In this video, we will discuss transfer learning and its applications.
Data Augmentation
29. Data Augmentation
December 27, 2017
In this video, we discuss data augmentation and its main features.
Style Transfer Explained
28. Style Transfer Explained
December 27, 2017
This video explains style transfer and its underlying mechanism in depth.
Advanced Techniques
27. Advanced Techniques
December 27, 2017
In this video, we will go through some of the advanced techniques of style transfer.
Single Style Transfer
26. Single Style Transfer
December 27, 2017
This video is a hands-on demonstration of style transfer.
Introduction to Neural Style Transfer
25. Introduction to Neural Style Transfer
December 27, 2017
This video is about neural style transfer and how it works. We also cover a brief overview of the optimization phase.
Hybrid System
24. Hybrid System
December 27, 2017
In this video we will talk about hybrid systems and extend the Keras example from the previous video.
Collaborative Filtering
23. Collaborative Filtering
December 27, 2017
In this video we go through user based collaborative filtering with a Keras example.
Content/Item Based Filtering
22. Content/Item Based Filtering
December 27, 2017
Here we further extend our knowledge of collaborative filtering with a Keras example.
What are Recommender Systems?
21. What are Recommender Systems?
December 27, 2017
We get to know what is behind the term recommender system and its applications.
Embedding Layers
20. Embedding Layers
December 27, 2017
Embedding layer is widely used in deep learning.
19. "Many to Many" Architecture
December 27, 2017
What is a many to many architecture?
Recurrent Neural Networks
16. Recurrent Neural Networks
December 27, 2017
What makes neural network recurrent?
Introduction to Recurrent Networks
15. Introduction to Recurrent Networks
December 27, 2017
This video introduces recurrent network and sequential data
Image Segmentation Example
14. Image Segmentation Example
December 27, 2017
This video discusses what segmentation in deep learning is
Image Classification Example
13. Image Classification Example
December 27, 2017
In this video, we will implement convolutional networks in Keras.
CNN Architectures
12. CNN Architectures
December 27, 2017
In this video, we will learn about CNN architectures and research trends.
Convolutional Networks
11. Convolutional Networks
December 27, 2017
Convolution networks are very good at image classification.
Introduction to Computer Vision
10. Introduction to Computer Vision
December 27, 2017
Computer vision was the first field revolutionized by deep learning.
Regularization in Deep Learning
9. Regularization in Deep Learning
December 27, 2017
Effective regularization is important for proper functioning of our models with real inputs. This video deals with some regularization techniques.
Design and Train Deep Neural Networks
8. Design and Train Deep Neural Networks
December 27, 2017
In this video, as the name suggests, we will go through the designing, training and visualization phase with Keras.
Presentation of Keras and Its API
7. Presentation of Keras and Its API
December 27, 2017
What is a model in Keras and how do we design, train, evaluate and predict a model?
Configuration of Keras
6. Configuration of Keras
December 27, 2017
To get going with Keras, the first step would be to set up a GPU in an Amazon instance or a computer, and install Keras.
Optimization
5. Optimization
December 27, 2017
Optimization of models is very important and this video discusses about it.
Foundations of Neural Networks
4. Foundations of Neural Networks
December 27, 2017
In this video, we will learn essential concepts of fully connected architecture.
Machine Learning Concepts
3. Machine Learning Concepts
December 27, 2017
Like all other technologies, Machine Learning also has some basics concepts which are important.
What is Deep Learning?
2. What is Deep Learning?
December 27, 2017
Before going into the concepts of deep learning, it is important we understand what deep learning is.
The Course Overview
1. The Course Overview
December 27, 2017
This video provides an overview of the entire course.
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    December 27, 2017
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