10-01-2024, 10:21 PM
![[Image: 359020115_tuto.jpg]](https://img87.pixhost.to/images/599/359020115_tuto.jpg)
5.03 GB | 00:11:57 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
1 - Welcome and Introduction (22.18 MB)
2 - How to get the most out of this course (39.14 MB)
3 - Course Materials Downloads (27.04 MB)
1 - Download and Install Anaconda (63.81 MB)
2 - How to open Jupyter Notebooks (84.37 MB)
3 - How to work with Jupyter Notebooks (81.15 MB)
1 - Introduction (12.52 MB)
2 - Loading and inspecting the Dataset with Pandas (67.21 MB)
3 - Prices and Financial Returns (40.28 MB)
4 - Simple Moving Averages (SMA) (41.4 MB)
5 - Excursus Creating Technical Indicators with Pandas (24.55 MB)
6 - MACD Lines (22.7 MB)
7 - Relative Strength Index (RSI) (18.28 MB)
8 - Stochastic Oscillators & Conclusion (103.33 MB)
1 - What is ChatGPT and how does it work (17.22 MB)
10 - Prompt Engineering Techniques (Part 2) (51.34 MB)
11 - Prompt Engineering Techniques (Part 3) (70.8 MB)
2 - ChatGPT vs Search Engines (33.07 MB)
3 - Artificial Intelligence vs Human Intelligence (25.3 MB)
4 - Creating a ChatGPT account and getting started (51.63 MB)
5 - Update August 2024 (55.64 MB)
6 - Features, Options and Products around GPT models (35.09 MB)
7 - Navigating the OpenAI Website (98.84 MB)
8 - What is a Token and how do Tokens work (63.34 MB)
9 - Prompt Engineering Techniques (Part 1) (107.16 MB)
1 - Introduction and Overview (7.82 MB)
2 - Reinforcement Learning vs traditional Machine Learning (84.44 MB)
3 - Reinforcement Learning - Use Cases (68.66 MB)
4 - Reinforcement Learning - Models and Algorithms (74.07 MB)
1 - Introduction (58.32 MB)
10 - Training an RL Agent with Q-Tables (75.25 MB)
11 - Q-learning explained - the Hyperparameters (107.92 MB)
12 - Q-learning explained - Discretization of the State Space (39.45 MB)
13 - Q-learning explained - the Q-Table (66.28 MB)
14 - Q-learning explained - Visualizing the Q-Table (75.65 MB)
15 - Q-learning explained - Updating the Q-Table (115.95 MB)
16 - Testing the trained Agent (51.07 MB)
17 - Visualizing the trained Agent (31.3 MB)
18 - Improving the Agent Training (Brainstorming) (91.09 MB)
19 - Excursus Randomness and Reproducibility of random events (122.06 MB)
2 - Project Assignment (36.07 MB)
20 - Training and Testing with Reproducibility (random seed) (72.52 MB)
21 - Tuning the Hyperparameters (42.2 MB)
22 - Increasing the number of Training Episodes (50.26 MB)
23 - Visualizing the Training Process and Performance Plateaus (78.41 MB)
24 - Increasing the State Space Discretization (78.48 MB)
25 - Conclusion and Outlook (38.66 MB)
3 - One Random Episode with Human Rendering (60.75 MB)
4 - Defining the maximum number of steps per Episode (36.66 MB)
5 - The code explained - line for line (77.46 MB)
6 - Running multiple random Episodes with human rendering (89.91 MB)
7 - Performance Measurement and Success Evaluation (104.77 MB)
8 - Running multiple Episodes without human Rendering (65.49 MB)
9 - Excursus RGB Rendering with Visualization (49.22 MB)
1 - Introduction (53.13 MB)
10 - SavingLoading and Testing a trained Agent (50.93 MB)
11 - The trained Agent live in Action (20.6 MB)
2 - One Random Episode with Human Rendering (80.14 MB)
3 - Running multiple random Episodes with human rendering (11.04 MB)
4 - Performance Measurement and Success Evaluation (22.54 MB)
5 - Running multiple Episodes without human Rendering (14.29 MB)
6 - Saving and visualizing successful Episodes (24.59 MB)
7 - Creating an appropriate Observation Space for Training (77.47 MB)
8 - State Space Discretization (34.54 MB)
9 - Training a RL Agent for the Lunar Lander (71.44 MB)
1 - Introduction and Assignment (28.92 MB)
10 - Reinforcement Learning with multiple Episodes (95.09 MB)
11 - Hyperparameter Optimization (46.56 MB)
12 - Testing the Agent on new Data (78.21 MB)
13 - The Importance of Trading Costs (38.8 MB)
14 - Modifying the Rewards Function (96.25 MB)
15 - Performance Evaluation and Identifying Overfitting (34.37 MB)
2 - Loading and Preparing the Dataset (22.71 MB)
3 - Splitting into Training and Test Set (20 MB)
4 - Discretization and Quantile Binning (Part 1) (70.3 MB)
5 - Discretization and Quantile Binning (Part 2) (35.99 MB)
6 - Discretization and Quantile Binning (Part 3) (67.52 MB)
7 - Trading Profits and Losses (47.45 MB)
8 - Introduction to Agent Training (one Episode) (61.02 MB)
9 - Training of an Algo Trading Agent - explained (120.11 MB)
