Last updated 3/2018MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 818.43 MB | Duration: 7h 54m
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. 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