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Build Midjourney/DALL-E Machine Learning AI Image Clone
Mammoth Interactive Courses Introduction
00 About Mammoth Interactive (1:12)
01 How To Learn Online Effectively (13:46)
Source Files
LEVEL 0 - Intro to Midjourney DALL-E Image Generation Masterclass
01 Welcome To Midjourney Dall-E Image Generation Masterclass (3:11)
02 How Midjourney Works (1:32)
03 How Dall-E Works (6:04)
Source Files
LEVEL 1 - Midjourney AI Prompt Engineering - Midjourney for Business
01.01. Introduction (2:47)
2.1. How to get Midjourney (8:35)
02.01. Generating Striking Logos for Businesses- (13:21)
02.03. Finishing Touches on Logo (5:16)
02.02. Remove background and edit logo in Illustrator_1 (13:23)
03 01 Conceptualizing Business Cards (15:20)
03.02. Refining Business Card in illustrator (11:31)
03.03. Adding more elements and Final retouches (9:56)
04.01. Creating Letterhead Templates pt.1 (5:21)
04.01. Creating Letterhead Templates pt.2 (5:51)
04.02. Edit Letterhead in Photoshop pt.1 (5:54)
04.02. Edit Letterhead in Photoshop pt.2 (5:33)
04.03. Finalizing edits and use template in Microsoft Word (9:37)
05 01 Designing Invoice Templates_1 (10:02)
05.02 Modifying invoice template (10:52)
05.03 Completing the elements pt.1 (5:49)
05.03 Completing the elements pt.2 (5:50)
06 01 Building Social Media Graphics_1 (10:01)
06.02. Erase and adjust elements in Photoshop (10:16)
06.03 Editing and Improving Social Media Graphics in Photoshop & Canva pt.1 (5:15)
06.03 Editing and Improving Social Media Graphics in Photoshop & Canva pt.2 (5:49)
07 01 Create Power Point presentation concept design_1 (9:38)
07.02. Edit Cover page in Photoshop pt.2 (6:22)
07.02. Edit Cover page in Photoshop pt.3 (5:18)
07.02. Edit Cover page in Photoshop pt (5:22)
07.03. Generate more slider designs (4:58)
07.04. Edit sliders and use graphics in power point pt.1 (5:43)
07.04. Edit sliders and use graphics in power point pt.2 (5:14)
08 01 Conceptualizing T-Shirt Designs_1 (10:26)
08.02. Visualizing T-Shirt Mockups in Illustrator (10:09)
09.01. Creating Designs for Flyers and Invitations (10:27)
09.02. Editing and Polishing Flyer design in Photoshop pt.1 (10:21)
09.02. Editing and Polishing Flyer design in Photoshop pt.2 (7:08)
09.03. Modify and Refine Invitation design (10:00)
10.01. Building Email Header Concepts pt.2 (5:24)
10.01. Building Email Header Concepts pt (5:23)
10.02. Modifying Email Header design in Photoshop (9:58)
10.03. Completing the email header design (7:31)
Conclusion, Tips and Tricks pt.2 (7:40)
Conclusion, Tips and Tricks pt (7:49)
Source Files
LEVEL 2 - Python Fundamentals - Introduction
00. Introduction (4:42)
01 Code Python on the Web
02.01 What is Google Colab (4:24)
02.02 What If I Get Errors (2:40)
02.03 How Do I Terminate a Session (2:40)
02 Python Language Fundamentals
02. Variables (19:17)
03. Type Conversion Examples (10:04)
04. Operators (7:04)
05. Operators Examples (21:52)
06. Collections (8:23)
07. Lists (11:38)
08. Multidimensional List Examples (8:05)
09. Tuples Examples (8:34)
10. Dictionaries Examples (14:24)
11. Ranges Examples (8:30)
12. Conditionals (6:41)
13. If Statement Examples (10:16)
14. If Statement Variants Examples (11:18)
15. Loops (7:00)
16. While Loops Examples (11:30)
17. For Loops Examples (11:18)
18. Functions (7:47)
19. Functions Examples (9:16)
20. Parameters And Return Values Examples (13:46)
21. Classes And Objects (11:13)
22. Classes Example (13:11)
23. Objects Examples (9:54)
24. Inheritance Examples (17:26)
25. Static Members Example (11:03)
26. Summary And Outro (4:06)
Source code
LEVEL 3 - Data Science and Machine Learning - Intro to tensorflow
00. Course Intro (6:10)
01. Intro To Tensorflow (5:33)
02. Installing Tensorflow (3:52)
03. Intro To Linear Regression (9:26)
04. Linear Regression Model - Creating Dataset (5:49)
05. Linear Regression Model - Building The Model (7:22)
06. Linear Regression Model - Creating A Loss Function (5:57)
07. Linear Regression Model - Training The Model (12:43)
08. Linear Regression Model - Testing The Model (5:22)
