Deep Learning with TensorFlow, Keras, and PyTorch

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Course Report - Deep Learning with TensorFlow, Keras, and PyTorch

Course Report

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Course Features

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Duration

7.19 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

7.19 hours

Course Description

A quick, practical introduction to deep-learning and TensorFlow and PyTorch Deep Learning With TensorFlow and Keras and PyTorch is an introduction into deep learning. It demonstrates the revolutionary machine-learning approach with interactive demos using TensorFlow and its API Keras as well as the new PyTorch library. To provide an intuitive understanding about deep learning's foundations, i.e. artificial neural networks, essential theory is whiteboarded. This foundational knowledge, along with tips and tricks to overcome common pitfalls, is provided in Python-based Jupyter notebooks. It empowers individuals without any previous knowledge of neural networks to create powerful state-of the-art deep learning models.

Course Overview

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

Prerequisites/Requirements

Machine learning or statistics

Object-oriented programming, specifically Python

Simple shell commands; eg, in Bash

What You Will Learn

Build deep learning models in all the major libraries: TensorFlow, Keras, and PyTorch

Create algorithms with state-of-the-art performance by fine-tuning model architectures

Excel across a broad range of computational problems including machine vision, natural language processing, and reinforcement learning

Self-direct and complete your own Deep Learning projects

Understand the language and theory of artificial neural networks

Target Students

Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful

Previous experience with statistics or machine learning is not necessary

Software engineers, data scientists, analysts, and statisticians with an interest in deep learning

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