1 - Welcome and Introduction (22.18 MB)
2 - How to get the most out of this course (39.14 MB)
3 - Course Materials Downloads (27.04 MB)
1 - Download and Install Anaconda (63.81 MB)
2 - How to open Jupyter Notebooks (84.37 MB)
3 - How to work with Jupyter Notebooks (81.15 MB)
1 - Introduction (12.52 MB)
2 - Loading and inspecting the Dataset with Pandas (67.21 MB)
3 - Prices and Financial Returns (40.28 MB)
4 - Simple Moving Averages (SMA) (41.4 MB)
5 - Excursus Creating Technical Indicators with Pandas (24.55 MB)
6 - MACD Lines (22.7 MB)
7 - Relative Strength Index (RSI) (18.28 MB)
8 - Stochastic Oscillators & Conclusion (103.33 MB)
1 - What is ChatGPT and how does it work (17.22 MB)
10 - Prompt Engineering Techniques (Part 2) (51.34 MB)
11 - Prompt Engineering Techniques (Part 3) (70.8 MB)
2 - ChatGPT vs Search Engines (33.07 MB)
3 - Artificial Intelligence vs Human Intelligence (25.3 MB)
4 - Creating a ChatGPT account and getting started (51.63 MB)
5 - Update August 2024 (55.64 MB)
6 - Features, Options and Products around GPT models (35.09 MB)
7 - Navigating the OpenAI Website (98.84 MB)
8 - What is a Token and how do Tokens work (63.34 MB)
9 - Prompt Engineering Techniques (Part 1) (107.16 MB)
1 - Introduction and Overview (7.82 MB)
2 - Reinforcement Learning vs traditional Machine Learning (84.44 MB)
3 - Reinforcement Learning - Use Cases (68.66 MB)
4 - Reinforcement Learning - Models and Algorithms (74.07 MB)
1 - Introduction (58.32 MB)
10 - Training an RL Agent with Q-Tables (75.25 MB)
11 - Q-learning explained - the Hyperparameters (107.92 MB)
12 - Q-learning explained - Discretization of the State Space (39.45 MB)
13 - Q-learning explained - the Q-Table (66.28 MB)
14 - Q-learning explained - Visualizing the Q-Table (75.65 MB)
15 - Q-learning explained - Updating the Q-Table (115.95 MB)
16 - Testing the trained Agent (51.07 MB)
17 - Visualizing the trained Agent (31.3 MB)
18 - Improving the Agent Training (Brainstorming) (91.09 MB)
19 - Excursus Randomness and Reproducibility of random events (122.06 MB)
2 - Project Assignment (36.07 MB)
20 - Training and Testing with Reproducibility (random seed) (72.52 MB)
21 - Tuning the Hyperparameters (42.2 MB)
22 - Increasing the number of Training Episodes (50.26 MB)
23 - Visualizing the Training Process and Performance Plateaus (78.41 MB)
24 - Increasing the State Space Discretization (78.48 MB)
25 - Conclusion and Outlook (38.66 MB)
3 - One Random Episode with Human Rendering (60.75 MB)
4 - Defining the maximum number of steps per Episode (36.66 MB)
5 - The code explained - line for line (77.46 MB)
6 - Running multiple random Episodes with human rendering (89.91 MB)
7 - Performance Measurement and Success Evaluation (104.77 MB)
8 - Running multiple Episodes without human Rendering (65.49 MB)
9 - Excursus RGB Rendering with Visualization (49.22 MB)
1 - Introduction (53.13 MB)
10 - SavingLoading and Testing a trained Agent (50.93 MB)
11 - The trained Agent live in Action (20.6 MB)
2 - One Random Episode with Human Rendering (80.14 MB)
3 - Running multiple random Episodes with human rendering (11.04 MB)
4 - Performance Measurement and Success Evaluation (22.54 MB)
5 - Running multiple Episodes without human Rendering (14.29 MB)
6 - Saving and visualizing successful Episodes (24.59 MB)
7 - Creating an appropriate Observation Space for Training (77.47 MB)
8 - State Space Discretization (34.54 MB)
9 - Training a RL Agent for the Lunar Lander (71.44 MB)
1 - Introduction and Assignment (28.92 MB)
10 - Reinforcement Learning with multiple Episodes (95.09 MB)
11 - Hyperparameter Optimization (46.56 MB)
12 - Testing the Agent on new Data (78.21 MB)
13 - The Importance of Trading Costs (38.8 MB)
14 - Modifying the Rewards Function (96.25 MB)
15 - Performance Evaluation and Identifying Overfitting (34.37 MB)
2 - Loading and Preparing the Dataset (22.71 MB)
3 - Splitting into Training and Test Set (20 MB)
4 - Discretization and Quantile Binning (Part 1) (70.3 MB)
5 - Discretization and Quantile Binning (Part 2) (35.99 MB)
6 - Discretization and Quantile Binning (Part 3) (67.52 MB)
7 - Trading Profits and Losses (47.45 MB)
8 - Introduction to Agent Training (one Episode) (61.02 MB)
9 - Training of an Algo Trading Agent - explained (120.11 MB)
Screenshot
![[Image: VouKCXXJ_o.jpg]](https://images2.imgbox.com/ac/3a/VouKCXXJ_o.jpg)
![[Image: signature.png]](https://softwarez.info/images/avsg/signature.png)
