Tag Archive: Practical

KEY FEATURES

When it comes to web programming, there are a lot of tools you can learn and use to make your workflow more efficient and your products more exciting. Getting started can be daunting when you know just how much there is to learn. However, the barriers to learning are lower than ever, and this immersive course will give you a crash course into a variety of languages and tools, plus how to integrate them, giving you an excellent foundation for further learning.

• Access 67 lectures & 10.5 hours of content 24/7
• Add dynamic features to a website using JavaScript & jQuery
• Transfer information between web pages using JSON
• Layout websites more efficiently w/ CSS & HTML
• Power the back-end of a website w/ C#
• Work w/ data more efficiently using SQL Lite

PRODUCT SPECS

Details & Requirements

• Length of time users can access this course: lifetime
• Access options: web streaming, mobile streaming
• Certification of completion not included
• Redemption deadline: redeem your code within 30 days of purchase
• Experience level required: all levels

Compatibility

• Internet required

THE EXPERT

Erik Owsiak started working with programming and technology back in the 90’s when a 200 MHz Pentium 1 was a speed demon. As programming and hardware has matured over time so did Erik working with it. This gives him a unique perspective on what it is that really matters when it comes to programming. Over the years he has seen a few technologies come and go but a few basics have stayed which he believes will still be with us a 100 years from now.

Over the course of his career, Erik has worked on many different projects, with many programming languages from C/C++, C#, php, JavaScript/JScript, asp.net, python, Lua to name just a few.

KEY FEATURES

The applications of Deep Learning are many, and constantly growing, just like the neural networks that it supports. In this course, you’ll delve into advanced concepts of Deep Learning, starting with the basics of TensorFlow and Theano, understanding how to build neural networks with these popular tools. Using these tools, you’ll learn how to build and understand a neural network, knowing exactly how to visualize what is happening within a model as it learns.

• Access 23 lectures & 3 hours of programming 24/7
• Discover batch & stochastic gradient descent, two techniques that allow you to train on a small sample of data at each iteration, greatly speeding up training time
• Discuss how momentum can carry you through local minima
• Learn adaptive learning rate techniques like AdaGrad & RMSprop
• Explore dropout regularization & other modern neural network techniques
• Understand the variables & expressions of TensorFlow & Theano
• Set up a GPU-instance on AWS & compare the speed of CPU vs GPU for training a deep neural network
• Look at the MNIST dataset & compare against known benchmarks

PRODUCT SPECS

Details & Requirements

• Length of time users can access this course: lifetime
• Access options: web streaming, mobile streaming
• Certification of completion not included
• Redemption deadline: redeem your code within 30 days of purchase
• Experience level required: all levels, but you must have some knowledge of calculus, linear algebra, probability, Python, and Numpy
• All code for this course is available for download here, in the directory ann_class2

Compatibility

• Internet required

THE EXPERT

The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master’s thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.

He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

Multiple businesses have benefitted from his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.