LEARNING PATH: Keras: Deep Learning with Keras

LEARNING PATH: Keras: Deep Learning with Keras, available at $19.99, has an average rating of 3.3, with 72 lectures, 2 quizzes, based on 14 reviews, and has 150 subscribers.

You will learn about Understand the main concepts of machine learning and deep learning Build, train, and run fully-connected, convolutional and recurrent neural networks Optimize deep neural networks through efficient hyper parameter searches Work with any kind of data involving images, text, time series, sound and videos Use GPUs to leverage the training experience Build your own Multilayer Neural Networks Build Convolutional Neural Networks and Recurrent Neural Networks Build Auto encoders and Generative Adversarial Networks This course is ideal for individuals who are This Learning Path is geared towards software developers and machine learning enthusiasts who would like to improve their skills and expertise in machine learning and more specifically deep learning. It is particularly useful for This Learning Path is geared towards software developers and machine learning enthusiasts who would like to improve their skills and expertise in machine learning and more specifically deep learning.

Enroll now: LEARNING PATH: Keras: Deep Learning with Keras

Summary

Title: LEARNING PATH: Keras: Deep Learning with Keras

Price: $19.99

Average Rating: 3.3

Number of Lectures: 72

Number of Quizzes: 2

Number of Published Lectures: 72

Number of Published Quizzes: 2

Number of Curriculum Items: 74

Number of Published Curriculum Objects: 74

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the main concepts of machine learning and deep learning
Build, train, and run fully-connected, convolutional and recurrent neural networks
Optimize deep neural networks through efficient hyper parameter searches
Work with any kind of data involving images, text, time series, sound and videos
Use GPUs to leverage the training experience
Build your own Multilayer Neural Networks
Build Convolutional Neural Networks and Recurrent Neural Networks
Build Auto encoders and Generative Adversarial Networks

Who Should Attend

This Learning Path is geared towards software developers and machine learning enthusiasts who would like to improve their skills and expertise in machine learning and more specifically deep learning.

Target Audiences

This Learning Path is geared towards software developers and machine learning enthusiasts who would like to improve their skills and expertise in machine learning and more specifically deep learning.

Keras is a deep learning library written in Python for quick, efficient training of deep learning models, and can also work with Tensorflow and Theano. Because of its lightweight and very easy to use nature, Keras has become popularity in a very short span of time. So, if you are a data scientist with experience in machine learning with some exposure to neural networks, then go for this Learning Path.

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

The highlights of this Learning Path are:

Understand the main concepts of machine learning and deep learning
Work with any kind of data involving images, text, time series, sound and videos
Learn to build auto encoders and generative adversarial networks

Let’s take a quick look at your learning journey. You will start with the basics of Keras, in a highly practical manner. You will then dive into deep learning with convolutional and recurrent neural networks, which are the cornerstones of deep learning. You will then take to look at recommender system and some of its types. You will move ahead with a popular Keras framework for style transfer, some advanced techniques and in-depth explanations of the style transfer mechanism. You will also learn to build, train and run generative adversarial networks, go through some of its most popular architectures, and learn techniques to make them work better. Next, you will get an hands-on training of CNNs, RNNs, LSTMs, autoencoders and generative adversarial networks using real-world training datasets. Finally, you will learn the concepts and applications of generative adversarial networks, implementation with Keras, using Batch Normalization to improve performance.

By the end of this Learning Path, you will be well-versed with deep learning and its implementation with Keras and will be able to solve different kinds of problems.

Meet Your Expert:

We have the best works of the following esteemed author to ensure that your learning journey is smooth:

Philippe Remyis a research engineer and entrepreneur working on deep learning and living in Tokyo, Japan. As a research engineer, Philippe reads scientific papers and implements artificial intelligence algorithms related to handwriting character recognition, time series analysis, and natural language processing. As an entrepreneur, his vision is to bring a meaningful and transformative impact to society with the ultimate goal of enhancing overall quality of life and pushing the limits of what is considered possible today. Philippe contributes to different open source projects related to deep learning and fintech (github. com/philipperemy). You can visit Philippe Remy’s blog on philipperemy . github .io.
TsvetoslavTsekov has worked for 5 years on various software development projects – desktop applications, backend applications, WinCE embedded software, RESTful APIs. He then became exceedingly interested in Artificial Intelligence and particularly Deep Learning. After receiving his Deep Learning Nanodegree, he has worked on numerous projects – Image Classification, Sport Results Prediction, Fraud Detection, and Machine Translation. He is also very interested in General AI research and is always trying to stay up to date with the cutting-edge developments in the field.

