The Complete Deep Learning Course 2024 With 7+ Real Projects

The Complete Deep Learning Course 2024 With 7+ Real Projects, available at $19.99, has an average rating of 4, with 169 lectures, based on 29 reviews, and has 290 subscribers.

You will learn about Artificial Neural Networks (ANN) Convolution Neural Network (CNN) Recurrent Neural Network (RNN) Generative adversarial network (GAN) Deep Convolutional Generative adversarial network (DCGAN) Natural Language Processing (NLP) Image Processing Sentiment Analysis Autoencoder Restricted Boltzman Machine Deep Reinforcement Learning – Monte Carlo Numpy Pandas Tensorflow This course is ideal for individuals who are Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence or Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning, Deep Learning, Artificial Intelligence. or Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science or Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence. or Any people who are not satisfied with their job and who want to become a Data Scientist. or Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer. or AI experts who want to expand on the field of applications or Data Scientists who want to take their AI Skills to the next level or Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence or Anyone passionate about Artificial Intelligence It is particularly useful for Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence or Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning, Deep Learning, Artificial Intelligence. or Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science or Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence. or Any people who are not satisfied with their job and who want to become a Data Scientist. or Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer. or AI experts who want to expand on the field of applications or Data Scientists who want to take their AI Skills to the next level or Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence or Anyone passionate about Artificial Intelligence.

Enroll now: The Complete Deep Learning Course 2024 With 7+ Real Projects

Summary

Title: The Complete Deep Learning Course 2024 With 7+ Real Projects

Price: $19.99

Average Rating: 4

Number of Lectures: 169

Number of Published Lectures: 143

Number of Curriculum Items: 170

Number of Published Curriculum Objects: 144

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Artificial Neural Networks (ANN)
Convolution Neural Network (CNN)
Recurrent Neural Network (RNN)
Generative adversarial network (GAN)
Deep Convolutional Generative adversarial network (DCGAN)
Natural Language Processing (NLP)
Image Processing
Sentiment Analysis
Autoencoder
Restricted Boltzman Machine
Deep Reinforcement Learning – Monte Carlo
Numpy
Pandas
Tensorflow

Who Should Attend

Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence
Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence
Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning, Deep Learning, Artificial Intelligence.
Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.
Any students in college who want to start a career in Data Science
Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.
Any people who are not satisfied with their job and who want to become a Data Scientist.
Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.
AI experts who want to expand on the field of applications
Data Scientists who want to take their AI Skills to the next level
Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence
Anyone passionate about Artificial Intelligence

Target Audiences

Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence
Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence
Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning, Deep Learning, Artificial Intelligence.
Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.
Any students in college who want to start a career in Data Science
Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.
Any people who are not satisfied with their job and who want to become a Data Scientist.
Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.
AI experts who want to expand on the field of applications
Data Scientists who want to take their AI Skills to the next level
Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence
Anyone passionate about Artificial Intelligence

Welcome to the Complete Deep Learning Course 2021 With 7+ Real Projects

This course will guide you through how to use Google’s TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google’s TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!

This course is designed to balance theory and practical implementation, with complete google colab and Jupiter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!

This course covers a variety of topics, including

Deep Learning.
Google Colab
Anaconda
Jupiter Notebook
Activation Function.
Keras.
Pandas.
Seaborn.
Feature scaling.
Matplotlib.
scikit-learn
Sigmoid Function.
Tanh Function.
ReLU Function.
Leaky Relu Function.
Exponential Linear Unit Function.
Swish function.
Corpora.
NLTK.
TensorFlow 2.0
Tokenization.
Spacy.
PoS tagging.
NER.
Stemming and lemmatization.
Semantics and topic modelling.
Sentiment analysis techniques.
Lexicon-based methods.
Rule-based methods.
Statistical methods.
Machine learning methods.
Bernoulli RBMs.
Introduction to RBMs (Restricted Boltzman Machine).
Introduction to BMs (Boltzman Machine).
Learning data representations with RBMs.
Multilayer neural networks.
Latent vector.
Loading data.
Analysing data.
Training model.
Compiling model.
Visualizing data and model.
Implementing multilayer neural networks
Improving the model performance by removing outliers.
Building a Keras deep neural network model
Neural Network Basics.
TensorFlow Basics.
Artificial Neural Networks (ANN).
Densely Connected Networks.
Convolutional Neural Networks (CNN).
Recurrent Neural Networks (RNN).
AutoEncoders.
Generative Adversarial Network (GAN).
Deep Convolutional Generative adversarial network (DCGAN).
Natural Language Processing (NLP).
Image Processing.
Sentiment Analysis.
Restricted Boltzman Machine.
Reinforcement Learning.

There are many Deep Learning Frameworks out there, so why use TensorFlow?

