Deep Learning for Beginners in Python- Work On 12+ Projects

Deep Learning for Beginners in Python: Work On 12+ Projects, available at $64.99, has an average rating of 4.77, with 106 lectures, based on 179 reviews, and has 1722 subscribers.

You will learn about Complete Understanding of Deep Learning from the Scratch Building the Artificial Neural Networks (ANNs) from the Scratch Artificial Neural Networks (ANNs) for Binary Data Classification Building Convolutional Neural Networks from the Scratch Convolutional Neural Network for Image Classification Convolutional Neural Network for Digit Recognition Breast Cancer Detection with Convolutional Neural Networks Convolutional Neural Networks for Predictive Analysis Convolutional Neural Networks for Fraud Detection Building the Recurrent Neural Networks (ANNs) from Scratch LSTM and GRU Review Classification with LSTM and GRU LSTM and GRU for Image Classification Prediction of Google Stock Price with RNN and LSTM Transfer Learning Natural Language Processing Crash Course on Numpy (Data Analysis) Crash Course on Pandas (Data Analysis) Crash course on Matplotlib (Data Visualization) This course is ideal for individuals who are Anyone who wants to learn Deep Learning and AI or Students and Professionals who want to start a career in Data Science, Deep Learning and AI It is particularly useful for Anyone who wants to learn Deep Learning and AI or Students and Professionals who want to start a career in Data Science, Deep Learning and AI.

Enroll now: Deep Learning for Beginners in Python: Work On 12+ Projects

Summary

Title: Deep Learning for Beginners in Python: Work On 12+ Projects

Price: $64.99

Average Rating: 4.77

Number of Lectures: 106

Number of Published Lectures: 106

Number of Curriculum Items: 106

Number of Published Curriculum Objects: 106

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Complete Understanding of Deep Learning from the Scratch

Building the Artificial Neural Networks (ANNs) from the Scratch

Artificial Neural Networks (ANNs) for Binary Data Classification

Building Convolutional Neural Networks from the Scratch

Convolutional Neural Network for Image Classification

Convolutional Neural Network for Digit Recognition

Breast Cancer Detection with Convolutional Neural Networks

Convolutional Neural Networks for Predictive Analysis

Convolutional Neural Networks for Fraud Detection

Building the Recurrent Neural Networks (ANNs) from Scratch

LSTM and GRU

Review Classification with LSTM and GRU

LSTM and GRU for Image Classification

Prediction of Google Stock Price with RNN and LSTM

Transfer Learning

Natural Language Processing

Crash Course on Numpy (Data Analysis)

Crash Course on Pandas (Data Analysis)

Crash course on Matplotlib (Data Visualization)

Who Should Attend

Anyone who wants to learn Deep Learning and AI

Students and Professionals who want to start a career in Data Science, Deep Learning and AI

Target Audiences

Anyone who wants to learn Deep Learning and AI

Students and Professionals who want to start a career in Data Science, Deep Learning and AI

Artificial Intelligence and Deep Learning are growing exponentially in today’s world. There is multiple application of AI and Deep Learning like Self Driving Cars, Chatbots, Image Recognition, Virtual Assistance, ALEXA, and so on

With this course, you will understand the complexities of Deep Learning in an easy way, as well as you will have A Complete Understanding of the Googles TensorFlow 2.0 Framework

TensorFlow 2.0 Framework has amazing features that simplify the Model Development, Maintenance, Processes, and Performance

In TensorFlow 2.0 you can start the coding with Zero Installation,whether you’re an expert or a beginner, in this course you will learn an end-to-end implementation of Deep Learning Algorithms

List of the Projects that you will work on,

Part 1: Artificial Neural Networks (ANNs)

Project 1: Multiclass image classification with ANN

Project 2: Binary Data Classification with ANN

Part 2: Convolutional Neural Networks (CNNs)

Project 3: Object Recognition in Images with CNN

Project 4: Binary Image Classification with CNN

Project 5: Digit Recognition with CNN

Project 6: Breast Cancer Detection with CNN

Project 7: Predicting the Bank Customer Satisfaction

Project 8: Credit Card Fraud Detection with CNN

Part 3: Recurrent Neural Networks (RNNs)

Project 9: IMDB Review Classification with RNN – LSTM

Project 10: Multiclass Image Classification with RNN – LSTM

Project 11: Google Stock Price Prediction with RNN and LSTM

Part 4: Transfer Learning

Part 5: Natural Language Processing

Basics of Natural Language Processing

Project 12: Movie Review Classification with NLTK

Part 6: Data Analysis and Data Visualization

Crash Course on Numpy (Data Analysis)

Crash Course on Pandas (Data Analysis)

Crash course on Matplotlib (Data Visualization)

With this course, you will learn,

1) To build the Neural Networks from the scratch

2) You will have a complete understanding of  Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks

3) You will learn to build the neural networks with LSTM and GRU

4) Hands-On Transfer Learning

5) Learn Natural Language Processing by doing a text classification project

6) Improve your skills in Data Analysis with Numpy, Pandas, and Data Visualization with Matplotlib

So what are you waiting for, Enroll Now and understand Deep Learning to advance your career and increase your knowledge!

