Deep Learning with Tensor Flow Training at Massive Tech- It is one of the latest technologies in AI and Machine Learning, used in smartphone apps, Power grids, helping us to find the solutions to climate changes and more. This training will lead to a lucrative role in IT, healthcare, FinTech, e-commerce and other Industries.
During this training, you will become familiar with the language and fundamental concepts of artificial networks, PyTorch, autoencoders and more. Upon completion of the training, you will be able to build deep learning models, interpret results and build your own deep learning project.
There is a huge demand for skilled deep learning engineers is booming across a wide range of industries, making this Deep Learning course well-suited for Professionals at the intermediate to advanced level. We recommend this deep learning online course particularly for Software Engineers, Data Scientists, Data Analysts and Statisticians with an interest in deep learning.
Prerequisites
Candidates in this deep learning online training should have familiarity with
- Programming fundamentals
- Fair Understanding of basics of statistics and mathematics
- Good understanding of Machine Learning Concepts
Key Features
- LIVE Instructor-led Classes
- 24x7 on-demand technical support for assignments, queries, quizzes, project, etc.
- Flexibility to attend the class at your convenient time.
- Server Access to Massive's Tech Management System until you get into your dream carrier.
- A huge database of Interview Questions
- Professional Resume Preparation
- Earn a Skill Certificate
- Enroll today and get the advantage.
Curriculum
- Introduction to Tensorflow
- Learning Objectives
- Introduction to TensorFlow
- TensorFlow’s Hello World
- Liner Regression with Tensorflow
- Logistic Regression with Tensorflow
- Activation Functions
- Intro to Deep Learning
- Deep Neural Networks
- Objectives
- Intro to Convolutional Networks
- CNN for Classifications
- CNN Architecture
- Understanding Convolutions
- CNN with MNIST Dataset
- Objectives
- The Sequential Problem
- The RNN Model
- The LSTM Model
- Applying RNNs to Language Modeling
- LTSM Basics
- MNIST Data Classification with RNN/LSTM
- Applying RNN/LSTM to Language Modelling
- Applying RNN/LSTM to Character Modelling
- Objectives
- Intro to RBMs
- Training RBMs
- RBM MNIST
- Collaborative Filtering with RBM
- Objectives
- Intro to Autoencoders
- Applying RNNs to Language Modelling
- Autoencoders
- DBN MNIST
Have Any Questions?
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