Learning Path Keras Deep Learning With Keras

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, 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
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 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 Remy is 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.
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 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
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.


Homepage

https://www.udemy.com/course/learning-path-keras-deep-learning-with-keras/