09. Summary And Outro (2:55)
Source Files
Machine Learning Theory
00. Course Intro (6:05)
01. Quick Intro To Machine Learning (9:01)
02. Deep Dive Into Machine Learning (6:01)
03. Problems Solved With Machine Learning Part 1 (13:26)
04. Problems Solved With Machine Learning Part 2 (16:25)
05. Types Of Machine Learning (10:15)
06. How Machine Learning Works (11:40)
07. Common Machine Learning Structures (13:51)
08. Steps To Build A Machine Learning Program (16:34)
09. Summary And Outro (2:49)
Source Files
Numpy
00. Course Intro (5:11)
01. Intro To Numpy (6:21)
02. Installing Numpy (3:59)
03. Creating Numpy Arrays (16:55)
04. Creating Numpy Matrices (11:57)
05. Getting And Setting Numpy Elements (16:59)
06. Arithmetic Operations On Numpy Arrays (11:56)
07. Numpy Functions Part 1 (19:13)
08. Numpy Functions Part 2 (12:36)
09. Summary And Outro (3:01)
Source Files
Review Sentiment Analysis
00. Course Intro (6:19)
01. How Machines Interpret Text (15:23)
02. Building the Model Part 1 - Examining Dataset (12:27)
03. Building the Model Part 2 - Formatting Dataset (15:14)
04. Building the Model Part 3 - Building the Model (10:30)
05. Building the Model Part 4 - Training the Model (5:42)
06. Building the Model Part 5 - Testing the Model.mp4 (9:26)
07. Course Summary and Outro (3:29)
Source Files
Learn to Graph Data with Python and Matplotlib
00. Course Intro (5:30)
01. Intro to Pyplot (5:11)
02. Installing Matplotlib (5:51)
03. Basic Line Plot (7:53)
04. Customizing Graphs (10:47)
05. Plotting Multiple Datasets (8:10)
06. Bar Chart (6:26)
07. Pie Chart (9:13)
08. Histogram (10:14)
09. 3D Plotting (6:28)
10. Course Outro (4:09)
Pyplot Code
Complete Beginners Data Analysis with Pandas and Python
00. Panda Course Introduction (5:43)
01. Intro To Pandas (7:55)
02. Installing Pandas (5:28)
03. Creating Pandas Series (20:34)
04. Date Ranges (11:29)
05. Getting Elements From Series (19:21)
06. Getting Properties Of Series (13:04)
07. Modifying Series (19:02)
08. Operations On Series (11:48)
09. Creating Pandas Dataframes (22:57)
10. Getting Elements From Dataframes (25:12)
11. Getting Properties From Dataframes (17:44)
12. Dataframe Modification (36:24)
13. Dataframe Operations (20:09)
14 Dataframe Comparisons And Iteration (15:35)
15. Reading Csvs (12:00)
16. Summary And Outro (4:14)
Source Files
Beginner Data Science and Machine Learning Bootcamp
01 Project Preview (3:29)
03-01 What Is Machine Learning (5:26)
03-02 What Is Unsupervised Learning (8:17)
04-01 Create A Dataset (5:17)
04-02 Vectorize Text (16:27)
04-03 Build A Word Cloud (7:08)
04-04 Reduce Data Dimensionality With Principal Component Analysis (6:08)
04-05 Perform Unsupervised Classification With K-Means Clusters (17:33)
Source Files
Machine Learning Theory for Business
01-01 Hash Table Or Dictionary Visualized With Time And Space Complexity (4:19)
01-02 Types Of Machine Learning (12:09)
01-03 What Is Supervised Learning (9:59)
02 What Machine Learning Can And Cannot Do (11:27)
03a-01 What Is Linear Regression (4:37)
03a-02 What Is Logistic Regression (3:54)
03a-03 Make Decisions With Decision Trees (10:31)
03b-01 What Is Deep Learning (5:44)
03b-02 What Is A Neural Network (7:07)
04 What Are Machine Learning Libraries (11:59)
Machine Learning Fundamentals
00 Course Overview (13:46)
03-01 Probability And Information Theory Overview (5:15)
03-02 Combinatorics For Probability (8:44)
03-03 Law Of Large Numbers (10:38)
03-04 Calculate Center Of Distribution (7:40)
04-01 Uniform Distribution (5:25)
04-02 Gaussian Distribution (3:45)
04-03 Log-Normal Distribution (3:28)
04-04 Exponential Distribution (3:04)
04-05 Laplace Distribution (1:54)
04-06 Binomial Distribution (9:05)
04-07 Multinomial Distribution (3:59)
04-08 Poisson Distribution (4:21)
05 Calculate Error Of Machine Learning Model (8:44)
Source Files
Introduction to Machine Learning and Python Data Science
00. Course Intro (5:30)
01. Intro To Pyplot (5:11)
02. Installing Matplotlib (5:52)
03. Basic Line Plot (7:53)
04. Customizing Graphs (10:47)
05. Plotting Multiple Datasets (8:10)
