ChatGPT for Deep Learning with Python Keras and Tensorflow

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Alexander Hagmann

14:12:44

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1. Introduction

1. Welcome and Introduction.mp4

02:34

2. Sneak Preview Deep Learning with ChatGPT.mp4

06:13

3. How to get the most out of this course.mp4

05:27

4. Course Overview.mp4

05:45

5.1 Course Materials.zip

5. Download Materials Downloads.mp4

02:41

2. ChatGPT Introduction

1. What is ChatGPT and how does it work.mp4

03:56

2. ChatGPT vs. Search Engines.mp4

05:23

3. Artificial Intelligence vs. Human Intelligence.mp4

04:45

4. Creating a ChatGPT account and getting started.mp4

07:44

5. Design Update November 2023.mp4

01:47

6. Features, Options and Products around GPT models.mp4

06:41

7. Navigating the OpenAI Website.mp4

07:38

8. What is a Token and how do Tokens work.mp4

07:33

9. Prompt Engineering Techniques (Part 1).mp4

12:33

10. Prompt(s) used in previous Lecture.html

11. Prompt Engineering Techniques (Part 2).mp4

05:03

12. Prompt(s) used in previous Lecture.html

13. Prompt Engineering Techniques (Part 3).mp4

08:01

14. Prompt(s) used in previous Lecture.html

3. Python Installation

1. Download and Install Anaconda.mp4

06:15

2. How to open Jupyter Notebooks.mp4

12:24

3. How to work with Jupyter Notebooks.mp4

17:25

4. How to create a customized Environment for Deep Learning.mp4

07:28

4. Understanding Deep Learning and Neural Networks - with ChatGPT

1. Deep Learning vs. traditional Machine Learning.mp4

07:01

2. Prompt(s) used in previous Lecture.html

3. Neural Network Types - Overview.mp4

07:32

4. Prompt(s) used in previous Lecture.html

5. The Feedforward Neural Network (FNN) explained.mp4

08:36

6. Prompt(s) used in previous Lecture.html

7. Neural Network Types - CNN and RNN at a glance.mp4

13:30

8. Prompt(s) used in previous Lecture.html

9. Pre-trained GPT models vs. customized Neural Networks - What to use when.mp4

08:02

10. Prompt(s) used in previous Lecture.html

11. Test your Deep Learning Neural Networks Knowledge.html

5. Introduction Project Explore an unknown Dataset with ChatGPT and Pandas

1. Project Introduction.mp4

03:21

2. Project Assignment.mp4

05:44

3. Providing the Dataset to GPT3.5.mp4

03:20

4. Prompt(s) used in previous Lecture.html

5. Task 1 Inspecting the Dataset with GPT3.5.mp4

10:44

6. Prompt(s) used in previous Lecture.html

7. Task 2 Brainstorming with GPT3.5.mp4

05:19

8. Prompt(s) used in the previous Lecture.html

9. Task 3 Data Cleaning with GPT3.5.mp4

11:37

10. Prompt(s) used in previous Lecture.html

11. Task 4 Identifying and Creating new Features with GPT3.5.mp4

06:35

12. Prompt(s) used in previous Lecture.html

13. Task 5 Saving the cleaned Dataset.mp4

04:02

14. Prompt(s) used in previous Lecture.html

15. Loading the Dataset with GPT4.mp4

02:10

16. Prompt(s) used in previous Lecture.html

17. Initial Data Inspection and Brainstorming with GPT4.mp4

07:11

18. Prompt(s) used in previous Lecture.html

19. Data Cleaning with GPT4.mp4

06:45

20. Prompt(s) used in previous Lecture.html

21. Troubleshooting.mp4

05:03

22. Identifying and Creating new Features with GPT4.mp4

06:46

23. Prompt(s) used in previous Lecture.html

24. How to download and save the cleaned Dataset from GPT4.mp4

02:30

25. Prompt(s) used in previous Lecture.html

26. Conclusion, Final Remarks and Troubleshooting.mp4

06:40

6. Using ChatGPT for Explanatory Data Analysis (EDA)

1. Project Introduction.mp4

02:53

2. Project Assignment.mp4

04:00

3. Task 1 (Up-) Loading the Dataset and first Inspection.mp4

03:57

4. Prompt(s) used in the previous Lecture.html

5. Excursus Behind the Scenes.mp4

02:48

6. Task 2 Brainstorming Goals and Objectives of an EDA.mp4

03:29

7. Prompt(s) used in the previous Lecture.html

8. Task 3 Univariate Data Analysis.mp4

08:27

9. Prompt(s) used in the previous Lecture.html

10. Task 4 Multivariate Data Analysis Correlations.mp4

07:17

11. Prompt(s) used in the previous Lecture.html

12. Task 5 Exploring Factors influencing Income.mp4

07:15

13. Prompt(s) used in the previous Lecture.html

14. Task 6 Implications & Outlook.mp4

03:29

15. Prompt(s) used in the previous Lecture.html

16. The Code reviewed & Troubleshooting.mp4

09:03

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

