AI and Deep Learning with TensorFlow

March 13, 2020

AI and Deep Learning with TensorFlow: Master Deep Learning – the mean to culminate Machine Learning into Artificial Intelligence. Deep Learning is the technique to implement Machine Learning and eventually achieve practical implementations of artificial intelligence. Learn Machine Learning paradigms, Deep Learning, Multi-task Learning, Convolution Networks, Neural Networks, Multi Digit Number Recognition, autoencoders, Boltzmann Machines and practical implementation of Deep Learning.
The Machine Learning course and Deep Learning Specialization teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

AI and Deep Learning with TensorFlow: If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In these courses you will:

  • Learn how to build machine learning models in TensorFlow
  • Build image recognition algorithms with deep neural networks and convolutional neural networks
  • Understand how to deploy your models on mobile and the web
  • Go beyond image recognition into object detection, text recognition, and more
  • Expand the basic APIs for custom learning/training

Course Objectives

By the end of the course, learner will be able to:

  • Understand various Machine Learning Paradigms
  • Understand Supervised & Unsupervised Learning Algorithms
  • Master basic concepts of Deep Learning
  • Learn practical aspects of Recurrent and Recursive Neural Networks
  • Understand large scale Deep Learning
  • Implement the learnt concept in a case study based Project


Course Information

Estimated Time: 25 Hours

Difficulty: Intermediate

Tracks:

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Module 1: Understanding Machine Learning basics

Module 2: Introduction to Neural Nets and Deep Learning

Module 3: Introduction to TensorFlow

Module 4: Convolutional Networks

Module 5: Recurrent and Recursive Nets

Module 6: Unsupervised Learning: Autoencoders, RBM

Module 7: Practical Methodology

Module 8: Hands on Project

Course Information

Estimated Time: 25 Hours

Difficulty: Intermediate

Tracks:

Course Instructor

Free Enroll

Enroll Now

Module 1: Understanding Machine Learning basics

Module 2: Introduction to Neural Nets and Deep Learning

Module 3: Introduction to TensorFlow

Module 4: Convolutional Networks

Module 5: Recurrent and Recursive Nets

Module 6: Unsupervised Learning: Autoencoders, RBM

Module 7: Practical Methodology

Module 8: Hands on Project

+110 enrolled

Course Information

Estimated Time: 25 Hours

Difficulty: Intermediate

Tracks:

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