Last updated 3/2019
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.72 GB | Duration: 9h 16m
Deep learning with one of its most popular frameworks: Keras: Build cutting-edge Deep Learning models with ease!
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
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
Requirements
While knowledge of the Keras framework is not required, it is assumed that you’re well versed with the Machine Learning concepts and Python programming language.
Description
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 OverviewThis 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 AuthorsAntonio 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 Pal is 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 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 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 Tsekov 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, 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.
Overview
Section 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
Section 2: Advanced Deep Learning with Keras
Lecture 10 The Course Overview
Lecture 11 What is Deep Learning?
Lecture 12 Machine Learning Concepts
Lecture 13 Foundations of Neural Networks
Lecture 14 Optimization
Lecture 15 Configuration of Keras
Lecture 16 Presentation of Keras and Its API
Lecture 17 Design and Train Deep Neural Networks
Lecture 18 Regularization in Deep Learning
Lecture 19 Introduction to Computer Vision
Lecture 20 Convolutional Networks
Lecture 21 CNN Architectures
Lecture 22 Image Classification Example
Lecture 23 Image Segmentation Example
Lecture 24 Introduction to Recurrent Networks
Lecture 25 Recurrent Neural Networks
Lecture 26 “One to Many” Architecture
Lecture 27 “Many to One” Architecture
Lecture 28 “Many to Many” Architecture
Lecture 29 Embedding Layers
Lecture 30 What are Recommender Systems?
Lecture 31 Content/Item Based Filtering
Lecture 32 Collaborative Filtering
Lecture 33 Hybrid System
Lecture 34 Introduction to Neural Style Transfer
Lecture 35 Single Style Transfer
Lecture 36 Advanced Techniques
Lecture 37 Style Transfer Explained
Lecture 38 Data Augmentation
Lecture 39 Transfer Learning
Lecture 40 Hyper Parameter Search
Lecture 41 Natural Language Processing
Lecture 42 An Introduction to Generative Adversarial Networks (GAN)
Lecture 43 Run Our First GAN
Lecture 44 Deep Convolutional Generative Adversarial Networks (DCGAN)
Lecture 45 Techniques to Improve GANs
Section 3: Keras Deep Learning Projects
Lecture 46 The Course Overview
Lecture 47 Jupyter Notebook Basics
Lecture 48 Data Shapes
Lecture 49 Neural Networks and How They Are Implemented with Keras
Lecture 50 Building Connected Layers and Applying Activation Functions
Lecture 51 Applying Loss Functions and Optimizers for Backpropagation
Lecture 52 Advanced Implementation with Keras
Lecture 53 Training the Model
Lecture 54 Testing the Model
Lecture 55 Metrics and Improving Performance
Lecture 56 Concepts of CNNs
Lecture 57 Applying Filters, Strides, Padding, and Pooling
Lecture 58 Basic Implementation with Keras
Lecture 59 Leaky Rectified Linear Units
Lecture 60 Dropout
Lecture 61 Advanced Implementation with Keras
Lecture 62 Training the Model
Lecture 63 Testing the Model and Metrics
Lecture 64 Transfer Learning
Lecture 65 Concepts and Applications of Autoencoders
Lecture 66 Basic Implementation with Keras
Lecture 67 Advanced Implementation with Keras
Lecture 68 Convolutional Autoencoder with Keras
Lecture 69 Training the Model
Lecture 70 Testing the Model
Lecture 71 Concepts of RNNs, LSTM Cells, and GRU Cells
Lecture 72 Data Preprocessing
Lecture 73 Building a Simple RNN Model in Keras
Lecture 74 Advanced Implementation with Keras
Lecture 75 Training the Model
Lecture 76 Testing the Model
Lecture 77 Concepts and Applications of GANs
Lecture 78 Batch Normalization
Lecture 79 Convolutional GAN with Keras
Lecture 80 Training the Model
Lecture 81 Testing the Model
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.
Homepage
https://www.udemy.com/course/progressive-deep-learning-with-keras-in-practice/
Download From 1DL
DOWNLOAD FROM 1DL.NET
DOWNLOAD FROM 1DL.NET
DOWNLOAD FROM 1DL.NET
DOWNLOAD FROM 1DL.NET
DOWNLOAD FROM RAPIDGATOR.NET
DOWNLOAD FROM RAPIDGATOR.NET
DOWNLOAD FROM RAPIDGATOR.NET
DOWNLOAD FROM RAPIDGATOR.NET
DOWNLOAD FROM UPLOADGIG.COM
DOWNLOAD FROM UPLOADGIG.COM
DOWNLOAD FROM UPLOADGIG.COM
DOWNLOAD FROM UPLOADGIG.COM
DOWNLOAD FROM NITROFLARE.COM
DOWNLOAD FROM NITROFLARE.COM
DOWNLOAD FROM NITROFLARE.COM
DOWNLOAD FROM NITROFLARE.COM