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Building Machine Learning Systems with TensorFlow

Video Description

Engaging projects that will teach you how complex data can be exploited to gain the most insight

About This Video

  • Bored with too much theory on TensorFlow? This book is what you need! Thirteen solid projects and four examples teach you how to implement TensorFlow in production.

  • This example-rich guide teaches you how to perform highly accurate and efficient numerical computing with TensorFlow.

  • A practical and methodically explained guide that allows you to apply Tensorflow's features from scratch.

  • In Detail

    This video, with the help of practical projects, highlights how TensorFlow can be used in different scenarios—this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with tensors. Simply pick a project in line with your environment and get stacks of information on how to implement TensorFlow in production.

    Table of Contents

    1. Chapter 1 : Exploring and Transforming Data
      1. The Course Overview 00:03:24
      2. TensorFlow's Main Data Structure Tensors 00:07:15
      3. Handling the Computing Workflow TensorFlow's Data Flow Graph 00:05:25
      4. Basic Tensor Methods 00:08:23
      5. How TensorBoard Works? 00:05:32
      6. Reading Information from Disk 00:04:00
    2. Chapter 2 : Clustering
      1. Learning from Data Unsupervised Learning 00:02:15
      2. Mechanics of k-Means 00:03:35
      3. k-Nearest Neighbor 00:05:33
      4. Project 1 k-Means Clustering on Synthetic Datasets 00:04:08
      5. Project 2 Nearest Neighbor on Synthetic Datasets 00:01:53
    3. Chapter 3 : Linear Regression
      1. Univariate Linear Modelling Function 00:04:54
      2. Optimizer Methods in TensorFlow The Train Module 00:03:11
      3. Univariate Linear Regression 00:05:10
      4. Multivariate Linear Regression 00:05:15
    4. Chapter 4 :Logistic Regression
      1. Logistic Function Predecessor The Logit Functions 00:04:07
      2. The Logistic Function 00:05:54
      3. Univariate Logistic Regression 00:06:55
      4. Univariate Logistic Regression with keras 00:02:27
    5. Chapter 5 : Simple FeedForward Neural Networks
      1. Preliminary Concepts 00:07:41
      2. First Project Non-Linear Synthetic Function Regression 00:02:31
      3. Second Project Modeling Cars Fuel Efficiency with Non-Linear Regression 00:03:06
      4. Third Project Learning to Classify Wines: Multiclass Classification 00:02:56
    6. Chapter 6 : Convolutional Neural Networks
      1. Origin of Convolutional Neural Networks 00:03:26
      2. Applying Convolution in TensorFlow 00:03:55
      3. Subsampling Operation Pooling 00:02:57
      4. Improving Efficiency Dropout Operation 00:02:15
      5. Convolutional Type Layer Building Methods 00:01:03
      6. MNIST Digit Classification 00:03:30
      7. Image Classification with the CIFAR10 Dataset 00:02:27
    7. Chapter 7 : Recurrent Neural Networks and LSTM
      1. Recurrent Neural Networks 00:03:40
      2. A Fundamental Component Gate Operation and Its Steps 00:04:23
      3. TensorFlow LSTM Useful Classes and Methods 00:02:01
      4. Univariate Time Series Prediction with Energy Consumption Data 00:02:36
      5. Writing Music "a la" Bach 00:08:06
    8. Chapter 8 : Deep Neural Networks
      1. Deep Neural Network Definition and Architectures Through Time 00:02:35
      2. Alexnet 00:03:52
      3. Inception V3 00:01:00
      4. Residual Networks (ResNet) 00:02:06
      5. Painting with Style VGG Style Transfer 00:03:10
    9. Chapter 9 : Library Installation and Additional Tips
      1. Windows Installation 00:02:38
      2. mac OS Installation 00:02:57