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Building machine learning systems with tensorflow

171 minutes of course content

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Course 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.

After taking this course, you will be able to:

  • Load, interact, dissect, process, and save complex datasets
  • Predict the outcome of a simple time series using Linear Regression modeling
  • Classify images using deep neural network schemes
  • Solve classification and regression problems using state of the art techniques
  • Use a logistic regression scheme to predict the future result of a time series
  • Tag a set of images and detect features using a deep neural network, including a convolutional neural network layer

This course is for professionals within the following business functions:

  • This video course is for data analysts, data scientists, and researchers who want to increase the speed and efficiency of their machine learning activities and results. Anyone looking for a fresh guide to complex numerical computations with TensorFlow will find this an extremely helpful resource. This video is also for developers who want to implement TensorFlow in production in various scenarios.

      1. Exploring and Transforming Data - 38 Minutes

        1. The Course Overview
        2. TensorFlow's Main Data Structure – Tensors
        3. Handling the Computing Workflow – TensorFlow's Data Flow Graph
        4. Basic Tensor Methods
        5. How TensorBoard Works?
        6. Reading Information from Disk
      2. Clustering - 18 Minutes

        1. Learning from Data –Unsupervised Learning
        2. Mechanics of k-Means
        3. k-Nearest Neighbor
        4. Project 1 – k-Means Clustering on Synthetic Datasets
        5. Project 2 – Nearest Neighbor on Synthetic Datasets
      3. Linear Regression - 19 Minutes

        1. Univariate Linear Modelling Function
        2. Optimizer Methods in TensorFlow – The Train Module
        3. Univariate Linear Regression
        4. Multivariate Linear Regression
      4. Logistic Regression - 20 Minutes

        1. Logistic Function Predecessor – The Logit Functions
        2. The Logistic Function
        3. Univariate Logistic Regression
        4. Univariate Logistic Regression with keras
      5. Simple FeedForward Neural Networks - 16 Minutes

        1. Preliminary Concepts
        2. First Project – Non-Linear Synthetic Function Regression
        3. Second Project – Modeling Cars Fuel Efficiency with Non-Linear Regression
        4. Third Project – Learning to Classify Wines: Multiclass Classification
      6. Convolutional Neural Networks - 20 Minutes

        1. Origin of Convolutional Neural Networks
        2. Applying Convolution in TensorFlow
        3. Subsampling Operation –Pooling
        4. Improving Efficiency – Dropout Operation
        5. Convolutional Type Layer Building Methods
        6. MNIST Digit Classification
        7. Image Classification with the CIFAR10 Dataset
      7. Recurrent Neural Networks and LSTM - 21 Minutes

        1. Recurrent Neural Networks
        2. A Fundamental Component – Gate Operation and Its Steps
        3. TensorFlow LSTM Useful Classes and Methods
        4. Univariate Time Series Prediction with Energy Consumption Data
        5. Writing Music "a la" Bach
      8. Deep Neural Networks - 13 Minutes

        1. Deep Neural Network Definition and Architectures Through Time
        2. Alexnet
        3. Inception V3
        4. Residual Networks (ResNet)
        5. Painting with Style – VGG Style Transfer
      9. Library Installation and Additional Tips - 6 Minutes

        1. Windows Installation
        2. macOS Installation

      About the Expert



      Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work. With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now. From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer. Packt courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.
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