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.
* Hard copy material is not applicable for this course.
Why should one take Deep Learning with Keras Certification?
This Course is intended for Individuals wanting to understand a deeper level of deep learning using Keras. The course provides you a comprehensive introduction to deep learning, you will also be trained on neural networks and optimization techniques.
Earning Vskills Deep Learning with Keras Certification can help candidate differentiate in today's competitive job market, broaden their employment opportunities by displaying their advanced skills, and result in higher earning potential.
Who will benefit from taking Deep Learning with Keras Certification?
IT specialists aspiring to learn a new skill set; statisticians; computer scientists; and IT analysts etc.
Companies that hire Vskills Certified Deep Learning with Keras Professional
Data Science with Python is one of the faster growing filed and are in great demand. Companies like KPMG, Accenture, TCS & Cognizant specializing in Data Science related activities are constantly looking for certified professionals.
Deep Learning with Keras Table of Contents
Deep Learning with Keras Interview Questions
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Table of Content
NEURAL NETWORKS FOUNDATIONS
- The Course Overview
- Building a Network to Recognize Handwritten Numbers
- Playing Around with the Parameters to Improve Performance
KERAS INSTALLATION AND API
- Installing and Configuring Keras
- Keras API
- Callbacks for Customizing the Training Process
DEEP LEARNING WITH CONVOLUTIONAL NETWORKS
- Deep Convolutional Neural Network - DCNN
- Recognizing CIFAR-10 Images with Deep Learning
INTRODUCTION TO DEEP LEARNING
- The Course Overview
- What is Deep Learning?
- Machine Learning Concepts
- Foundations of Neural Networks
GET STARTED WITH KERAS
- Configuration of Keras
- Presentation of Keras and Its API
- Design and Train Deep Neural Networks
- Regularization in Deep Learning
CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS
- Introduction to Computer Vision
- Convolutional Networks
- CNN Architectures
- Image Classification Example
- Image Segmentation Example
- Introduction to Recurrent Networks
- Recurrent Neural Networks
- “One to Many” Architecture
- “Many to One” Architecture
- “Many to Many” Architecture
- Embedding Layers
- What are Recommender Systems?
- Content/Item Based Filtering
- Collaborative Filtering
- Hybrid System
NEURAL STYLE TRANSFER
- Introduction to Neural Style Transfer
- Single Style Transfer
- Advanced Techniques
- Style Transfer Explained
- Data Augmentation
- Transfer Learning
- Hyper-Parameter Search
- Natural Language Processing
GENERATIVE ADVERSARIAL NETWORKS
- An Introduction to Generative Adversarial Networks (GAN)
- Run Our First GAN
- Deep Convolutional Generative Adversarial Networks (DCGAN)
- Techniques to Improve GANs