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

Course Description

This course, 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.

Learning Outcomes

  • 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


Some experience with C++ and Python is expected.

Who is this course intended for?

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. Some experience with C++ and Python is expected.

Your Instructor

Packt Publishing

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 everyday tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Course Curriculum

Exploring and Transforming Data
The Course Overview 00:00:00
TensorFlow’s Main Data Structure – Tensors 00:00:00
Handling the Computing Workflow – TensorFlow’s Data Flow Graph 00:00:00
Basic Tensor Methods 00:00:00
How TensorBoard Works? 00:00:00
Reading Information from Disk 00:00:00
Learning from Data –Unsupervised Learning 00:00:00
Mechanics of k-Means 00:00:00
k-Nearest Neighbor 00:00:00
Project 1 – k-Means Clustering on Synthetic Datasets 00:00:00
Project 2 – Nearest Neighbor on Synthetic Datasets 00:00:00
Linear Regression
Univariate Linear Modelling Function 00:00:00
Optimizer Methods in TensorFlow – The Train Module 00:00:00
Univariate Linear Regression 00:00:00
Multivariate Linear Regression 00:00:00
Logistic Regression
Logistic Function Predecessor – The Logit Functions 00:00:00
The Logistic Function 00:00:00
Univariate Logistic Regression 00:00:00
Univariate Logistic Regression with keras 00:00:00
Simple FeedForward Neural Networks
Preliminary Concepts 00:00:00
First Project – Non-Linear Synthetic Function Regression 00:00:00
Second Project – Modeling Cars Fuel Efficiency with Non-Linear Regression 00:00:00
Third Project – Learning to Classify Wines: Multiclass Classification 00:00:00
Convolutional Neural Networks
Origin of Convolutional Neural Networks 00:00:00
Applying Convolution in TensorFlow 00:00:00
Subsampling Operation – Pooling 00:00:00
Improving Efficiency – Dropout Operation 00:00:00
Convolutional Type Layer Building Methods 00:00:00
MNIST Digit Classification 00:00:00
Image Classification with the CIFAR10 Dataset 00:00:00
Recurrent Neural Networks and LSTM
Recurrent Neural Networks 00:00:00
A Fundamental Component – Gate Operation and Its Steps 00:00:00
TensorFlow LSTM Useful Classes and Methods 00:00:00
Univariate Time Series Prediction with Energy Consumption Data 00:00:00
Writing Music “a la” Bach 00:00:00
Deep Neural Networks
Deep Neural Network Definition and Architectures Through Time 00:00:00
Alexnet 00:00:00
Inception V3 00:00:00
Residual Networks (ResNet) 00:00:00
Painting with Style – VGG Style Transfer 00:00:00
Library Installation and Additional Tips
Windows Installation 00:00:00
MacOS Installation 00:00:00

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