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ChatGPT for Deep Learning with Python Keras and Tensorflow

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ChatGPT for Deep Learning with Python Keras and Tensorflow, available at $54.99, has an average rating of 4.44, with 193 lectures, 8 quizzes, based on 9 reviews, and has 270 subscribers.

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You will learn about Utilize ChatGPT for real-life Data Science and Deep Learning projects Let ChatGPT do the coding work for you (Python, Pandas, Keras etc.) Use ChatGPT to select the most suitable Neural Network for your task Utilize ChatGPT to analyse and interpret the outcomes of your Deep Learning models Ask ChatGPT to critically assess and improve your Neural Networks Perform an Explanatory Data Analysis with ChatGPT and Python Use ChatGPT for Data Manipulation, Aggregation, advanced Pandas coding & more Utilize ChatGPT to fit, evaluate and optimize FNN, CNN, RNN and LSTM models Utilize ChatGPT for Regression and Classification tasks using Keras & Tensorflow Utilize ChatGPT for Image Recognition Utilize ChatGPT for Time Series Prediction Use ChatGPT for Error Handling and Troubleshooting This course is ideal for individuals who are Beginners seeking to master real-life Data Science Projects in no time without the need to learn everything from scratch. or Data Scientists interested in boosting their work with Artificial Intelligence and Neural Networks or Everybody in a Data-related Profession wanting to leverage the power of ChatGPT for their day-to-day work. or Data Analysts seeking to outsource the most time-consuming parts of their work to ChatGPT. or Machine Learning / Deep Learning Wizards needing help and assistance for their models from ChatGPT. It is particularly useful for Beginners seeking to master real-life Data Science Projects in no time without the need to learn everything from scratch. or Data Scientists interested in boosting their work with Artificial Intelligence and Neural Networks or Everybody in a Data-related Profession wanting to leverage the power of ChatGPT for their day-to-day work. or Data Analysts seeking to outsource the most time-consuming parts of their work to ChatGPT. or Machine Learning / Deep Learning Wizards needing help and assistance for their models from ChatGPT.

Enroll now: ChatGPT for Deep Learning with Python Keras and Tensorflow

Summary

Title: ChatGPT for Deep Learning with Python Keras and Tensorflow

Price: $54.99

Average Rating: 4.44

Number of Lectures: 193

Number of Quizzes: 8

Number of Published Lectures: 193

Number of Published Quizzes: 8

Number of Curriculum Items: 201

Number of Published Curriculum Objects: 201

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Utilize ChatGPT for real-life Data Science and Deep Learning projects
  • Let ChatGPT do the coding work for you (Python, Pandas, Keras etc.)
  • Use ChatGPT to select the most suitable Neural Network for your task
  • Utilize ChatGPT to analyse and interpret the outcomes of your Deep Learning models
  • Ask ChatGPT to critically assess and improve your Neural Networks
  • Perform an Explanatory Data Analysis with ChatGPT and Python
  • Use ChatGPT for Data Manipulation, Aggregation, advanced Pandas coding & more
  • Utilize ChatGPT to fit, evaluate and optimize FNN, CNN, RNN and LSTM models
  • Utilize ChatGPT for Regression and Classification tasks using Keras & Tensorflow
  • Utilize ChatGPT for Image Recognition
  • Utilize ChatGPT for Time Series Prediction
  • Use ChatGPT for Error Handling and Troubleshooting

Who Should Attend

  • Beginners seeking to master real-life Data Science Projects in no time without the need to learn everything from scratch.
  • Data Scientists interested in boosting their work with Artificial Intelligence and Neural Networks
  • Everybody in a Data-related Profession wanting to leverage the power of ChatGPT for their day-to-day work.
  • Data Analysts seeking to outsource the most time-consuming parts of their work to ChatGPT.
  • Machine Learning / Deep Learning Wizards needing help and assistance for their models from ChatGPT.

Target Audiences

  • Beginners seeking to master real-life Data Science Projects in no time without the need to learn everything from scratch.
  • Data Scientists interested in boosting their work with Artificial Intelligence and Neural Networks
  • Everybody in a Data-related Profession wanting to leverage the power of ChatGPT for their day-to-day work.
  • Data Analysts seeking to outsource the most time-consuming parts of their work to ChatGPT.
  • Machine Learning / Deep Learning Wizards needing help and assistance for their models from ChatGPT.

### Updated: Now including the latest models  GPT-4o and GPT-4o mini ###

Welcome to a game-changing learning experience with “ChatGPT for Deep Learning using Python Keras and TensorFlow”.