1. Project Introduction.mp4

02:57

2. Project Assignment.mp4

05:43

3. Task 1 (Up-) Loading the Dataset and first Inspection.mp4

02:24

4. Prompt(s) used in previous Lecture.html

5. Task 2 Brainstorming How to best tackle a FNN Classification Project.mp4

08:57

6. Prompt(s) used in previous Lecture.html

7. Task 3 Data Pre-processing and Feature Engineering (Theory).mp4

10:00

8. Prompt(s) used in previous Lecture.html

9. Feature-specific questions and considerations.mp4

06:10

10. Prompt(s) used in previous Lecture.html

11. Actions derived from Brainstorming.mp4

02:48

12. Task 4 Data Pre-Processing and Feature Engineering (Code).mp4

07:38

13. Prompt(s) used in previous Lecture.html

14. Task 5 Defining and Fitting an FNN Baseline Model.mp4

09:58

15. Prompt(s) used in previous Lecture.html

16. Task 6 Evaluation of Baseline Model on the Test Set.mp4

08:06

17. Prompt(s) used in previous Lecture.html

18. Task 7 Model Optimization - Theory.mp4

05:46

19. Prompt(s) used in previous Lecture.html

20. Task 7 Model Optimization - Code.mp4

08:32

21. Prompt(s) used in the previous Lecture.html

22. Performance Evaluation and Model Architecture.mp4

10:16

23. Prompt(s) used in the previous Lecture.html

24. Modifying the number of Hidden Layers.mp4

03:34

25. Task 8 Decision Thresholds (Precision vs. Recall).mp4

07:54

26. Prompt(s) used in the previous Lecture.html

27. The full project using GPT4 (Part 1).mp4

07:57

28. The full project using GPT4 (Part 2).mp4

06:22

29. Bonus Task Feature Importance and Outlook (Part 1).mp4

08:01

30. Prompt(s) used in the previous Lecture.html

31. Bonus Task Feature Importance and Outlook (Part 2).mp4

08:39

32. Prompt(s) used in the previous Lecture.html

8. Using ChatGPT for Image Recognition with Convolutional Neural Networks (CNN)

1. Project Introduction.mp4

03:51

2. Project Assignment.mp4

04:22

3. Task 1 Downloading the Dataset.mp4

02:40

4. Task 2 Loading the Dataset with Python and first Data Inspection.mp4

11:06

5. Prompt(s) used in the previous Lecture.html

6. Task 3 Displaying the images with Python.mp4

03:41

7. Prompt(s) used in the previous Lecture.html

8. Task 4 Loading, Merging, formatting and storing the full dataset.mp4

04:36

9. Prompt(s) used in the previous Lecture.html

10. Task 5 Data Preprocessing.mp4

04:45

11. Prompt(s) used in the previous Lecture.html

12. Task 6 Brainstorming.mp4

03:49

13. Prompt(s) used in the previous Lecture.html

14. Task 7 Creating and Training a Baseline CNN model.mp4

08:18

15. Prompt(s) used in the previous Lecture.html

16. Task 8 Evaluating the Baseline Model.mp4

13:00

17. Prompt(s) used in the previous Lecture.html

18. Task 9 Data Augmentation & Model Checkpointing.mp4

12:02

19. Prompt(s) used in the previous Lecture.html

20. Model Checkpointing.mp4

04:17

21. Advanced Data Augmentation & Fine Tuning.mp4

05:29

22. Prompt(s) used in the previous Lecture.html

23. Task 10 Increasing Model Architecture Complexity & Dropout.mp4

08:49

24. Prompt(s) used in the previous Lecture.html

25. Adding Dropout.mp4

04:13

26. Prompt(s) used in the previous Lecture.html

9. Using ChatGPT for Time Series Prediction with Recurrent Neural Networks (RNN)

1. Project Introduction.mp4

02:55

2. Project Assignment.mp4

06:19

3. Task 1 (Up-) Loading the Dataset and first Inspection.mp4

05:00

4. Prompt(s) used in the previous Lecture.html

5. Task 2 Explanatory Data Analysis (EDA).mp4

05:27

6. Prompt(s) used in previous Lecture.html

7. Task 3 Brainstorming How to best tackle an RNN Time Series Project.mp4

08:49

8. Prompt(s) used in the previous Lecture.html

9. Task 4 Covariance Stationarity and other Time Series specific aspects.mp4

10:27

10. Prompt(s) used in the previous Lecture.html

11. Task 5 Feature Creation - adding temporal features.mp4

11:27

12. Prompt(s) used in the previous Lecture.html

13. Task 6 Creating and fitting a Baseline Model.mp4

11:25

14. Prompt(s) used in the previous Lecture.html

15. Performance Evaluation on the Test Set.mp4

05:59

16. Prompt(s) used in the previous Lecture.html

17. Finding the optimal look-back period (Lags).mp4

05:21

18. Task 7 Adding more Features to the model (Part 1).mp4

07:17

19. Prompt(s) used in the previous Lecture.html

20. Task 7 Adding more Features to the model (Part 2).mp4

03:51

21. Task 8 Adding Temporal Features to the model.mp4

