Learning Path: The Road To Tensorflow

Discover deep learning with Python and TensorFlow

It can be hard to get started with machine learning, particularly as new frameworks like TensorFlow start to gain traction across enterprise companies. If you have no prior exposure to one of the most important trends impacting how we do data science in the next few years, this path will help you get up to speed. It specifically focuses on getting you up and running with TensorFlow, after up-and-running coverage of Python and Deep Learning in Python with Theano.

Daniel Arbuckle

Daniel Arbuckle holds a Doctorate in Computer Science from the University of Southern California, where he specialized in robotics and was a member of the nanotechnology lab. He now has more than ten years behind him as a consultant, during which time he’s been using Python to help an assortment of businesses, from clothing manufacturers to crowdsourcing platforms. Python has been his primary development language since he was in High School. He’s also an award-winning teacher of programming and computer science.

Saimadhu Polamuri

Saimadhu Polamuri is a data science educator and the founder of Data Aspirant, a Data Science portal for beginners. He has 3 years of experience in data mining and 5 years of experience in Python. He is also interested in big data technologies such as Hadoop, Pig, and Spark. He has a good command of the R programming language and Matlab. He has a rudimentary understanding of Cpp Computer vision library (opencv) and big data technologies.

Eder Santana

Eder Santana is a PhD candidate on Electrical and Computer Engineering. His thesis topic is on Deep and Recurrent neural networks. After working for 3 years with Kernel Machines (SVMs, Information Theoretic Learning, and so on), Eder moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks. Now, Eder contributes to Keras: Deep Learning Library for Python. Besides deep learning, he also likes data visualization and teaching machine learning, either on online forums or as a teacher assistant.

Dan Van Boxel

Dan Van Boxel is a data scientist and machine learning engineer with over 10 years of experience. He is most well-known for Dan Does Data, a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas. Dan has also published research articles and presented findings at the Transportation Research Board and other academic journals.

A firm understanding of Python and the Python ecosystem

Build Python packages to efficiently create reusable code

Become proficient at creating tools and utility programs in Python

Use the Git version control system to protect your development environment from unwanted changes

Harness the power of Python to automate other software

Distribute computation tasks across multiple processors

Handle high I/O loads with asynchronous I/O for smoother performance

Take advantage of Python's metaprogramming and programmable syntax features

Get to grips with unit testing to write better code, faster

Understand the basic data mining concepts to implement efficient models using Python

Know how to use Python libraries and mathematical toolkits such as numpy, pandas, matplotlib, and sci-kit learn

Build your first application that makes predictions from data and see how to evaluate the regression model

Analyze and implement Logistic Regression and the KNN model

Dive into the most effective data cleaning process to get accurate results

Master the classification concepts and implement the various classification algorithms

Get a quick brief about backpropagation

Perceive and understand automatic differentiation with Theano

Exhibit the powerful mechanism of seamless CPU and GPU usage with Theano

Understand the usage and innards of Keras to beautify your neural network designs

Apply convolutional neural networks for image analysis

Discover the methods of image classification and harness object recognition using deep learning

Get to know recurrent neural networks for the textual sentimental analysis model

Set up your computing environment and install TensorFlow

Build simple TensorFlow graphs for everyday computations

Apply logistic regression for classification with TensorFlow

Design and train a multilayer neural network with TensorFlow

Understand intuitively convolutional neural networks for image recognition

Bootstrap a neural network from simple to more accurate models

See how to use TensorFlow with other types of networks

Program networks with SciKit-Flow, a high-level interface to TensorFlow