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.
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.