My course does exactly what the title describes in a simple, relatable way. I help you to grasp the complete start to end concepts of fundamental deep learning. On your own it can be quite confusing, difficult and frustrating. I've been through the process myself, and with the help of lifelong ... I want to share this with my fellow beginners, developers, AI aspirers, with you. I will give you straightforward examples, instructions, advice, insights and resources for you to take simple steps to create your own neural networks from scratch.

Artificial Intelligence is yet to become a standard in schools, but it has the potential to transform the educational field. It's is a technology whose time has certainly come because it can already outperform humans in many ways. However, it can be very helpful for tech students. Meeting the needs of each student becomes a must in today's classroom. For example, a teacher should create personalized tasks to fit the learning style of students and ensure that they enjoy the same access to learning.

In this tutorial, you will learn how to train and test an end-to-end deep learning model for autonomous driving using data collected from the AirSim simulation environment. You will train a model to learn how to steer a car through a portion of the Mountain/Landscape map in AirSim using a single front facing webcam for visual input. Such a task is usually considered the "hello world" of autonomous driving, but after finishing this tutorial you will have enough background to start exploring new ideas on your own. Through the length of this tutorial, you will also learn some practical aspects and nuances of working with end-to-end deep learning methods. The code presented in this tutorial is written in Keras, a high-level deep learning Python API capable of running on top of CNTK, TensorFlow or Theano.

TensorFlow has quickly become a popular choice of tool for performing fast, efficient, and accurate deep learning. This Learning Path presents the implementation of practical, real-world projects, teaching you how to leverage TensorFlow's capabilities to perform efficient deep learning. So, if you are interested to acquire complete knowledge on deep learning with TensorFlow, then you should surely go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a look at your learning journey.

Keras is a deep learning library for fast, efficient training of deep learning models, and can also work with Tensorflow and Theano. Because it is lightweight and very easy to use, Keras has gained quite a lot of popularity in a very short time. This course will show you how to leverage the power of Keras to build and train high performance, high accuracy deep learning models, by implementing practical projects in real-world domains.Spanning over three hours, this course will help you master even the most advanced concepts in deep learning and how to implement them with Keras. You will train CNNs, RNNs, LSTMs, Autoencoders and Generative Adversarial Networks using real-world training datasets. These datasets will be from domains such as Image Processing and Computer Vision, Natural Language Processing, Reinforcement Learning and more.By the end of this highly practical course, you will be well-versed with deep learning and its implementation with Keras.

With this course, you'll have the keys to the entire R Machine Learning Kingdom!

MATLAB is the language of choice for many researchers and mathematics experts when it comes to machine learning. This video will help beginners build a foundation in machine learning using MATLAB. You'll start by getting your system ready with the MATLAB environment for machine learning and you'll see how to easily interact with the MATLAB workspace. You'll then move on to data cleansing, mining, and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll learn about the different types of regression technique and how to apply them to your data using the MATLAB functions.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business forecasting research. Learning deep learning regression is indispensable for data mining applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data mining, applied statistical learning or artificial intelligence. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for algorithm learning to achieve greater effectiveness. This practical course contains 35 lectures and 4 hours of content.

Keras is an open source neural network library written in Python. It is capable of running on top of MXNet, Deeplearning4j, Tensorflow, CNTK, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being minimal, modular, and extensible. This course provides a comprehensive introduction to deep learning. We start by presenting some famous success stories and a brief recap of the most common concepts found in machine learning.

Learn how to select features and build simpler, faster and more reliable machine learning models. This is the most comprehensive, yet easy to follow, course for feature selection available online. Throughout this course you will learn a variety of techniques used worldwide for variable selection, gathered from data competition websites and white papers, blogs and forums, and from the instructor's experience as a Data Scientist. You will have at your fingertips, altogether in one place, multiple methods that you can apply to select features from your data set. The course starts describing simple and fast methods to quickly screen the data set and remove redundant and irrelevant features.