Regards,

Vijay Gadhave

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction

Lecture 2: Updates on Udemy Reviews

Lecture 3: Course FAQs

Chapter 2: Installation and Setup

Lecture 1: Introduction to Google Colab

Lecture 2: Google Colab Update (Dont Skip)

Lecture 3: Colab notebooks and datasets

Chapter 3: -Part 1: Artificial Neural Networks (ANNs)–

Lecture 1: Welcome to Part 1 – Artificial Neural Networks (ANNs)

Chapter 4: Introduction to Artificial Neural Networks (ANNs)

Lecture 1: The Neuron

Lecture 2: Activation Function

Lecture 3: Cost Function

Lecture 4: Gradient Descent and Back-Propagation

Chapter 5: Project 1: Multiclass image classification with ANN

Lecture 1: Step 1 – Installation and Setup

Lecture 2: Step 2 – Data Preprocessing

Lecture 3: Imp Lecture (dont skip)

Lecture 4: Step 3 – Building the Model

Lecture 5: Step 4 – Training the Model

Lecture 6: Step 5 – Model evaluation and performance

Chapter 6: Project 2: Binary Data Classification with ANN

Lecture 1: Binary Data Classification Step 1

Lecture 2: Binary Data Classification Step 2

Lecture 3: Binary Data Classification Step 3

Lecture 4: Binary Data Classification Step 4

Lecture 5: Binary Data Classification Step 5

Chapter 7: -Part 2: Convolutional Neural Networks (CNNs)–

Lecture 1: Welcome to Part 2 – Convolutional Neural Networks (CNNs)

Chapter 8: Introduction to Convolutional Neural Networks (CNNs)

Lecture 1: Convolutional Neural Network Part 1

Lecture 2: Convolutional Neural Network Part 2

Chapter 9: Project 3: Object Recognition in Images with CNN

Lecture 1: Object Recognition in Images Step 1

Lecture 2: Object Recognition in Images Step 2

Lecture 3: Object Recognition in Images Step 3

Lecture 4: Object Recognition in Images Step 4

Lecture 5: Object Recognition in Images Step 5

Chapter 10: Project 4: Binary Image Classification with CNN

Lecture 1: Binary Image Classification Step 1

Lecture 2: Binary Image Classification Step 2

Lecture 3: Binary Image Classification Step 3

Lecture 4: Binary Image Classification Step 4

Lecture 5: Binary Image Classification Step 5

Chapter 11: Project 5: Digit Recognition with CNN

Lecture 1: Digit Recognition with CNN – Step 1

Lecture 2: Digit Recognition with CNN – Step 2

Lecture 3: Digit Recognition with CNN – Step 3

Chapter 12: Project 6: Breast Cancer Detection with CNN

Lecture 1: Breast Cancer Detection with CNN – Step 1

Lecture 2: Breast Cancer Detection with CNN – Step 2

Lecture 3: Breast Cancer Detection with CNN – Step 3

Chapter 13: Project 7: Predicting the Bank Customer Satisfaction with CNN

Lecture 1: Predicting the Bank Customer Satisfaction – Step 1

Lecture 2: Predicting the Bank Customer Satisfaction – Step 2

Lecture 3: Predicting the Bank Customer Satisfaction – Step 3

Lecture 4: Predicting the Bank Customer Satisfaction – Step 4

Chapter 14: Project 8: Credit Card Fraud Detection with CNN

Lecture 1: Credit Card Fraud Detection with CNN – Step 1

Lecture 2: Credit Card Fraud Detection with CNN – Step 2

Lecture 3: Credit Card Fraud Detection with CNN – Step 3

Lecture 4: Credit Card Fraud Detection with CNN – Step 4

Chapter 15: –Part 3: Recurrent Neural Networks (RNNs)–

Lecture 1: Welcome to Part 3 – Recurrent Neural Networks (RNNs)

Chapter 16: Introduction to Recurrent Neural Networks

Lecture 1: Introduction to Recurrent Neural Networks

Lecture 2: Vanishing Gradient Problem

Lecture 3: LSTM and GRU

Chapter 17: Project 9: IMDB Review Classification with RNN – LSTM

Lecture 1: IMDB Review Classification with RNN – LSTM: Step 1

Lecture 2: IMDB Review Classification with RNN – LSTM: Step 2

Lecture 3: IMDB Review Classification with RNN – LSTM: Step 3

Chapter 18: Project 10: Multiclass Image Classification with RNN – LSTM

Lecture 1: Multiclass Image Classification with RNN – LSTM: Step 1

Lecture 2: Multiclass Image Classification with RNN – LSTM: Step 2

Lecture 3: Multiclass Image Classification with RNN – LSTM: Step 3

Chapter 19: Project 11: Google Stock Price Prediction with RNN and LSTM

Lecture 1: Google Stock Price Prediction with RNN and LSTM: Step 1

Lecture 2: Google Stock Price Prediction with RNN and LSTM: Step 2

Lecture 3: Google Stock Price Prediction with RNN and LSTM: Step 3

Lecture 4: Google Stock Price Prediction with RNN and LSTM: Step 4

Lecture 5: Google Stock Price Prediction with RNN and LSTM: Step 5

Chapter 20: –Part 4: Transfer Learning–

Lecture 1: Welcome to Part 4 – Transfer Learning

Chapter 21: Transfer Learning

Lecture 1: Transfer Learning Step 1

Lecture 2: Transfer Learning Step 2

Lecture 3: Transfer Learning Step 3

Lecture 4: Transfer Learning Step 4

Chapter 22: –Part 5: Natural Language Processing–

Lecture 1: Welcome to Part 5: Natural Language Processing

Chapter 23: Basics of Natural Language Processing

Lecture 1: Introduction to Natural Language Processing

Lecture 2: NLTK Introduction and Installation

Lecture 3: Tokenization

Lecture 4: Stemming

Lecture 5: Lemmatization

Lecture 6: Stop Words

Lecture 7: POS Tagging

Instructors

Vijay Gadhave
Data Scientist and Software Developer

Rating Distribution

1 stars: 7 votes

2 stars: 4 votes

3 stars: 26 votes

4 stars: 49 votes

5 stars: 93 votes

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