06. Bar Chart (6:26)
07. Pie Chart (9:13)
08. Histogram (10:14)
09. 3D Plotting (6:28)
10. Course Outro (4:09)
Pyplot Code
Data Engineering and Machine Learning Masterclass
00-00 What Is Python (4:48)
00-01. Intro To Python (4:37)
00b-00 Course Overview (3:26)
03-01 Load And Clean A Public Dataset (8:55)
03-01B What Is One-Hot Encoding (10:02)
03-02 Build X And Y Data With One Hot Encoding (4:57)
03-03 Logistic Regression With One Hot Encoding (2:20)
04-04 Scale And Encode Data With Scikit-Learn (3:47)
04-04 What Is Scaling Data (6:36)
04-05 Build, Train And Test A Machine Learning Model (4:37)
05-01 Compare Decision Tree And Linear Regression Models (6:26)
05-01C What Is The Kbins Discretizer (4:54)
05-02 Bin Data With Kbins Discretizer (3:42)
05-03 Compare Binned Regression Models (3:39)
05-04 Build A Linear Regression Model On Stacked Data (3:20)
05-05A What Is K Means Clustering (11:58)
06-01 Build Univariate Nonlinear Transformatio (1:55)
06-01 What Is Gaussian Probability Distribution- (2:31)
06-01B What Is Poisson Distribution (1:08)
06-02 Build X Y Data With Poisson Distribution In Numpy (3:34)
06-02C What Is Logarithmic Data Transformation (2:34)
06-03 Build A Ridge Regression Model (3:41)
Source Files - Course Overview
Build Machine Learning Models
01-01 Course Overview (3:30)
01-02 Build Models On The Web (5:06)
02-01 What Are Search Algorithms (7:21)
02-02 Depth First Search (9:00)
02-02b Build A Depth First Search Algorithm (8:26)
02-03 What Is Breadth First Search (bfs) (5:08)
02-03b Build A Breadth First Search Algorithm (6:56)
02-04 Depth Limited Search (3:58)
02-05 Iterative Deepening Depth First Search (5:32)
02-06 What Is Uniform Cost Search (6:04)
02-06b Build A Uniform Cost Search Algorithm (8:07)
02-07 Bidirectional Search (4:44)
03-01 What Are Informed Search Algorithms (4:07)
03-02 What Is Greedy Best-first Search (8:16)
03-02b Build A Greedy Best First Search Algorithm (10:43)
03-03 What Is A Search (5:10)
04-01 How Does A Machine Learning Agent Learn (7:37)
04-02 What Is Inductive Learning (4:10)
04-03 Make Decisions With Decision Trees (10:50)
04-04 Performance Of A Machine Learning Algorithm (4:13)
04-05 Handle Noise In Data (5:20)
04-06 Statistical Learning (3:56)
05-01 What Is Logistic Regression (4:26)
05-03 Prepare Data For Logistic Regression (12:19)
05-03a How To Prepare Data (8:52)
05-04 Build A Logistic Regression Model (5:29)
05-04a How To Build A Logistic Regression Model (3:28)
05-04b What Is Optimization (12:10)
05-05 Optimize The Logistic Regression Model (12:44)
05-05a How To Optimize A Logistic Regression Model (12:45)
05-06 Train The Model (10:09)
05-07 Test The Model (2:33)
05-08 Visualize Results (5:38)
06.01 What Is Gradient Boosting (1:54)
06.02 Prepare Data For Gradient Boosted Classification (7:19)
06.03 Build Binary Classes (6:12)
06.04a How To Shape Data For Classification (2:58)
06.04b Shape Data For Classification (7:06)
06.05a How To Build A Boosted Trees Classifier (4:03)
06.05b Build A Boosted Trees Classifier (4:37)
07.01 Build Input Functions (3:55)
07.02 Build A Boosted Trees Regressor (3:02)
07.03 Train And Evaluate The Model (4:07)
Source Files
Beginners Machine Learning Masterclass with Tensorflow JS
00-01b What You-ll Learn (7:12)
00-02 What Is Tensorflow Js (4:28)
00-03 Load Tensorflow Object (4:28)
01b-01 Build A Scatter Plot (8:41)
01b-02 Build A Bar Chart (5:33)
01b-03 Build A Histogram (6:39)
01c-01 Build Sample Data (5:16)
01c-02 Build The Model (11:14)
01c-03 Make A Prediction (7:47)
01d-01 Generate Data (13:38)
01d-02 Visualize Data (16:10)
02-00 What Is Linear Regression (7:52)
02-01 Prepare Training Data (7:10)
02-02 Build The Model (14:05)
02-03 Make A Prediction (3:53)
02b-01 Set Up The Canvas (3:48)
02b-02 Draw A Data Sample (6:20)
02b-03 Create Loss And Prediction Functions (6:00)
02b-04 Collect User Input For Data (8:50)
02b-05 Visualize Linear Regression With Dynamic Data (6:46)
03-01 Set Up The Canvas (11:00)
03-02 Visualize Linear Regression With Dynamic Data (16:33)
04-01 Generate Samples (6:21)
