The Complete Guide to TensorFlow 1.x
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The Complete Guide to TensorFlow 1.x

Become an expert in machine learning and deep learning with the new TensorFlow 1.x
3.3 (2 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.
29 students enrolled
Created by Packt Publishing
Last updated 6/2017
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  • 2.5 hours on-demand video
  • 8 Articles
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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
View Curriculum
  • Knowledge of Python is a must
  • Basic knowledge of Math and Statistics would be beneficial, however is not mandatory

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 is the target audience?
  • 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
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Curriculum For This Course
48 Lectures
Getting Started with Machine Learning and Deep Learning
2 Lectures 24:34

A quick overview

Test Your Knowledge
10 questions
First Look at TensorFlow
1 Lecture 22:42
Up and running with TensorFlow

Test Your Knowledge
4 questions
Exploring and Transforming Data
5 Lectures 29:38

In this video, we will explore the basics of data management on tensor.

Preview 06:46

The aim of this video is to show how to handle the computing workflow of TensorFlow's data flow graph.

Handling the computing workflow – TensorFlow's data flow graph

In this video, we will explore some basic methods supported by TensorFlow.

Basic tensor methods

In this video, we will learn how TensorBoard works.

How TensorBoard works?

The aim of this video is to show how to read information from a disk.

Reading information from a disk

Test Your Knowledge
2 questions
6 Lectures 16:50

In this video, we will learn to review two cases of unsupervised learning.

Learning from data – unsupervised learning

Understanding clustering

The aim of this video is to show the mechanics of k-means.

Mechanics of k-means

In this video, we will discuss the k-nearest neighbor.

k-nearest neighbor

In this video, we will explore a few topics about the clustering on synthetic datasets.

Project 1 – k-means clustering on synthetic datasets

The aim of this video is to show how to load a dataset with which the k-means algorithm has problems separating classes.

Project 2 – nearest neighbor on synthetic datasets

Test Your Knowledge
1 question
Linear Regression
4 Lectures 17:15

The aim of this video is to show how to interact with linear equation using univariate linear modelling function.

Univariate linear modeling function

In this video, we will learn to train the optimization stage, which is a vital part of the machine learning workflow.

Optimizer methods in TensorFlow – the train module

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.

Univariate linear regression

Multivariate linear regression
Logistic Regression
4 Lectures 17:51

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.

Logistic function predecessor – the logit functions

In this video, we will discuss the logistic function that will serve us to represent the binary options in our new regression tasks.

The logistic function

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

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.

Univariate logistic regression with Keras
Reinforcement Learning
1 Lecture 12:56
Dive into reinforcement learning

Test Your Knowledge
5 questions
Simple Feed-Forward Neural Networks
4 Lectures 14:52

The aim of this video is to show how to build a simple framework into the neural network components and architectures.

Preliminary concepts

In this video, we will see how close we can be predicting values taken from a test population.

First project – nonlinear synthetic function regression

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.

Second project – modeling cars fuel efficiency with nonlinear regression

In this video, we will see how to work with a more complex dataset.

Third project – learning to classify wines (multiclass classification)
Convolutional Neural Networks
7 Lectures 17:58

In this video, we will explore the historical development of the operation and then start looking at convolution in the continuous domain.

Origin of convolutional neural networks

In this video, we will see how to apply convolution in TensorFlow.

Applying convolution in TensorFlow

The aim of this video is to show how to get the maximum and the average of the elements for an applied kernel.

Subsampling operation – pooling

In this video, we will see how the dropout operation reduces the value of some randomly selected weights to zero.

Improving efficiency – dropout operation

In this video, we will see how to use simple utility functions to facilitate the building of convolutional layers:

Convolutional type layer building methods

In this video, we will see how to work for the first time on one of the most well-known datasets for pattern recognition.

MNIST digit classification

In this video, we will see how to work on one of the most extensively used datasets in image comprehension.

Image classification with the CIFAR10 dataset
1 Lecture 21:03
Optimizing TensorFlow autoencoders

Test Your Knowledge
2 questions
5 More Sections
About the Instructor
Packt Publishing
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Tech Knowledge in Motion

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 Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.