Progressive Deep Learning with Keras in Practice

Progressive Deep Learning with Keras in Practice, available at $49.99, has an average rating of 4.17, with 81 lectures, 2 quizzes, based on 6 reviews, and has 136 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 Discover some advanced neural architectures such as generative adversarial networks Find out about a wide range of subjects from recommender systems to transfer learning Explore the Concepts of Convolutional Neural Networks and Recurrent Neural Networks Use Concepts, intuitive understating and applications of Autoencoders and Generative Adversarial Networks Build Autoencoders and Generative Adversarial Networks This course is ideal for individuals who are This course is perfect for: or Software developers, Data Scientists with experience in Machine Learning or an AI Programmer with some exposure to Neural Networks: would like to improve their skills and expertise in Machine Learning and more specifically Deep Learning. It is particularly useful for This course is perfect for: or Software developers, Data Scientists with experience in Machine Learning or an AI Programmer with some exposure to Neural Networks: would like to improve their skills and expertise in Machine Learning and more specifically Deep Learning.

Enroll now: Progressive Deep Learning with Keras in Practice

Summary

Title: Progressive Deep Learning with Keras in Practice

Price: $49.99

Average Rating: 4.17

Number of Lectures: 81

Number of Quizzes: 2

Number of Published Lectures: 81

Number of Published Quizzes: 2

Number of Curriculum Items: 83

Number of Published Curriculum Objects: 83

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

Discover some advanced neural architectures such as generative adversarial networks

Find out about a wide range of subjects from recommender systems to transfer learning

Explore the Concepts of Convolutional Neural Networks and Recurrent Neural Networks

Use Concepts, intuitive understating and applications of Autoencoders and Generative Adversarial Networks

Build Autoencoders and Generative Adversarial Networks

Who Should Attend

This course is perfect for:

Software developers, Data Scientists with experience in Machine Learning or an AI Programmer with some exposure to Neural Networks: would like to improve their skills and expertise in Machine Learning and more specifically Deep Learning.

Target Audiences

This course is perfect for:

Software developers, Data Scientists with experience in Machine Learning or an AI Programmer with some exposure to Neural Networks: would like to improve their skills and expertise in Machine Learning and more specifically Deep Learning.

Keras is an (Open source Neural Network library written in Python) Deep Learning library for fast, efficient training of Deep Learning models. It is a minimal, highly modular framework that runs on both CPUs and GPUs, and allows you to put your ideas into action in the shortest possible time. Because it is lightweight and very easy to use, Keras has gained quite a lot of popularity in a very short time.

This comprehensive 3-in-1 course takes a step-by-step practical approach to implement fast and efficient Deep Learning models: Projects on Image Processing, NLP, and Reinforcement Learning. Initially, you’ll learn backpropagation, install and configure Keras and understand callbacks and for customizing the process. You’ll build, train, and run fully-connected, Convolutional and Recurrent Neural Networks. You’ll also solve Supervised and Unsupervised learning problems using images, text and time series. Moving further, you’ll use concepts, intuitive understating and applications of Autoencoders and Generative Adversarial Networks. Finally, you’ll build projects on Image Processing, NLP, and Reinforcement Learning and build cutting-edge Deep Learning models in a simple, easy to understand way.

Towards the end of this course, you’ll get to grips with the basics of Keras to implement fast and efficient Deep Learning models: Projects on Image Processing, NLP, and Reinforcement Learning.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Deep Learning with Keras, covers implementing deep learning neural networks with Python. Keras is a high-level neural network library written in Python and runs on top of either Theano or TensorFlow. It is a minimal, highly modular framework that runs on both CPUs and GPUs, and allows you to put your ideas into action in the shortest possible time. This course will help you get started with the basics of Keras, in a highly practical manner.

The second course, Advanced Deep Learning with Keras,covers Deep learning with one of it’s most popular frameworks: Keras. This course provides a comprehensive introduction to deep learning. We start by presenting some famous success stories and a brief recap of the most common concepts found in machine learning. Then, we introduce neural networks and the optimization techniques to train them. We’ll show you how to get ready with Keras API to start training deep learning models, both on CPU and on GPU. Then, we present two types of neural architecture: convolutional and recurrent neural networks. First, we present a well-known use case of deep learning: recommender systems, where we try to predict the “rating” or “preference” that a user would give to an item. Then, we introduce an interesting subject called style transfer. Deep learning has this ability to transform images based on a set of inputs, so we’ll morph an image with a style image to combine them into a very realistic result. In the third section, we present techniques to train on very small datasets. This comprises transfer learning, data augmentation, and hyperparameter search, to avoid overfitting and to preserve the generalization property of the network. Finally, we complete this course by what Yann LeCun, Director at Facebook, considered as the biggest breakthrough in Machine Learning of the last decade: Generative Adversarial Networks. These networks are amazingly good at capturing the underlying distribution of a set of images to generate new images.

The third course, Keras Deep Learning Projects,covers Projects on Image Processing, NLP, and Reinforcement Learning. This course will show you how to leverage the power of Keras to build and train high performance, high accuracy deep learning models, by implementing practical projects in real-world domains. Spanning over three hours, this course will help you master even the most advanced concepts in deep learning and how to implement them with Keras. You will train CNNs, RNNs, LSTMs, Autoencoders and Generative Adversarial Networks using real-world training datasets. These datasets will be from domains such as Image Processing and Computer Vision, Natural Language Processing, Reinforcement Learning and more. By the end of this highly practical course, you will be well-versed with deep learning and its implementation with Keras. By the end of this course, you will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.

Towards the end of this course, you’ll get to grips with the basics of Keras to implement fast and efficient Deep Learning models: Projects on Image Processing, NLP, and Reinforcement Learning.

About the Authors

Antonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, innovation, and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing. So far, he has been lucky enough to gain professional experience in four different countries in Europe and has managed people in six different countries in Europe and America. Antonio served as CEO, GM, CTO, VP, director, and site lead in multiple fields ranging from publishing (Elsevier) to consumer internet (Ask and Tiscali) and high-tech R&D (Microsoft and Google).

Sujit Palis a technology research director at Elsevier Labs, working on building intelligent systems around research content and metadata. His primary interests are information retrieval, ontologies, natural language processing, machine learning, and distributed processing. He is currently working on image classification and similarity using deep learning models. Prior to this, he worked in the consumer healthcare industry, where he helped build ontology-backed semantic search, contextual advertising, and EMR data processing platforms. He writes about technology on his blog at Salmon Run.

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 on society with the ultimate goal of enhancing the 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/philipperemy). You can visit Philippe Remy’s blog on philipperemy.

Tsvetoslav Tsekovhas 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, Sports 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: Deep Learning with Keras

Lecture 1: The Course Overview

Lecture 2: Perceptron

Lecture 3: Building a Network to Recognize Handwritten Numbers

Lecture 4: Playing Around with the Parameters to Improve Performance

Lecture 5: Installing and Configuring Keras

Lecture 6: Keras API

Lecture 7: Callbacks for Customizing the Training Process

Lecture 8: Deep Convolutional Neural Network – DCNN

Lecture 9: Recognizing CIFAR-10 Images with Deep Learning

Chapter 2: 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 3: 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

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5 stars: 2 votes

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