04-02 Generate A Prediction Equation With Weights (6:54)
04-03 Train The Model (5:26)
04-04 Visualize Predictions (18:01)
04-05 Visualize Prediction Error (10:00)
05-01 Load Models Into Html (5:51)
05-02 Train Model On Images (13:13)
05-03 Make A Prediction (6:58)
Source Files
Beginners Guide to Neural Networks in Tensorflow JS
00 What You-ll Learn (7:44)
04-01 Build A Perceptron (13:26)
04-02 Build A Sigmoid Function (8:01)
04-03 Build A Sigmoid Perceptron (7:35)
04-04 Build A Relu Activation Function (7:12)
04-05 Build A Leaky Relu Activation Function (6:10)
05-01 Build Neural Network Layers (9:57)
05-02 Train And Test The Neural Network (11:24)
06-01 Build A Dataset-1 (8:26)
06-02 Build A Neural Network-2 (5:35)
06-03 Train The Neural Network-3 (10:05)
06-04 Make A Prediction With The Neural Network-4 (8:43)
07-00 What Is Cross Validation-1 (8:24)
07-01 Load A Model Into Html-2 (4:57)
07-02 Use A Neural Network In Your Website-3 (8:49)
07-03 Show Neural Network Results On Website-4 (5:34)
08-01 Build A Dataset For Xor (6:32)
08-02 Build A Neural Network For Xor (5:19)
08-03 Train And Test The Neural Network (11:06)
09-01 Load An Rnn Into Your Website (5:37)
09-02 Set Up The Canvas (7:06)
09-03 Draw With A Neural Network (8:50)
10-01 Load An Image For Object Detection (6:13)
10-02 Load A Neural Network For Object Detection (6:15)
10-03 Outline Objects In The Image (12:17)
11-01 Build A Deep Neural Network With Gradient Descent From Scratch-1 (9:21)
11-03 Build A Deep Neural Network With Gradient Descent With Tensorflow Js-2 (11:24)
11-04 Build A Deep Neural Network With Backpropagation-3 (7:03)
11-05 Build The Backpropagation-4 (16:56)
12-01 Reduce Neural Network Error-1 (17:12)
12-02 Build A Gradient Descent Algorithm-2 (8:48)
13 Train The Deep Neural Network With Gradient Descent (15:24)
Tensorflow JS Source Files
Advanced Machine Learning with TensorFlow.js
02-01 Load The Model With Text (4:18)
02-02 View Model Results Of Text Toxicity (6:40)
02-03 Clean Up Prediction Results (6:18)
03-01 Set Up The Speed Recognition Model (6:00)
03-02 Set Up The Canvas (3:26)
03-03 Classify Words Through Microphone (6:55)
03-04 Draw From User Commands (7:35)
03-05 Optimize The Drawing (5:53)
04-01 Tidy Tensors (6:26)
04-02 Keep Tensors (3:10)
04-03 Dispose Tensors (2:41)
04-04 Build A Memory Leak Example (4:35)
05-01 Load Json Data (7:34)
05-02 Convert Json Data To Tensor (9:08)
05-03 Visualize Dataset With Tf-Vis (5:38)
05-04 Build And Train Model (10:22)
05-05 Visualize Model-s Training Epochs (9:12)
05-06 Make A Prediction (13:49)
05-07 Visualize Prediction (9:09)
06-01 Load Dataset From Json File (6:48)
06-02 Visualize Dataset-s Features (9:26)
06-03 Build A Multi Layer Model (7:43)
06-04 Extract Inputs And Outputs (7:10)
06-05 Normalize Data (4:47)
06-06 Train The Model (6:01)
06-07 Evaluate Model Performance (6:12)
07-00 What Is Logistic Regression (4:32)
07-00B Calculate Logistic Regression Accuracy (5:20)
07-01 Build A Logistic Regression Model (7:08)
07-02 Train The Logistic Regression Model (15:20)
07-03 Visualize Logistic Regression Results (12:52)
07-04 Visualize Original Data (12:13)
07-05 Visualize Model Error (7:37)
08-00 What Is Fast Fourier Transform (2:42)
08-01 Build And Visualize A Dataset (10:48)
08-02 Visualize Frequencies With Fast Fourier Transform (11:53)
08-03 Visualize Inverse Fast Fourier Transform (5:44)
09-00 What Is Principal Component Analysis (6:13)
09-01 Build Principal Component Analysis (6:24)
09-02 Calculate Variance Of Data And Principal Component Analysis (9:28)
09-03 Visualize Data Slices (12:01)
09-04 Visualize Principal Component Analysis Results (3:03)
Source Files
Advanced Neural Networks with TensorFlow.js
02 What Is Tensorflow Js (4:29)
02-00 What Is One Hot Encoding (6:53)
02-01 Build Training Data (7:34)
02-02 Build The Neural Network (6:48)
02-03 Train The Neural Network (9:33)
02-04 Make A Prediction (10:11)
03 Load Tensorflow Object (5:08)
03-01 Build Training Data To Represent Images (12:15)
03-02 Build The Convolutional Neural Network (10:39)