This unique course combines the power of ChatGPT with the technical depth of Python, Keras, and TensorFlow to offer you an innovative approach to tackling complex Deep Learning projects. Whether you’re a beginner or a seasoned Data Scientist, this course will significantly enhance your skill set, making you more proficient and efficient in your work.

Why This Course?

Deep learning and Artificial Intelligence are revolutionizing industries across the globe, but mastering these technologies often requires a significant time investment (for theory and coding). This course cuts through the complexity, leveraging ChatGPT to simplify the learning curve and expedite your project execution. You’ll learn how to harness the capabilities of AI to streamline tasks from data processing to complex model training, all without needing exhaustive prior knowledge of the underlying mathematics and Python code.

Comprehensive Learning Objectives

By the end of this course, you will be able to apply the most promising ChatGPT prompting strategies and techniques in real-world scenarios:

  • ChatGPT Integration: Utilize ChatGPT effectively to automate and enhance various stages of your Data Science projects, including coding, model development, and result analysis.

  • Data Management: Master techniques for loading, cleaning, and visualizing data using Python libraries like Pandas, Matplotlib, and Seaborn.

  • Deep Learning Modeling: Gain hands-on experience in constructing and fine-tuning Neural Networks for tasks such as Image Recognition with CNNs, Time Series prediction with RNNs and LSTMs, and classification and regression with Feedforward Neural Networks (FNN), using ChatGPT as your assistant.

  • Advanced Techniques: Learn how to best utilize ChatGPT to select the best Neural Network architecture for your projects. Optimize your models with techniques like Hyperparameter Tuning and Regularization, and enhance your models’ performance with strategies like Data Augmentation.

  • Theoretical Foundations: While the course emphasizes practical skills, you’ll also gain a clear understanding of the theoretical underpinnings of the models you’re using, helping you make informed decisions about your approach to each project.

Course Structure

This course is structured around interactive, project-based learning. Each module is designed as a “Do-It-Yourself” project that challenges you to apply what you’ve learned in real-time. You’ll receive:

  • Detailed Project Assignments: These assignments mimic real-world problems and are designed to test your application of the course material.

  • Supporting Materials: Access to a wealth of resources, including sample prompts for ChatGPT, code snippets, and datasets.

  • Video Solutions: At the end of each project, a detailed video solution will guide you through the expected outcomes and provide additional insights.

  • Prompting Strategies: Exclusive content on effective prompting for both GPT-3.5 / GPT-4o mini (free) and GPT-4 / GPT-4o (Plus), helping you maximize your use of these powerful tools.

Who Should Enroll?

  • Data Science Beginners: If you are new to Data Science and Deep Learning, this course offers a friendly introduction to complex concepts and applications, significantly reducing your learning time.

  • Experienced Data Scientists and Analysts: For those looking to enhance their productivity and incorporate cutting-edge AI tools into their workflows, this course provides advanced strategies and techniques to streamline and optimize your projects.

Are You Ready to Revolutionize Your Data Science Capabilities?

Enroll now to begin your journey at the forefront of artificial intelligence and deep learning innovation. Transform your professional capabilities and embrace the future of AI with confidence!

Course Curriculum

Chapter 1: Getting started

Lecture 1: Welcome and Introduction

Lecture 2: Sneak Preview: Deep Learning with ChatGPT

Lecture 3: How to get the most out of this course

Lecture 4: Course Overview

Lecture 5: Download Materials / Downloads

Chapter 2: ChatGPT Introduction

Lecture 1: What is ChatGPT and how does it work?

Lecture 2: ChatGPT vs. Search Engines

Lecture 3: Artificial Intelligence vs. Human Intelligence

Lecture 4: Creating a ChatGPT account and getting started

Lecture 5: **Update July 2024**

Lecture 6: Features, Options and Products around GPT models

Lecture 7: Update (July 2024): Products and Availability (FREE vs. PLUS)

Lecture 8: Navigating the OpenAI Website

Lecture 9: What is a Token and how do Tokens work?

Lecture 10: Prompt Engineering Techniques (Part 1)

Lecture 11: Prompt(s) used in previous Lecture

Lecture 12: Prompt Engineering Techniques (Part 2)

Lecture 13: Prompt(s) used in previous Lecture

Lecture 14: Prompt Engineering Techniques (Part 3)