Course Curriculum

Chapter 1: Advanced Deep Learning with Keras

Lecture 1: The Course Overview

Lecture 2: What is Deep Learning?

Lecture 3: Machine Learning Concepts

Lecture 4: Foundations of Neural Networks

Lecture 5: Optimization

Lecture 6: Configuration of Keras

Lecture 7: Presentation of Keras and Its API

Lecture 8: Design and Train Deep Neural Networks

Lecture 9: Regularization in Deep Learning

Lecture 10: Introduction to Computer Vision

Lecture 11: Convolutional Networks

Lecture 12: CNN Architectures

Lecture 13: Image Classification Example

Lecture 14: Image Segmentation Example

Lecture 15: Introduction to Recurrent Networks

Lecture 16: Recurrent Neural Networks

Lecture 17: “One to Many” Architecture

Lecture 18: “Many to One” Architecture

Lecture 19: “Many to Many” Architecture

Lecture 20: Embedding Layers

Lecture 21: What are Recommender Systems?

Lecture 22: Content/Item Based Filtering

Lecture 23: Collaborative Filtering

Lecture 24: Hybrid System

Lecture 25: Introduction to Neural Style Transfer

Lecture 26: Single Style Transfer

Lecture 27: Advanced Techniques

Lecture 28: Style Transfer Explained

Lecture 29: Data Augmentation

Lecture 30: Transfer Learning

Lecture 31: Hyper Parameter Search

Lecture 32: Natural Language Processing

Lecture 33: An Introduction to Generative Adversarial Networks (GAN)

Lecture 34: Run Our First GAN

Lecture 35: Deep Convolutional Generative Adversarial Networks (DCGAN)

Lecture 36: Techniques to Improve GANs

Chapter 2: Keras Deep Learning Projects

Lecture 1: The Course Overview

Lecture 2: Jupyter Notebook Basics

Lecture 3: Data Shapes

Lecture 4: Neural Networks and How They Are Implemented with Keras

Lecture 5: Building Connected Layers and Applying Activation Functions

Lecture 6: Applying Loss Functions and Optimizers for Backpropagation

Lecture 7: Advanced Implementation with Keras

Lecture 8: Training the Model

Lecture 9: Testing the Model

Lecture 10: Metrics and Improving Performance

Lecture 11: Concepts of CNNs

Lecture 12: Applying Filters, Strides, Padding, and Pooling

Lecture 13: Basic Implementation with Keras

Lecture 14: Leaky Rectified Linear Units

Lecture 15: Dropout

Lecture 16: Advanced Implementation with Keras

Lecture 17: Training the Model

Lecture 18: Testing the Model and Metrics

Lecture 19: Transfer Learning

Lecture 20: Concepts and Applications of Autoencoders

Lecture 21: Basic Implementation with Keras

Lecture 22: Advanced Implementation with Keras

Lecture 23: Convolutional Autoencoder with Keras

Lecture 24: Training the Model

Lecture 25: Testing the Model

Lecture 26: Concepts of RNNs, LSTM Cells, and GRU Cells

Lecture 27: Data Preprocessing

Lecture 28: Building a Simple RNN Model in Keras

Lecture 29: Advanced Implementation with Keras

Lecture 30: Training the Model

Lecture 31: Testing the Model

Lecture 32: Concepts and Applications of GANs

Lecture 33: Batch Normalization

Lecture 34: Convolutional GAN with Keras

Lecture 35: Training the Model

Lecture 36: Testing the Model

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 2 votes
2 stars: 2 votes
3 stars: 1 votes
4 stars: 2 votes
5 stars: 7 votes

Frequently Asked Questions

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