03-03 Train The Convolutional Neural Network (9:06)
03-04 Make A Prediction Of Number Of Lines (15:05)
04-00 What Is A Recurrent Neural Network (6:38)
04-01 Generate Sequence And Label (6:25)
04-02 Generate Dataset (6:02)
04-03 Build The Lstm Model (4:55)
04-04 Train The Model (11:25)
06-01 Process Iris Data (7:37)
06-02 Convert Data To Tensors (8:45)
06-03 Separate Training And Testing Data (8:54)
06-04 Create Training And Testing Datasets (4:42)
06-05 Build The Model (9:29)
06-06 Train The Model (4:11)
06-07 Make A Prediction (8:45)
07-01 Load Model And Dataset (5:57)
07-02 Get User Input For Sentiment Analysis (10:59)
07-03 Make A Prediction (7:11)
08-00 What Is A Convolutional Neural Network (19:29)
08-01 Set Up Canvas To Load Image Data (10:36)
08-02 Load Mnist Dataset (6:47)
08-03 Separate Training And Testing Data (5:40)
08-04 Build The Model (6:48)
08-04A What Are The Network-s Layers (14:14)
08-05 Train The Model (11:27)
08-06 Create Training Batches (6:14)
08-07 Create Testing Batches (11:31)
08-08 Fit Neural Network Through Data (8:54)
Source Files
Python and Android Tensor Flow Lite - Machine Learning for App Development
00-00 Course Overview (3:12)
00-01 What You-ll Need (4:29)
04b Project Preview (2:17)
05-01 Build A Linear Regression Model With Python (15:06)
05-02 Convert Python Model To Tensorflow Lite (5:38)
06-03 Build A New Android Studio App (7:39)
06-04 Build App Layout (10:18)
07-05 Load Machine Learning Model (4:53)
07-06 Use Machine Learning Model (5:18)
07-07 Connect App Layout To Model (6:08)
08-00 Project Preview (1:49)
08-00 What Is Logistic Regression (4:32)
09-01 Load And Process Data For Logistic Regression With Scikit Learn (9:14)
09-02 Build A Logistic Regression Model With Python (8:01)
09-03 Convert Logistic Regression Model To Tensorflow Lite (2:38)
10-04 Build A New Android Studio App With Tf Lite Model (5:48)
10-05 Build App Layout For Logistic Regression (9:26)
11-06 Load Logistic Regression Model In Android Studio (5:01)
11-07 Use Logistic Regression Model In Android (8:46)
11-08 Enable App User Interaction With Machine Learning Model (9:54)
Source files
CoreML SwiftUI Masterclass - Machine Learning App Development
00-00 Course Overview (6:54)
00-01 What You-ll Need (5:56)
00-02 What Is Coreml (6:43)
01-00A What Is Sentiment Analysis (4:39)
01-00B Natural Language Framework (4:32)
01-01 Build A New Swiftui App For Sentiment Analysis (8:59)
01-01 Train A Model With CreateML (12:13)
01-02 Perform Sentiment Analysis In SwiftUI (7:38)
01-02 Test The Model With CoreML In An App (14:17)
01-03 Change Color Depending On Sentiment (4:56)
01-03 Display Prediction Accuracy (6:41)
04-01 Load A CoreML Model Into A New Xcode Project (11:00)
04-02 Add Images For Classification (6:31)
04-03 Enable User To Loop Through Image (5:40)
04-04 Import CoreML Model Into The View (5:28)
04-05 Resize Image For Model (6:26)
04-05A Resizing Image Overview (7:44)
04-06 Convert Image To Buffer For Model (8:55)
04-06A Image To Buffer Overview (6:55)
04-07 Test The Model On Image Classification (14:31)
05-00 Tip - How To Unhide Library Folder (1:22)
05-01 Build A New Xcode Project To Compile Model (4:44)
05-02 Build A Playground With Object Detection Model (4:28)
05-03 Instantiate A Model 05-Object (6:12)
05-04 Build An Image Analysis Request (7:23)
05-05 Resize Image For Model (9:36)
05-06 Convert Image To Buffer For Model (9:47)
05-07 Test Object Detection On Image (4:53)
Source Files
LEVEL 4 - Image Generation
01 Generate Image With Dall-E Api Python (3:31)
02 Edit Image With Dall-E Api Python (4:35)
03 Create Image Variation With Dall-E Api Python (2:20)
Source Files
Image Generation with Diffusion Neural Network - Reshape image data for NN
01 Load flower image dataset in Colab (4:34)
02 Reshape image data with Python (4:48)
03 Preprocess image data with Python (3:08)
Source Files
02 Calculate Kernel Inception Distance
01 Calculate Kernel Inception Distance in Python (6:34)
02 Update Kernel State with Keras (8:49)
Source Files
03 Build downsampling and upsampling with Keras
