English | MP4 | AVC 1920×1020 | AAC 48KHz 2ch | 2h 03m | 585 MB
Develop industrial solutions based on deep learning models with Apache Spark
Deep learning has solved tons of interesting real-world problems in recent years. Apache Spark has emerged as the most important and promising machine learning tool and currently a stronger challenger of the Hadoop ecosystem. In this course, you’ll learn about the major branches of AI and get familiar with several core models of Deep Learning in its natural way.
You’ll begin with building deep learning networks to deal with speech data and explore tricks to solve NLP problems and classify video frames using RNN and LSTMs. You’ll also learn to implement the anomaly detection model that leverages reinforcement learning techniques to improve cyber security.
Moving on, you’ll explore some more advanced topics by performing prediction classification on image data using the GAN encoder and decoder. Then you’ll configure Spark to use multiple workers and CPUs to distribute your Neural Network training. Finally, you’ll track progress, solve the most common problems in your neural network, and debug your models that run within the distributed Spark engine.
This course takes a practical approach to networking and will get you familiar with several core models. It will help you implement deep learning models like CNN, RNN, LTSMs on Spark and get hands-on experience of what it takes and a general feeling of the complexity we are dealing with.
What You Will Learn
- Configure a Convolutional Neural Network (CNN) to extract value from images
- Create a deep network with multiple layers to perform computer vision
- Classify speech and audio data
- Leverage RNN and LSTMs for video classification for hospital data
- Improve cybersecurity with deep reinforcement learning
- Use a generative adversarial network for training
- Create highly distributed algorithms using Spark