by Will Ballard | Released October 2017 | ISBN: 9781788396899

Use Tensorflow's capabilities to perform efficient deep learning on ImagesAbout This VideoBuild practical, real-world deep learning projects in TensorflowMaster the different deep learning paradigms and build real-world projects related to Computer Vision, Natural Language Understanding, and deep learningYour handy tutorial to mastering deep learning with Tensorflow through interesting use cases to ensure a quality learning experienceIn DetailTensorflow is Google's popular offering for machine learning and deep learning. It has quickly become a popular choice of tool for performing fast, efficient, and accurate deep learning. This course presents the implementation of practical, real-world projects, teaching you how to leverage Tensforflow's capabilties to perform efficient deep learning.In this video, you will be acquainted with the different paradigms of performing deep learning such as deep neural nets, convolutional neural networks, recurrent neural networks, and more, and how they can be implemented using Tensorflow.This will be demonstrated with the help of end-to-end implementations of three real-world projects on popular topic areas such as natural language processing, image classification, fraud detection, and more. By the end of this course, you will have mastered all the concepts of deep learning and their implementation with Tensorflow and Keras. Show and hide more

1. Chapter 1 : Machine Learning Toolkit
   • The Course Overview 00:02:52
   • Installing Docker 00:04:09
   • The Machine Learning Dockerfile 00:04:09
   • Sharing Data 00:02:59
   • Machine Learning REST Service 00:03:15
2. Chapter 2 : Image Data
   • MNIST Digits 00:03:17
   • Tensors: Just Multidimensional Arrays 00:02:19
   • Turning Images into Tensors 00:02:21
   • Turning Categories into Tensors 00:02:48
3. Chapter 3 : Classical Neural Network
   • Classical/Dense Neural Network 00:02:46
   • Activation and Non Linearity 00:02:25
   • Softmax 00:03:00
   • Training and Testing Data 00:02:55
   • Dropout and Flatten 00:03:07
   • Solvers 00:03:45
   • Hyperparameters 00:02:18
   • Grid Search 00:06:31
4. Chapter 4 : A Convolutional Neural Network
   • Convolutions 00:03:24
   • Pooling 00:03:44
   • Convolutional Neural Network 00:03:23
   • Deep Neural Network 00:05:54
5. Chapter 5 : An Image Classification Server
   • REST API Definition 00:05:40
   • Trained Models in Docker Containers 00:04:47
   • Making Predictions 00:03:18
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