01 Build residual block with Python (3:24)
02 Build down and up blocks with Keras (3:29)
Source files
04 Build a CNN with Keras
01 Build sinusoidal embedding with TensorFlow (3:35)
02 Build a CNN for image generation (5:30)
CNN Source Files
05 Build a diffusion model for image generation
01 Build a diffusion model for image generation (4:02)
02 Denoise images in Python (2:09)
03 Build a diffusion schedule with TensorFlow (3:00)
04 Build reverse diffusion algorithm (4:45)
Diffusion Source Files
06 Generate images with diffusion
01 Generate images with diffusion (3:07)
02 Build training step for model (7:49)
03 Build Keras model test step (3:58)
06 Source Files
07 Train and visualize image generation model
01 Run model training for diffusion image generation_1 (5:51)
02 Visualize image generation results (4:59)
Source files
Image Generation with Open Source Stable Diffusion Machine Learning Python
01 What are diffusion models (2:40)
02 What is the diffusers library (8:36)
03 What is stable diffusion (3:09)
Source files
01 Image generation with Stable Diffusion Python
01 Load Stable Diffusion model in Colab (7:54)
Source Files
02 Image to image translation with Stable Diffusion Python
01 Load pretrained image translation model (3:05)
02 Preprocess original image for translation with Python (4:31)
03 Generate image variant with prompt (3:50)
Image translation files
03 Inpainting with Stable Diffusion Python
01 Load images for inpainting (2:43)
02 Generate inpainted image with prompt (4:48)
Inpainting files
Computer Vision and Deep Learning with OpenCV and Python - Build 15 Projects
01-01 Course Overview - Opencv (4:51)
01-02 What You-ll Need (2:38)
03-01 Detect Edges In An Image (8:19)
03-02 Detect Contours In An Image (11:23)
03-03 Detect Corners In An Image (9:37)
04 Restore A Damaged Image (18:57)
05-01 Detect Objects In An Image With Masking (15:39)
05-02 Detect Faces In Images (11:31)
05-03 Extract Foreground In An Image (18:30)
05-04 Find Object In Image With Template Matching (12:27)
06-01 What Is Machine Learning (5:26)
06-02 What Is Deep Learning (7:42)
06-03 What Is A Neural Network (8:47)
06-04 What Is Ml-Agents (5:16)
07-01 Extract Text From An Image With Tesseract (13:31)
07-02 Improve Accuracy With Thresholding (8:10)
07-03 Change Perspective Of An Image With Foreign Text (15:30)
07-04 Extract Foreign Language Text From An Image (8:03)
08-01 Generate Data (7:09)
08-02 Build An Artificial Neural Network (9:16)
08-03 Visualize Model Results (14:30)
09-01 Load YOLO Dnn Model (3:18)
09-02 Build A Neural Network With Opencv (7:44)
09-03 Print Out Detected Objects (6:44)
09-04 Outline Objects In The Original Image (21:57)
10-01 Outline Objects In A Video (10:55)
10-02 Draw Contours On Video (16:30)
10-03 Save New Frames As A Video (5:04)
11-01 Load A Video From Drive (7:39)
11-02 Detect Faces In Video (10:32)
11-03 Detect Eyes In Video (6:40)
11-04 Save New Frames As A Video (7:40)
12-01 Track Color In A Video (20:06)
12-02 Save New Frames As A Video (7:14)
13-01 Load A Driving Dash Cam Video (4:05)
13-02 Process Each Video Frame (14:54)
13-03 Outline Lanes Detected (12:21)
13-04 Save New Frames As A Video (13:52)
14-01 Load A Video From Drive (5:28)
14-02 Detect Objects In A Video With Contours (10:05)
14-03 Detect When Motion Begins And Ends (15:17)
14-04 Record Each Time Motion Begins (16:36)
15 Detect Emotion In A Video (12:11)
16-01 Load Images From The Web Into Colab (3:00)
16-02 Get Facial Landmarks From Image (11:55)
16-03 Build A Matrix From Landmark Points (10:08)
16-04 Draw A Mask Over Facial Landmarks (7:07)
16-05 Build A Warped Mask (4:09)
16-06 Combine Face Masks (8:15)
Source Files
Creative Machine Learning - Draw and Paint with 3 Neural Network Projects
00 Project Preview (1:47)
02 Project 2 Preview (1:06)
03 Project 3 Overview (0:47)
04 What You-ll Need (2:43)
Source Files
02 Collect and Process Data
01 Load Drawings Dataset (10:03)
02 Label Data (12:17)
03 Build A Training Dataset (8:30)
04 Visualize Dataset (6:20)
05 Batch And Shuffle Data (4:39)
Source Files
03 Build a Generative Neural Network
