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Deep Learning Course with TensorFlow
Deep Learning Developed by Google Brain Team, TensorFlow is an open-source software library for programming the flow of data among operations. It was devised with an intention to empower automated systems backed by machine learning applications such as artificial neural networks. In Deep Learning course you will learn Deep Learning Algorithms, Data Abstraction & Neural Networks.
What would you learn in certification course for Deep Learning with TensorFlow in Mumbai?
Through our Deep Learning with TensorFlow course, you will get a hold of:
– Fundamentals of TensorFlow, such as main functions, operational aspects, and execution pipelines.
– Using TensorFlow in curve fitting, regression, classification, and minimization of errors.
– Concepts of Deep Architectures
– Programming networks with SciKit-Flow
– Building simple TensorFlow graphs for computational purposes.
– Designing and training a multilayer neural network
– Integrating TensorFlow with other types of networks
Learning Objectives
For Individuals
- They would gain access to self-paced, high-quality learning content and experiences that have been developed by experts in the deep learning field.
For Businesses
- They would be taught with well-blended models
- Deep Learning course would offer them high-quality teaching and assistance
- 24/7 teaching assistance and support
360EduKraft's Training Key Features:
Deep Learning (with TensorFlow) Course Description
A certification course in Deep Learning with TensorFlow would prove to be a milestone in your career as a data scientist. If you already into data science, working with machine learning is a routine for you, and you want to take a leap in your career, Deep Learning with TensorFlow would help you learn working with convoluted neural networks, which is going to be the ‘next big step’ for you.
– Data scientists looking for advanced understanding of machine learning.
– Anyone, with a basic knowledge of C++ or Python, interested in Machine Learning.
– Any programmer who wants to shift from conventional programming to building machine learning algorithms.
Some of the benefits of opting for Deep Learning with TensorFlow course are:
– You would have better understanding of recurrent neural networks.
– You would gain insights into the very intuition of machine learning.
– You would be ready for the IoT revolution that relies highly on intelligent flow of data and neural networks.
– Basic Knowledge of C++ or Python
– High school level mathematics
– A PC with Windows 7 and above/Mac OS X 10 and above, Intel i5 processor, minimum 4GB of RAM, and 3-5 GB of disk space.
Deep Learning Course Content
- Introduction to Deep Learning
- Understanding Neural Networks with TensorFlow
- Deep dive into Neural Networks with TensorFlow
- Master Deep Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Restricted Boltzmann Machine (RBM) and Autoencoders
- Keras API
- TFLearn API
- In-Class Project
Deep Learning with TensorFlow Curriculum
1. Introduction
1.1 Introduction to Deep Learning
What is a neural network?
Supervised Learning with Neural Networks – Python
How Deep Learning is different from Machine Learning
2. Overview of Machine Learning Concept
2.1 Overview of Machine Learnin
Quick tour of the types of machine learning
What is Machine Learning?
Supervised Machine Learning algorithms
K-Nearest Neighbors (KNN) concept and application
Naive Bayes concept and application
Logistic Regression concept and application
Classification Trees concept and application
Unsupervised Machine Learning algorithms
Clustering with K-means concept and application
Hierarchial Clustering concept and application
3. Tensor Flow Essentials
3.1 A quick refresh on Tensor Flow Essentials:
Representing tensors
Creating operators and excuting with sessions
Introduction Jupyter notebook for TensorFlow coding
TensorFlow variables
Visualizing data using TensorBoard
4. ML Algorithm - Linear Regression in TensorFlow:
4.1 A quick crash course on ML Algorithm
Regression problems
Linear regression applications
Regularization
Available datasets
Coding Linear Regression with TensorFlow – Case study
5. Deep Neural Networks in TensorFlow
5.1 Introduction to Deep Neural Networks in TensorFlow
Basic Neural Nets
Single Hidden Layer Model
Multiple Hidden Layer Model
6. Convolutional Neural Networks
6.1 Overview to Convolutional Neural Networks
Introduction to Convolutional Neural Networks
Input Pipeline
Introduction to RNN, LSTM, GRU
7. Reinforcement Learning in Tensorflow
7.1 Overview to Reinforcement Learning in Tensorflow
Concept of Reinforcement Learning
Simple model applying Reinforcement Learning in TensorFlow
8. Hands on Deep Learning Application with TensorFlow
8.1 Overview to Hands on Deep Learning Application
Example Application – Case study
Hands on building the Deep Learning application with TensorFlow
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