Building Machine Learning Systems with TensorFlow
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Building Machine Learning Systems with TensorFlow

Engaging projects that will teach you how complex data can be exploited to gain the most insight
3.5 (6 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
102 students enrolled
Created by Packt Publishing
Last updated 5/2017
English
Current price: $10 Original price: $125 Discount: 92% off
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Includes:
  • 2.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Load, interact, dissect, process, and save complex datasets
  • Solve classification and regression problems using state-of-the-art techniques
  • Predict the outcome of a simple time series using Linear Regression modeling
  • Use a Logistic Regression scheme to predict the future result of a time series
  • Classify images using deep neural network schemes
  • Tag a set of images and detect features using a deep neural network, including a Convolutional Neural Network (CNN) layer
  • Resolve character-recognition problems using the Recurrent Neural Network (RNN) model
View Curriculum
Requirements
  • Some experience with C++ and Python is expected.
Description

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.

About the Author

Rodolfo Bonnin is a Systems Engineer and PhD student at Universidad Tecnológica Nacional, Argentina. He also pursued parallel programming and image understanding postgraduate courses at Uni Stuttgart, Germany.

He has done research on High Performance Computing since 2005 and began studying and implementing convolutional neural networks in 2008,writing a CPU and GPU - supporting neural network feed forward stage. More recently he's been working in the field of fraud pattern detection with Neural Networks, and is currently working on signal classification using ML techniques.

He is also the author of the book “Building Machine Learning projects with Tensorflow”, by Packt Publishing.

Who is the target audience?
  • 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.
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Curriculum For This Course
42 Lectures
02:43:52
+
Exploring and Transforming Data
6 Lectures 33:57

This video provides an overview of the entire course.

Preview 03:24

Learn to know the basics of data management on tensors.

TensorFlow's Main Data Structure – Tensors
07:14

Learn how to handle the computing workflow of TensorFlow’s data flow graph.

Handling the Computing Workflow – TensorFlow's Data Flow Graph
05:25

Explore some basic methods supported by TensorFlow.

Basic Tensor Methods
08:22

Learn to know how TensorBoard works.

How TensorBoard Works?
05:32

Ability to read information from disk.

Reading Information from Disk
04:00
+
Clustering
5 Lectures 17:21

Learn to review two cases of unsupervised learning.

Preview 02:15

Learn to know the mechanics of k-means.

Mechanics of k-Means
03:34

Ability to know the k-nearest neighbors.

k-Nearest Neighbor
05:33

Learn the few topics about the clustering on synthetic datasets.

Project 1 – k-Means Clustering on Synthetic Datasets
04:07

Ability to load a dataset with which the k-means algorithm has problems separating classes.

Project 2 – Nearest Neighbor on Synthetic Datasets
01:52
+
Linear Regression
4 Lectures 18:28

Learn to interact with linear equation using univariate linear modelling function.

Preview 04:53

Learn to train the optimization stage which is a vital part of the machine learning workflow.

Optimizer Methods in TensorFlow – The Train Module
03:11

Ability to create a regression model that tries to fit a linear function that minimizes the error function.

Univariate Linear Regression
05:10

Ability to work on a regression problem involving more than one variable.

Multivariate Linear Regression
05:14
+
Logistic Regression
4 Lectures 19:22

Learn to review the original function on which it is based, and which gives it some of its more general properties.

Preview 04:07

Learn to know the logistic function which will serve us to represent the binary options in our new regression tasks.

The Logistic Function
05:53

Ability to work approximating the probability of the presence of heart disease, using an univariate logistic regression.

Univariate Logistic Regression
06:55

Explore the univariate examples domain which will use help from a new library, which eases the model building for us, called keras.

Univariate Logistic Regression with keras
02:27
+
Simple FeedForward Neural Networks
4 Lectures 16:13

Learn to build a simple framework into the neural network components and architectures.

Preview 07:41

Learn to know how close we can be predicting values taken from a test population.

First Project – Non-Linear Synthetic Function Regression
02:31

Learn to build a regression model for the fuel efficiency of several car models, based on several variables.

Second Project – Modeling Cars Fuel Efficiency with Non-Linear Regression
03:05

Ability to work with a more complex dataset.

Third Project – Learning to Classify Wines: Multiclass Classification
02:56
+
Convolutional Neural Networks
7 Lectures 19:31

Learn to know the historical development of the operation and then start looking at convolution in the continuous domain.

Preview 03:26

Ability to apply convolution in TensorFlow.

Applying Convolution in TensorFlow
03:55

Ability to get the maximum and the average of the elements for an applied kernel.

Subsampling Operation –Pooling
02:56

Ability to know how the dropout operation reduces the value of some randomly selected weights to zero.

Improving Efficiency – Dropout Operation
02:15

Learn to use simple utility functions to facilitate the building of convolutional layers.

Convolutional Type Layer Building Methods
01:02

Learn to work for the first time on one of the most well-known datasets for pattern recognition.

MNIST Digit Classification
03:30

Learn to work on one of the most extensively used datasets in image comprehension.

Image Classification with the CIFAR10 Dataset
02:27
+
Recurrent Neural Networks and LSTM
5 Lectures 20:45

Define a sequential model of neural networks, which have the property of reusing information already given.

Preview 03:39

Ability to understand the building blocks of the internal of the LSTM cell and also we will describe the main operational block of the LSTM.

A Fundamental Component – Gate Operation and Its Steps
04:23

Learn to review the main classes and methods that we can use to build a LSTM layer.

TensorFlow LSTM Useful Classes and Methods
02:01

Gain the ability to solve a problem of the domain of regression.

Univariate Time Series Prediction with Energy Consumption Data
02:36

Learn to work with a recurrent neural network specialized in character sequences, or the char RNN model.

Writing Music "a la" Bach
08:06
+
Deep Neural Networks
5 Lectures 12:40

Exploring to the Neural Network architectures with like tens of layer, or combinations of complex constructs. 

Preview 02:34

Learn to extend the complexity of the models.

Alexnet
03:52

Learn to illustrate how the improved inception module can be interpreted.

Inception V3
00:59

Achieve the ability to use the output of each constitutional layer, and also combine the output of the layer with the original input.

Residual Networks (ResNet)
02:05

Learn to work with the implementation of the paper A Neural Algorithm of Artistic Style from Leon Gatys.

Painting with Style – VGG Style Transfer
03:10
+
Library Installation and Additional Tips
2 Lectures 05:35

Gain the ability to develop TensorFlow with the Windows operating system.

Preview 02:38

Learn to install TensorFlow on mac OS.

MacOS Installation
02:57
About the Instructor
Packt Publishing
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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.

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