Lecture 15: Prompt(s) used in previous Lecture

Chapter 3: Python Installation

Lecture 1: Download and Install Anaconda

Lecture 2: How to open Jupyter Notebooks

Lecture 3: How to work with Jupyter Notebooks

Lecture 4: How to create a customized Environment for Deep Learning

Chapter 4: Understanding Deep Learning and Neural Networks – with ChatGPT

Lecture 1: Deep Learning vs. traditional Machine Learning

Lecture 2: Prompt(s) used in previous Lecture

Lecture 3: Neural Network Types – Overview

Lecture 4: Prompt(s) used in previous Lecture

Lecture 5: The Feedforward Neural Network (FNN) explained

Lecture 6: Prompt(s) used in previous Lecture

Lecture 7: Neural Network Types – CNN and RNN at a glance

Lecture 8: Prompt(s) used in previous Lecture

Lecture 9: Pre-trained GPT models vs. customized Neural Networks – What to use when

Lecture 10: Prompt(s) used in previous Lecture

Chapter 5: Introduction Project: Explore an unknown Dataset with ChatGPT and Pandas

Lecture 1: Project Introduction

Lecture 2: GPT Model Upgrades (July 24)

Lecture 3: Project Assignment

Lecture 4: Providing the Dataset to GPT-3.5 / GPT-4o mini

Lecture 5: Prompt(s) used in previous Lecture

Lecture 6: Task 1: Inspecting the Dataset with GPT-3.5 / GPT-4o mini

Lecture 7: Prompt(s) used in previous Lecture

Lecture 8: Task 2: Brainstorming with GPT-3.5 / GPT-4o mini

Lecture 9: Prompt(s) used in the previous Lecture

Lecture 10: Task 3: Data Cleaning with GPT-3.5 / GPT-4o mini

Lecture 11: Prompt(s) used in previous Lecture

Lecture 12: Task 4: Identifying and Creating new Features with GPT-3.5 / GPT-4o mini

Lecture 13: Prompt(s) used in previous Lecture

Lecture 14: Task 5: Saving the cleaned Dataset

Lecture 15: Prompt(s) used in previous Lecture

Lecture 16: Loading the Dataset with GPT-4 / GPT-4o

Lecture 17: Prompt(s) used in previous Lecture

Lecture 18: Initial Data Inspection and Brainstorming with GPT-4 / GPT-4o

Lecture 19: Prompt(s) used in previous Lecture

Lecture 20: Data Cleaning with GPT-4 / GPT-4o

Lecture 21: Prompt(s) used in previous Lecture

Lecture 22: Troubleshooting

Lecture 23: Identifying and Creating new Features with GPT-4 / GPT-4o

Lecture 24: Prompt(s) used in previous Lecture

Lecture 25: How to download and save the cleaned Dataset from GPT-4 / GPT-4o

Lecture 26: Prompt(s) used in previous Lecture

Lecture 27: Conclusion, Final Remarks and Troubleshooting

Chapter 6: Using ChatGPT for Explanatory Data Analysis (EDA)

Lecture 1: Project Introduction

Lecture 2: Project Assignment

Lecture 3: Task 1: (Up-) Loading the Dataset and first Inspection

Lecture 4: Prompt(s) used in the previous Lecture

Lecture 5: Excursus: Behind the Scenes

Lecture 6: Task 2: Brainstorming: Goals and Objectives of an EDA

Lecture 7: Prompt(s) used in the previous Lecture

Lecture 8: Task 3: Univariate Data Analysis

Lecture 9: Prompt(s) used in the previous Lecture

Lecture 10: Task 4: Multivariate Data Analysis: Correlations

Lecture 11: Prompt(s) used in the previous Lecture

Lecture 12: Task 5: Exploring Factors influencing Income

Lecture 13: Prompt(s) used in the previous Lecture

Lecture 14: Task 6: Implications & Outlook

Lecture 15: Prompt(s) used in the previous Lecture

Lecture 16: The Code reviewed & Troubleshooting

Chapter 7: Using ChatGPT for Binary Classification with Feedforward Neural Networks (FNN)

Lecture 1: Project Introduction

Lecture 2: Project Assignment

Lecture 3: Task 1: (Up-) Loading the Dataset and first Inspection

Lecture 4: Prompt(s) used in previous Lecture

Lecture 5: Task 2: Brainstorming: How to best tackle a FNN Classification Project

Lecture 6: Prompt(s) used in previous Lecture

Lecture 7: Task 3: Data Pre-processing and Feature Engineering (Theory)

Lecture 8: Prompt(s) used in previous Lecture

Lecture 9: Feature-specific questions and considerations

Lecture 10: Prompt(s) used in previous Lecture

Lecture 11: Actions derived from Brainstorming

Lecture 12: Task 4: Data Pre-Processing and Feature Engineering (Code)

Lecture 13: Prompt(s) used in previous Lecture

Lecture 14: Task 5: Defining and Fitting an FNN Baseline Model

Lecture 15: Prompt(s) used in previous Lecture

Instructors

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  • 5 stars: 7 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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