01 Build A Generator (13:46)
02 Generate Noise (5:41)
Source Files
03a Generative Neural Network Fundamentals
01 What Is A Generative Neural Network (7:26)
02 What Is A Convolutional Neural Network (7:04)
03 How To Build A Convolutional Neural Network (14:04)
04 How To Build A Dense Layer (2:42)
05 How To Build A Batch Normalization Layer (1:52)
06 Leaky Relu Activation Function (6:04)
07 Transposed Convolution Layer (5:17)
08 Hyperbolic Tangent (Tanh) Activation Function (2:59)
Source Files
04 Build a Discriminator Neural Network
00 How Do You Build A Discriminator (10:19)
01 Build A Discriminator (10:53)
Source Files
05 Evaluate the Model's Performance
00 Performance Of A Machine Learning Algorithm (4:14)
01 Calculate Loss (7:11)
02 Assign Optimizers (3:02)
02A What Is The Adam Optimizer (6:55)
Source Files
06 Train the Model to Draw
01 Build A Training Step (11:03)
02 Train The Model (6:54)
03 Visualize Training (14:35)
Source Files
07 Test the Model's Drawing Ability
01 Test The Model (9:22)
Source Files
08 Build an Image Style Transfer Project
00 Style Transfer Project Overview (5:36)
01 Load The Model (4:57)
02 Load Images (6:53)
03 Reformat Image For Machine Learning (7:03)
04 Load Original And Style Images (6:27)
05 Display Processed Images (10:58)
06 Extract Image Features (6:59)
07 Calculate The Style Representation (6:01)
08 Optimize The Model (5:27)
09 Use Machine Learning To Transfer Image Style (13:54)
Source Files
09 Build an Image Approximation Project
01 Load And Process Image (7:14)
02 Build A Training Dataset (6:49)
03 Visualize Training Dataset (5:36)
04 Build A Testing Dataset (4:04)
05 Build A Neural Network (7:25)
06 Train The Neural Network (4:40)
07 Visualize Image Approximation Results (5:14)
Source Files
LEVEL 5 - Pass the Image Generation Coding Interview
01 What is discriminative modeling (1:00)
02 What is generative modeling (1:10)
Interview Source Files
02 Diffusion Deep Learning Interview Questions
01 What are diffusion models (1:08)
02 How diffusion works in deep learning (2:17)
03 Forward and backward diffusion in ML (1:52)
04 What is a U-Net ML model (1:30)
02b Stable Diffusion Interview Questions
01 Steps of latent reverse diffusion in Stable Diffusion_1 (1:44)
02 What is latent space (3:14)
03 What is the manifold hypothesis in ML (1:39)
03 Dimensionality Reduction Data Science Interview Questions
01 What is dimensionality reduction_1 (1:48)
02 What is principal component analysis (1:16)
04 Autoencoder Machine Learning Interview Questions
01 What are Autoencoders (1:38)
02 What are encoders and decoders in ML (2:13)
03 How do Autoencoders work (2:30)
04 What are Variational Autoencoders (2:59)
05 What is a Vector Quantized Variational Autoencoder (1:45)
05 GAN Neural Network Interview Questions
01 What is the structure of a Generative Adversarial Network_1 (4:17)
02 What are discriminators and generators (3:58)
03 What is zero-shot learning (2:55)
06 Scoring Interview Questions
01 What is Inception Score_1 (3:32)
02 What is Frechet Inception Distance (3:58)
03 How FID Works in ML (1:48)
04 What is Kernel Inception Distance (1:02)
07 Signal Processing Interview Questions
01 What is Time-Series data_1 (1:39)
02 What is signal data (3:40)
03 Continuous signals vs discrete signals (3:12)
04 What is Nyquist rate (1:42)
08 Fourier Analysis Interview Questions
01 What are periodic signals (1:51)
02 What is Fourier Transform (6:58)
Essential Algorithms and Data Structures
00-01. Kotlin Course Introduction (7:04)
00-02 Fizzbuzz Kotlin (5:26)
01-01 Reverse Words In A String Kotlin (3:53)
01-02 Rotate Array Kotlin (7:31)
01-03 Kth Largest Element In An Array Kotlin (4:26)
02-01 Set Matrix Zeros Kotlin (12:20)
02-02 Spiral Matrix Kotlin (21:56)
03 Queue With A Linkedlist Kotlin (10:43)
04-00 Build A Binary Tree (15:46)
04-01 Delete Tree Node Kotlin (17:20)
05-01 Delete Tree Node Kotlin (17:20)
05-02 Selection Sort Algorithm Kotlin (6:01)
05-03 Insertion Sort Kotlin (6:15)
05-04 Merge Sort Algorithm Kotlin (15:10)
06 Build A Graph Kotlin (7:28)
