Complete Guide to TensorFlow for Deep Learning with Python

BY
Udemy

Learn how to use TensorFlow with Python and develop the ability to use creative problem-solving techniques.

Mode

Online

Fees

₹ 599 3299

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course overview

Google created the open-source TensorFlow library, which is primarily used for applications involving deep learning. Additionally, it claims to support conventional machine learning. Complete Guide to TensorFlow for Deep Learning with Python online certification is developed by Jose Portilla - Head of Data Science at Pierian Training which is delivered by Udemy for candidates who want to master the concepts and strategies associated with TensorFlow for deep learning activities.

Complete Guide to TensorFlow for Deep Learning with Python online course provides 14 hours of thorough lectures along with 5 downloadable learning resources and 7 articles which aim to teach the candidates about the techniques necessary to create artificial neural networks for deep learning operations. The Complete Guide to TensorFlow for Deep Learning with Python online training explains the theories behind image classification, CNN, GNN, and RNN, as well as acts as a comprehensive manual for using the TensorFlow framework following its intended purposes while demonstrating to candidates the most recent deep learning methods.

The highlights

  • Certificate of completion
  • Self-paced course
  • 14 hours of pre-recorded video content
  • 7 articles 
  • 5 downloadable resources

Program offerings

  • Online course
  • Learning resources
  • 30-day money-back guarantee
  • Unlimited access
  • Accessible on mobile devices and tv

Course and certificate fees

Fees information
₹ 599  ₹3,299
certificate availability

Yes

certificate providing authority

Udemy

What you will learn

Machine learning Knowledge of python Knowledge of deep learning

After completing the Complete Guide to TensorFlow for Deep Learning with Python certification course, candidates will be introduced to the foundational concepts involved with TensorFlow for deep learning operation using the functionalities of Python programming. In this deep learning course, candidates will explore the fundamentals of machine learning, and reinforcement learning using OpenAI Gym as well as will acquire knowledge of the strategies involved with solving unsupervised learning problems using AutoEncoders. In this TensorFlow certification, candidates will learn about skills for utilizing TensorFlow for image classification with time series analysis and convolutional neural networks using recurrent neural networks s well as will about the generative adversarial networks.

The syllabus

Introduction

  • Introduction
  • Course Overview -- PLEASE DON'T SKIP THIS LECTURE! Thanks :)
  • FAQ - Frequently Asked Questions

Installation and Setup

  • Quick Note for MacOS and Linux Users
  • Installing TensorFlow and Environment Setup

What is Machine Learning?

  • Machine Learning Overview

Crash Course Overview

  • Crash Course Section Introduction
  • NumPy Crash Course
  • Pandas Crash Course
  • Data Visualization Crash Course
  • SciKit Learn Preprocessing Overview
  • Crash Course Review Exercise
  • Crash Course Review Exercise - Solutions

Introduction to Neural Networks

  • Introduction to Neural Networks
  • Introduction to Perceptron
  • Neural Network Activation Functions
  • Cost Functions
  • Gradient Descent Backpropagation
  • TensorFlow Playground
  • Manual Creation of Neural Network - Part One
  • Manual Creation of Neural Network - Part Two - Operations
  • Manual Creation of Neural Network - Part Three - Placeholders and Variables
  • Manual Creation of Neural Network - Part Four - Session
  • Manual Neural Network Classification Task

TensorFlow Basics

  • Introduction to TensorFlow
  • TensorFlow Basic Syntax
  • TensorFlow Graphs
  • Variables and Placeholders
  • TensorFlow - A Neural Network - Part One
  • TensorFlow - A Neural Network - Part Two
  • TensorFlow Regression Example - Part One
  • TensorFlow Regression Example _ Part Two
  • TensorFlow Classification Example - Part One
  • TensorFlow Classification Example - Part Two
  • TF Regression Exercise
  • TF Regression Exercise Solution Walkthrough
  • TF Classification Exercise
  • TF Classification Exercise Solution Walkthrough
  • Saving and Restoring Models

Convolutional Neural Networks

  • Introduction to Convolutional Neural Network Section
  • Review of Neural Networks
  • New Theory Topics
  • Quick note on MNIST lecture
  • MNIST Data Overview
  • MNIST Basic Approach Part One
  • MNIST Basic Approach Part Two
  • CNN Theory Part One
  • CNN Theory Part Two
  • CNN MNIST Code Along - Part One
  • CNN MNIST Code Along - Part Two
  • Introduction to CNN Project
  • CNN Project Exercise Solution - Part One
  • CNN Project Exercise Solution - Part Two

Recurrent Neural Networks

  • Introduction to RNN Section
  • RNN Theory
  • Manual Creation of RNN
  • Vanishing Gradients
  • LSTM and GRU Theory
  • Introduction to RNN with TensorFlow API
  • RNN with TensorFlow - Part One
  • RNN with TensorFlow - Part Two
  • Quick Note on RNN Plotting Part 3
  • RNN with TensorFlow - Part Three
  • Time Series Exercise Overview
  • Time Series Exercise Solution
  • Quick Note on Word2Vec
  • Word2Vec Theory
  • Word2Vec Code Along - Part One
  • Word2Vec Part Two

Miscellaneous Topics

  • Intro to Miscellaneous Topics
  • Deep Nets with Tensorflow Abstractions API - Part One
  • Deep Nets with Tensorflow Abstractions API - Estimator API
  • Deep Nets with Tensorflow Abstractions API - Keras
  • Deep Nets with Tensorflow Abstractions API - Layers
  • Tensorboard

AutoEncoders

  • Autoencoder Basics
  • Dimensionality Reduction with Linear Autoencoder
  • Linear Autoencoder PCA Exercise Overview
  • Linear Autoencoder PCA Exercise Solutions
  • Stacked Autoencoder

Reinforcement Learning with OpenAI Gym

  • Introduction to Reinforcement Learning with OpenAI Gym
  • Extra Resources for Reinforcement Learning
  • Introduction to OpenAI Gym
  • OpenAI Gym Steup
  • Open AI Gym Env Basics
  • Open AI Gym Observations
  • OpenAI Gym Actions
  • Simple Neural Network Game
  • Policy Gradient Theory
  • Policy Gradient Code Along Part One
  • Policy Gradient Code Along Part Two

GAN - Generative Adversarial Networks

  • Introduction to GANs
  • GAN Code Along - Part One
  • GAN Code Along - Part Two
  • GAN Code Along - Part Three

Bonus

  • Bonus Lecture

Instructors

Mr Jose Portilla
Head of Data Science
Udemy

Other Bachelors, M.S

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