08:25

22. Prompt(s) used in the previous Lecture.html

23. Task 9 Increase the complexity of the LSTM Architecture.mp4

06:42

24. Prompt(s) used in the previous Lecture.html

25. Task 10 Adding Early Stopping, Validation & more.mp4

03:13

26. Final Assessment and potential Improvements.mp4

04:32

27. Prompt(s) used in the previous Lecture.html

10. Appendix Pandas Crash Course

1. Introduction.mp4

02:24

2. Intro to Tabular Data Pandas.mp4

04:19

3. Create your very first Pandas DataFrame (from csv).mp4

06:58

4. Loading a CSV-file into Pandas.html

5. How to read CSV-files from other Locations.mp4

03:36

6. Pandas Display Options and the methods head() & tail().mp4

06:41

7. First Data Inspection.mp4

11:25

8. Summary Statistics.html

9. Built-in Functions, Attributes and Methods with Pandas.mp4

12:06

10. Make it easy TAB Completion and Tooltip.mp4

08:57

11. Selecting Columns.mp4

06:05

12. Selecting one Column with the dot notation.mp4

02:16

13. Selecting Columns.html

14. Zero-based Indexing and Negative Indexing.mp4

03:04

15. Selecting Rows with iloc (position-based indexing).mp4

10:07

16. Slicing Rows and Columns with iloc (position-based indexing).mp4

04:39

17.1 pandas iloc.pdf

17. Position-based Indexing Cheat Sheets.html

18. Position-based Indexing 1.html

19. Position-based Indexing 2.html

20. Selecting Rows with loc (label-based indexing).mp4

03:14

21. Slicing Rows and Columns with loc (label-based indexing).mp4

10:21

22.1 Pandas loc.pdf

22. Label-based Indexing Cheat Sheets.html

23. Label-based Indexing 1.html

24. Label-based Indexing 2.html

25. First Steps with Pandas Series.mp4

03:53

26. Analyzing Numerical Series with unique(), nunique() and value counts().mp4

13:50

27. Analyzing non-numerical Series with unique(), nunique(), value counts().mp4

07:17

28. First Steps with Pandas Index Objects.mp4

05:57

29. Filtering DataFrames by one Condition.mp4

10:20

30. Filtering DataFrames by many Conditions.mp4

04:45

31. Sorting DataFrames with sort index() and sort values().mp4

09:09

32. Visualizing Data with the plot() method.mp4

08:48

33. Creating Histograms.mp4

04:34

34. Creating Scatterplots.mp4

07:18

35. Understanding GroupBy objects.mp4

08:05

36. Splitting with many Keys.mp4

06:49

37. split-apply-combine explained.mp4

09:36

Description

Master Image Recognition, Time Series Prediction, Regression and Classification with ChatGPT! A Project-based Course.

What You'll 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 is this for?

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.

What You Need to Know?

An internet connection capable of streaming HD videos.

Some Data Science or Machine Learning related background (not required but it helps)

First Experience with Python and the Python Data Science Ecosystem (not required but it helps)

More details

Description
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. Youâ€™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 (free) and GPT-4 (paid), 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!
Who this course is for:
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.

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Alexander is a Data Scientist and Finance Professional with more than 10 years of experience in the Finance and Investment Industry. He is also a Bestselling Udemy Instructor for - Data Analysis/Manipulation with Pandas- (Financial) Data Science- Python for Business and Finance- Algorithmic TradingAlexander started his career in the traditional Finance sector and moved step-by-step into Data-driven and Artificial Intelligence-driven Finance roles. He is currently working on cutting-edge Fintech projects and creates solutions for Algorithmic Trading and Robo Investing. And Alexander is excited to share his knowledge with others here on Udemy. Students who completed his courses work in the largest and most popular tech and finance companies all over the world. Alexander´s courses have one thing in common: Content and concepts are practical and real-world proven. The clear focus is on acquiring skills and understanding concepts rather than memorizing things. Alexander holds a Master´s degree in Finance and passed all three CFA Exams (he is currently no active member of the CFA Institute).

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