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are five big projects on healthcare problems and one small project to practice. These projects are listed below:

Concrete Quality Prediction Using Deep Neural Networks.
CIFAR-10.
Classifying clothing images.
20 newsgroups.
Handwritten Digit.
Denoising autoencoders (DAEs).
Movie Reviews Sentiment Analysis Using Recurrent Neural Networks.
Predicting Stock Price
Iris Flower.

Become a machine learning, and deep learning guru today! We’ll see you inside the course!

Course Curriculum

Chapter 1: Convolutional Neural Network (CNN) (Ongoing updates not complete yet)

Chapter 2: Introduction

Lecture 1: Course Structure

Lecture 2: How To Make The Most Out Of This Course

Lecture 3: What is Neuron

Lecture 4: What is Deep Learning

Lecture 5: What is ANN

Lecture 6: What is Tensorflow and how to install it

Lecture 7: Important note about tools in this course

Lecture 8: Multilayer Neural Network

Lecture 9: Introduction to Pandas and visualization

Lecture 10: Data Preprocessing by Pandas

Lecture 11: (OPTIONAL) Anaconda Installation

Lecture 12: Some Of The Important Terms In Neural Network

Chapter 3: Activation function

Lecture 1: What is activation function

Lecture 2: What is sigmoid function

Lecture 3: What is tanh function

Lecture 4: What is Rectified Linear Unit function

Lecture 5: What is Leaky ReLU function

Lecture 6: What is The Exponential Linear Unit Function

Lecture 7: What is The Swish function

Lecture 8: What is The Softmax function

Lecture 9: Time to code all the activation functions

Chapter 4: Concrete Quality Prediction Projects By ANN

Lecture 1: Introduction to the project

Lecture 2: Importing Data and Libraries

Lecture 3: Exploratory analysis

Lecture 4: Data Visualization

Lecture 5: Data scaling

Lecture 6: Building Neural Network model

Lecture 7: Evaluating the model

Lecture 8: Improving the model

Lecture 9: Project Summary

Chapter 5: CIFAR-10

Lecture 1: Convolution Neural Network

Lecture 2: Convolution Layers

Lecture 3: Pooling Layers

Lecture 4: Introduction to the project

Lecture 5: Importing library and data

Lecture 6: Compiling the model

Lecture 7: Training Neural Network

Lecture 8: Results

Lecture 9: Visualizing filters

Lecture 10: Summary of the project

Chapter 6: Classifying Fashion Image

Lecture 1: Introduction to the project

Lecture 2: Importing library and data

Lecture 3: Visualizing data

Lecture 4: Building Neural Network model

Lecture 5: Training and Testing Model

Lecture 6: Visualizing Convolutional Filters

Lecture 7: Improving the clothing image classifier with data augmentation

Lecture 8: Summary of the project

Chapter 7: Analysing movie review sentiment

Lecture 1: Basic Introduction to RNN

Lecture 2: Fully Recurrent Neural Networks and Recursive Neural Networks

Lecture 3: Hopfield Recurrent Neural Networks and Elman Neural Networks

Lecture 4: Long Short-term Memory Network

Lecture 5: Sentiment analysis basic concepts

Lecture 6: Sentiment analysis techniques

Lecture 7: The next challenges for sentiment analysis

Lecture 8: Lexicon and semantics analysis

Lecture 9: Introduction to the project

Lecture 10: Importing library and data

Lecture 11: Exploratory analysis

Lecture 12: Visualizing data

Lecture 13: Building RNN model

Lecture 14: Exploring Results

Lecture 15: Summary of the project

Chapter 8: 20 Newsgroups by NLP

Lecture 1: What is NLP

Lecture 2: NLP Applications

Lecture 3: NLP tools Part 1

Lecture 4: NLP tools Part 2

Lecture 5: NLP tools Part 3

Lecture 6: NLP tools Part 4

Lecture 7: Introduction to the project

Lecture 8: Importing library and data

Lecture 9: Exploring the newsgroups data

Lecture 10: Counting the occurrence of each word token

Lecture 11: Text Preprocessing

Lecture 12: Dropping Stop Words

Lecture 13: Reducing inflectional and derivational forms of words

Lecture 14: What Is Dimensionality Reduction?

Lecture 15: T-SNE for Dimensionality Reduction

Lecture 16: Summary of the project

Chapter 9: Handwritten Digits Images

Lecture 1: Autoencoder introduction

Lecture 2: Principle of Autoencoder

Lecture 3: Importing library and data

Lecture 4: IMPORTANT note

Lecture 5: Build autoencoder model

Lecture 6: Reconstructing the input

Lecture 7: Train the autoencoder model

Lecture 8: Summary of the autoencoder model

Lecture 9: Visualizing latent vector PART 1

Lecture 10: Visualizing latent vector PART 2

Lecture 11: Analysing Results

Instructors

Hoang Quy La
Electrical Engineer

Rating Distribution

1 stars: 2 votes
2 stars: 3 votes
3 stars: 3 votes
4 stars: 0 votes
5 stars: 21 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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