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The Complete Guide to TensorFlow 1.x

Name: The Complete Guide to TensorFlow 1.x
Rating: 4.1 (13 reviews)

Become an expert in machine learning and deep learning with the new TensorFlow 1.x

Created byPackt Publishing

Last updated 8/2017

English

English [Auto],

What you'll learn

Learn about machine learning landscapes along with the historical development and progress of deep learning
Load, interact, process, and save complex datasets
Solve classification and regression problems using state-of-the-art techniques
Train machines quickly to learn from data by exploring reinforcement learning techniques
Classify images using deep neural network schemes
Learn about deep machine intelligence and GPU computing
Explore active areas of deep learning research and applications

Course content

15 sections • 48 lectures • 4h 35m total length

Course Introduction6:26
Explore TensorFlow 1.x for practical machine learning and deep learning, covering neural networks, convolutional networks, reinforcement learning, and real-world projects, including android-based deep learning.
A quick overview17:55
Test Your Knowledge

TensorFlow's main data structure – tensors6:46
In this video, we will explore the basics of data management on tensor.
Handling the computing workflow – TensorFlow's data flow graph5:21
The aim of this video is to show how to handle the computing workflow of TensorFlow's data flow graph.
Basic tensor methods8:16
In this video, we will explore some basic methods supported by TensorFlow.
How TensorBoard works?5:28
In this video, we will learn how TensorBoard works.
Reading information from a disk3:47
The aim of this video is to show how to read information from a disk.
Test Your Knowledge

Learning from data – unsupervised learning1:33
In this video, we will learn to review two cases of unsupervised learning.
Understanding clustering0:33
Mechanics of k-means3:31
The aim of this video is to show the mechanics of k-means.
k-nearest neighbor5:28
In this video, we will discuss the k-nearest neighbor.
Project 1 – k-means clustering on synthetic datasets4:02
In this video, we will explore a few topics about the clustering on synthetic datasets.
Project 2 – nearest neighbor on synthetic datasets1:42
The aim of this video is to show how to load a dataset with which the k-means algorithm has problems separating classes.
Test Your Knowledge

Univariate linear modeling function4:17
The aim of this video is to show how to interact with linear equation using univariate linear modelling function.
Optimizer methods in TensorFlow – the train module3:06
In this video, we will learn to train the optimization stage, which is a vital part of the machine learning workflow.
Univariate linear regression5:04
The aim of this video is to show how to create a regression model that tries to fit a linear function that minimizes the error function.
Multivariate linear regression4:48

Logistic function predecessor – the logit functions3:17
In this video, we will learn to review the original function on which it is based, and which gives it some of its more general properties.
The logistic function5:47
In this video, we will discuss the logistic function that will serve us to represent the binary options in our new regression tasks.
Univariate logistic regression6:49
In this video, we will learn to work approximating the probability of the presence of heart disease, using an univariate logistic regression.
Univariate logistic regression with Keras1:58
In this video, we will explore the univariate examples domain that will use help from a new library (called Keras), which eases the model building for us.

Preliminary concepts7:08
The aim of this video is to show how to build a simple framework into the neural network components and architectures.
First project – nonlinear synthetic function regression2:24
In this video, we will see how close we can be predicting values taken from a test population.
Second project – modeling cars fuel efficiency with nonlinear regression2:57
The aim of this video is to show how to build a regression model for the fuel efficiency of several car models, based on several variables.
Third project – learning to classify wines (multiclass classification)2:23
In this video, we will see how to work with a more complex dataset.

Origin of convolutional neural networks2:50
In this video, we will explore the historical development of the operation and then start looking at convolution in the continuous domain.
Applying convolution in TensorFlow3:50
In this video, we will see how to apply convolution in TensorFlow.
Subsampling operation – pooling2:51
The aim of this video is to show how to get the maximum and the average of the elements for an applied kernel.
Improving efficiency – dropout operation2:10
In this video, we will see how the dropout operation reduces the value of some randomly selected weights to zero.
Convolutional type layer building methods0:58
In this video, we will see how to use simple utility functions to facilitate the building of convolutional layers:
MNIST digit classification3:23
In this video, we will see how to work for the first time on one of the most well-known datasets for pattern recognition.
Image classification with the CIFAR10 dataset1:56
In this video, we will see how to work on one of the most extensively used datasets in image comprehension.

