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.
Learn to know the basics of data management on tensors.
Learn how to handle the computing workflow of TensorFlow’s data flow graph.
Explore some basic methods supported by TensorFlow.
Learn to know how TensorBoard works.
Ability to read information from disk.
Learn to review two cases of unsupervised learning.
Learn to know the mechanics of k-means.
Ability to know the k-nearest neighbors.
Learn the few topics about the clustering on synthetic datasets.
Ability to load a dataset with which the k-means algorithm has problems separating classes.
Learn to interact with linear equation using univariate linear modelling function.
Learn to train the optimization stage which is a vital part of the machine learning workflow.
Ability to create a regression model that tries to fit a linear function that minimizes the error function.
Ability to work on a regression problem involving more than one variable.
Learn to review the original function on which it is based, and which gives it some of its more general properties.
Learn to know the logistic function which will serve us to represent the binary options in our new regression tasks.
Ability to work approximating the probability of the presence of heart disease, using an univariate logistic regression.
Explore the univariate examples domain which will use help from a new library, which eases the model building for us, called keras.
Learn to build a simple framework into the neural network components and architectures.
Learn to know how close we can be predicting values taken from a test population.
Learn to build a regression model for the fuel efficiency of several car models, based on several variables.
Ability to work with a more complex dataset.
Learn to know the historical development of the operation and then start looking at convolution in the continuous domain.
Ability to apply convolution in TensorFlow.
Ability to get the maximum and the average of the elements for an applied kernel.
Ability to know how the dropout operation reduces the value of some randomly selected weights to zero.
Learn to use simple utility functions to facilitate the building of convolutional layers.
Learn to work for the first time on one of the most well-known datasets for pattern recognition.
Learn to work on one of the most extensively used datasets in image comprehension.
Define a sequential model of neural networks, which have the property of reusing information already given.
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.
Learn to review the main classes and methods that we can use to build a LSTM layer.
Gain the ability to solve a problem of the domain of regression.
Learn to work with a recurrent neural network specialized in character sequences, or the char RNN model.
Exploring to the Neural Network architectures with like tens of layer, or combinations of complex constructs.
Learn to extend the complexity of the models.
Learn to illustrate how the improved inception module can be interpreted.
Achieve the ability to use the output of each constitutional layer, and also combine the output of the layer with the original input.
Learn to work with the implementation of the paper A Neural Algorithm of Artistic Style from Leon Gatys.
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