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IT Certification

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Tensorflow 2 & Keras:Deep Learning & Artificial Intelligence

Deep Learning & Artificial Intelligence with Tensorflow 2 & Keras, Neural Networks, GANs,Autoencoders, Deep Learning A-Z

4.93 (7 reviews)

Students

8.5 hours

Content

May 2021

Last Update
Regular Price

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Entire course library + Leaning Path
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What you will learn

Complete Understanding of TensorFlow 2 (Google’s Deep Learning Framework) from the Scratch

Keras API to quickly build models that run on Tensorflow 2

Learn How Neural Network works

Understand Backpropagation, Forward Propogation, Gradient Descent

Artificial Neural Networks (ANNs)

Convolutional Neural Networks (CNNs)

Perform Image Classification with Convolutional Neural Networks

Image Recognition

Recurrent Neural Networks (RNNs)

Transfer Learning

Create Generative Adversarial Networks (GANs) with TensorFlow

Autoencoders

Generative Deep Learning - Neural Style Transfer

Data Analysis with Numpy, Pandas and Data Visualization with Matplotlib


Description

Welcome to Deep Learning and Artificial Intelligence with Tensorflow 2 and Keras API Course.

This course includes how to work with tensorflow 2 and creates Deep Learning applications with tensorflow 2 and Keras.

This course guide you how to work with google colab, all the hands on work done in google colab.

Many Projects included in this course like MNIST Digits Classification, MNIST Fashion data classification, Cat and Dog images Classification, Facial Expression Recognition, Leaf disease recognition, Generate Images with DCGANs(Deep Convolutional Generative Adversarial Networks) with Keras, Denoising autoencoders with Keras, TensorFlow, and Deep Learning etc.

Generative Deep Learning - Neural Style Transfer also included in this course.

For every lecture reference notes and code file is attached in this course.

Tensorflow is an open source machine library, and is one of the most widely used frameworks for deep learning.

Google released a new version of their TensorFlow deep learning library (TensorFlow 2) that integrated the Keras API directly and promoted this interface as the default or standard interface for deep learning development on the platform.

This course includes various topics -

  • Complete Understanding of TensorFlow 2.0 (Google’s Deep Learning Framework)

    from the Scratch

  • Keras API to quickly build models that run on Tensorflow 2

  • Learn How Neural Network works

  • Understand Backpropagation, Forward Propogation, Gradient Descent

  • Artificial Neural Networks (ANNs)

  • Convolutional Neural Networks (CNNs)

  • Perform Image Classification with Convolutional Neural Networks

  • Image Recognition

  • Recurrent Neural Networks (RNNs)

  • Transfer Learning

  • Create Generative Adversarial Networks (GANs) with TensorFlow

  • Autoencoders

  • Introduction to Natural Language Processing

  • Data Analysis with Numpy, Pandas and Data Visualization with Matplotlib


Screenshots

Tensorflow 2 & Keras:Deep Learning & Artificial Intelligence
Tensorflow 2 & Keras:Deep Learning & Artificial Intelligence
Tensorflow 2 & Keras:Deep Learning & Artificial Intelligence
Tensorflow 2 & Keras:Deep Learning & Artificial Intelligence

Content

Installation and Colab

Google Colab Introduction

Anaconda Installation

Jupyter Notebook

Crash course on Machine Learning

Concept of Machine Learning

What is Supervised Machine Learning and Linear Regression Algorithm

What is UnSupervised Machine Learning

Neural Network and Tensorflow

Describe Artificial Intelligence and Machine Learning and Deep Learning

Introduction to Neural Network

Types of Classification Problem

Activation Function part 1

Activation Function part 2

Forward Propogation

Back Propogation

Chain Rule

Gradient Descent

Tensorflow Introduction

Eager Execution

Keras

What is Keras

CIFAR 10

Fashion MNIST Part 1

Fashion MNIST Part 2

Fashion MNIST Part 3

CNN Neural Network

What is CNN

Working of CNN

MNIST digit classification

cat dog classification

cat dog classification 2

facial expression recognition 1

facial expression recognition 2

leaf diseases 1

leaf diseases 2

RNN

RNN INTRODUCTION

LSTM

Text classification

practical approach to word embedding

Bidirectional neural network

Autoencoders

Introduction to Autoencoders

implementation of autoencoder

Transfer Learning

CNN Transfer Learning

Data Augmentation

GAN

What is GANs

DCGAN INTRODUCTION

DCGAN Project

Generative Deep Learning

Neural Style Transfer

Neural Style Transfer Implementation part 1

Neural Style Transfer Implementation part 2

Crash Course on Data Science Libraries

Python for Data Analysis- Numpy

Pandas Series

Pandas DataFrames

Grouping and Filtering

Slicing and Sorting

Pandas Missing Values

Pandas Aggregation Functions

Matplotlib Introduction

Matplotlib Bar Graphs

Matplotlib Histogram

Matplotlib Scatter Plot

Matplotlib Area Plot

Matplotlib Pie Chart

Matplotlib Subplots


Reviews

K
Kapila7 May 2021

Nice content and explained in very well manner. Included lots of modules and also code files. Thank you goeduhb

S
Surabhi6 May 2021

Quality of content is good and way of explaining is clear. For those looking for a beginner to intermediate level of knowledge in Deep Learning, I would definitely recommend this course, as the concepts are explained very clearly and in simple language.


4003746

Udemy ID

4/24/2021

Course created date

5/7/2021

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