Requirements

Knowledge of Python is a must
Basic knowledge of Math and Statistics would be beneficial, however is not mandatory

Description

Are you a data analyst, data scientist, or a researcher looking for a guide that will help you increase the speed and efficiency of your machine learning activities? If yes, then this course is for you!

Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. It has helped engineers, researchers, and many others make significant progress with everything from voice/sound recognition to language translation and face recognition. It has also proved to be useful in the early detection of skin cancer and preventing blindness in diabetics. TensorFlow is designed to make distributed machine and deep learning easy for everyone, but using it requires understanding some general principles and algorithms. Furthermore, the latest release of TensorFlow comes with lots of exciting features. It’s incredibly fast, flexible, and more production-ready than ever!

The aim of this course is to help you tackle common commercial machine learning and deep learning problems that you’re facing in your day-to-day activities.

What is included?

Let’s take a look at the learning journey. The course begins with an introduction to machine learning and deep learning. You will explore the main features and capabilities of TensorFlow such as a computation graph, data model, programming model, and TensorBoard. The key highlight here is that this course will teach you how to upgrade your code from TensorFlow 0.x to TensorFlow 1.x. Next, you will learn the different techniques of machine learning such as clustering, linear regression, and logistic regression with the help of real-world projects and examples. You will also learn the concepts of reinforcement learning, the Q-learning algorithm, and the OpenAI Gym framework. Moving ahead, you will dive into neural networks and see how convolution, recurrent, and deep neural networks work and the main operation types used in building them. Next, you will learn advanced concepts such as GPU computing and multimedia programming. Finally, the course will demonstrate an example on deep learning on Android using TensorFlow.

By the end of this course, you will have a solid knowledge of the all-new TensorFlow and be able to implement it efficiently in production.

For this course, we have combined the best works of these extremely esteemed authors:

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, Packt Publishing.

Giancarlo Zaccone has more than ten years of experience in managing research projects both in scientific and industrial areas. He worked as a researcher at the National Research Council, where he was involved in projects relating to parallel computing and scientific visualization.

Currently, he is a system and software engineer at a consulting company developing and maintaining software systems for space and defense applications.

He is author of the following Packt books: Python Parallel Programming Cookbook and Getting Started with TensorFlow.

Md. Rezaul Karim has more than 8 years of experience in the area of research and development with a solid knowledge of algorithms and data structures in C/C++, Java, Scala, R, and Python, focusing on Big Data technologies such as Spark, Kafka, DC/OS, Docker, Mesos, Zeppelin, Hadoop, and MapReduce, and deep learning technologies such as TensorFlow, DeepLearning4j, and H2O-Sparking Water. His research interests include machine learning, deep learning, semantic web/linked data, Big Data, and bioinformatics.

Ahmed Menshawy is a research engineer at the Trinity College, Dublin, Ireland. He has more than 5 years of working experience in the area of machine learning and natural language processing (NLP). He holds an MSc in Advanced Computer Science. He started his career as a teaching assistant at the Department of Computer Science, Helwan University, Cairo, Egypt.

Who this course is for:

This 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 course extremely helpful

The Complete Guide to TensorFlow 1.x

What you'll learn

Explore related topics

Course content

Getting Started with Machine Learning and Deep Learning2 lectures • 24min

First Look at TensorFlow1 lecture • 21min

Exploring and Transforming Data5 lectures • 30min

Clustering6 lectures • 17min

Linear Regression4 lectures • 17min

Logistic Regression4 lectures • 18min

Reinforcement Learning1 lecture • 12min

Simple Feed-Forward Neural Networks4 lectures • 15min

Convolutional Neural Networks7 lectures • 18min

Autoencoders1 lecture • 19min

Requirements

Description

Who this course is for: