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Grasp all the knowledge you need to train your own deep learning models to solve different kinds of problems

What you’ll 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, 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

Requirements

Prior knowledge of Python and Keras is a must.

Description

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 . 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, 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 Remy is a research eeer and entrepreneur working on deep learning and living in Tokyo, Japan. As a research eeer, Philippe reads scientific papers and implements artificial intelligence algorithms related to handwriting character recognition, 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.

Overview

Section 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 Sntation 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

Section 2: Keras Deep Learning Projects

Lecture 37 The Course Overview

Lecture 38 Jupyter Notebook Basics

Lecture 39 Data Shapes

Lecture 40 Neural Networks and How They Are Implemented with Keras

Lecture 41 Building Connected Layers and Applying Activation Functions

Lecture 42 Applying Loss Functions and Optimizers for Backpropagation

Lecture 43 Advanced Implementation with Keras

Lecture 44 Training the Model

Lecture 45 Testing the Model

Lecture 46 Metrics and Improving Performance

Lecture 47 Concepts of CNNs

Lecture 48 Applying Filters, Strides, Padding, and Pooling

Lecture 49 Basic Implementation with Keras

Lecture 50 Leaky Rectified Linear Units

Lecture 51 Dropout

Lecture 52 Advanced Implementation with Keras

Lecture 53 Training the Model

Lecture 54 Testing the Model and Metrics

Lecture 55 Transfer Learning

Lecture 56 Concepts and Applications of Autoencoders

Lecture 57 Basic Implementation with Keras

Lecture 58 Advanced Implementation with Keras

Lecture 59 Convolutional Autoencoder with Keras

Lecture 60 Training the Model

Lecture 61 Testing the Model

Lecture 62 Concepts of RNNs, LSTM Cells, and GRU Cells

Lecture 63 Data Preprocessing

Lecture 64 Building a Simple RNN Model in Keras

Lecture 65 Advanced Implementation with Keras

Lecture 66 Training the Model

Lecture 67 Testing the Model

Lecture 68 Concepts and Applications of GANs

Lecture 69 Batch Normalization

Lecture 70 Convolutional GAN with Keras

Lecture 71 Training the Model

Lecture 72 Testing the Model

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.