English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 0h 44m | 168 MB
Enhance your knowledge of Neural Networks
Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the Globe is, "How do I get started in Machine Learning?" One reason could be the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This systematic guide will teach you various Machine Learning techniques.
You will start with the very basics of neural networks and types. Then we learn about powerful variations in neural networks and Recurrent Neural Networks. Finally, we conclude with a synthetic introduction to more advanced Machine Learning techniques, such as GANs and reinforcement learning.
This course gives you a glimpse into Machine Learning Models and the application of models at scale using clustering, classification, regression, and reinforcement learning, all with fun examples. Hands-on examples are presented to help you understand the power of problem-solving with Machine Learning and advanced architectures, software installation, and configuration.
What You Will Learn
- Learn the math and mechanics of Machine Learning via a developer-friendly approach
- Prepare yourself and other developers for working in the new, ubiquitous field of Machine Learning
- Get an overview of the most well known and powerful tools to solve computing problems using Machine Learning.
- Get an intuitive and down-to-earth introduction
- Apply the concepts to interesting and cutting-edge problems.
01 The Course Overview
02 History of Neural Models
03 Implementing a Simple Function
04 Loss Function
05 Origin of Convolutional Neural Networks
06 Implementing Discrete Convolution
07 Deep Neural Networks
10 Univariate Time Series Prediction
12 Reinforcement Learning