07-01 Coin Change Kotlin (8:02)
07-02 Maximum Sum Subarray Kotlin (7:06)
07-03 Edit Distance Kotlin (9:37)
08-01 Single Number Kotlin (7:29)
08-02 Number Of 1 Bits Kotlin (7:24)
08-03 Bitwise And Of A Range Kotlin (7:23)
09-01 Permutations Kotlin (16:12)
09-02 Combinations Kotlin (9:28)
09-03 Letter Combinations Of A Phone Number Kotlin (10:31)
10-01 Reverse Integer Kotlin (11:52)
10-02 Palindrome Number Kotlin (9:53)
10-03 Excel Sheet Column Number Kotlin (5:23)
Source Code
Python Interview Questions
01 Introduction Python (6:17)
02 Fizzbuzz Python (5:57)
Source Code
01 Time Complexity
00 Types Of Time Complexity Python (21:51)
01 Types Of Better Time Complexity Python (14:51)
Source Code
02 String and Array Interview Questions
01 Reverse Words In A String Python (2:44)
02 Rotate Array Python (8:56)
03 Kth Smallest Element In An Array Python (11:53)
Source Code
03 Matrix Interview Questions
01 Spiral Matrix Python (13:26)
02 Number Of Islands Python (18:54)
Source Code
04 Linked List Interview Questions
01 Implement A Stack Using An Array Python (9:45)
02 Add Two Numbers As Reversed Linked Lists Python (16:10)
03 Reverse A Linked List Python (16:10)
Source Code
05 Binary Tree Interview Questions
01 Inorder Traversal Python (11:08)
02 Preorder Traversal Python (8:48)
03 Postorder Traversal Python (7:05)
04 Binary Tree Maximum Path Sum Python (8:43)
Source Code
06 Graph Interview Questions
01 Find Strongly Connected Components Python (10:29)
Source Code
07 Sorting Interview Questions
01 Bubble Sort Algorithm Python (9:11)
02 Selection Sort Algorithm Python (5:56)
03 Insertion Sort Algorithm Python (4:29)
04 Quicksort Algorithm Python (4:29)
05 Merge Sort Algorithm Python (7:25)
06 Time Complexity Of Different Sorting Algorithms (2:55)
Source Code
08 Dynamic Programming Interview Questions
01 Coin Change Python (7:21)
02 Edit Distance Python (11:33)
03 Distinct Subsequences Python (6:59)
04 Maximum Sum Subarray Python (4:56)
Source Code
09 Bit Manipulation Interview Questions
01 Bitwise And Shift Operators (7:02)
02 Single Number Python (4:32)
03 Number Of 1 Bits Python (4:31)
04 Sum Of Two Integers Python (5:14)
05 Bitwise And Of A Range Python (5:36)
Source Code
10 Permutations and Combinations
01 Permutations Python (10:08)
02 Distinct Permutations Of A String Python (7:49)
03 Letter Combinations Of A Phone Number Python (11:54)
Source Code
11 Math Interview Questions
01 Reverse Integer Python (9:53)
02 Palindrome Number Python (9:49)
03 Excel Sheet Column Number Python (6:04)
Source Code
Machine Learning Interview Questions
00. Course Intro (5:09)
01-00. Intro (1:54)
01-01. What is Machine Learning (17:47)
01-02. Types Of Machine Learning (10:48)
01-03. Building A Machine Learning Model (17:02)
02-00. Intro (2:44)
02-01. How To Choose An Algorithm (16:42)
02-02. Common Machine Learning Algorithms Part 1 (15:58)
02-03. Common Machine Learning Algorithms Part 2 (22:52)
02-04. Common Machine Learning Algorithms Part 3 (13:03)
02-05. Comparison Interview Questions (16:20)
03-00. Intro (2:08)
03-01. Data Related Errors (16:55)
03-02. Model Related Errors (11:34)
03-03. Results Testing Techniques (11:18)
04-00. Intro (2:14)
04-01. Missing_Corrupted Data (5:08)
04-02. Selecting Important Variables (3:18)
04-03. Fixing Multicollinearity- (3:56)
04-04. Kernel Tick (3:21)
04-05. Slow Machine_Limited Memory (4:59)
04-06. Classification and Random Sampling (3:38)
04-07. Low Training Error with High Validation Error (4:40)
04-08. Cross Validation on Time Series Data (3:38)
04-09. Amazon Recommendation System (5:26)
05. Course Summary and Outro (3:12)
Source Files
Essential JavaScript Software Developer Interview Guide
00 Course Introduction (10:06)
00-01. Fizzbuzz (6:33)
01-01 Reverse Words In A String (4:31)
01-02 Rotate Array (7:44)
01-03 Isomorphic Strings (6:49)
01-04 Kth Largest Element In An Array (10:11)
02-01 Set Matrix Zero (8:15)
02-02 Spiral Matrix (10:46)
02-03 Number Of Islands (14:31)
03-01 Implement A Stack Using An Array (11:58)
03-02 Add Two Numbers As Reversed Linked Lists (14:07)
03-03 Reverse A Linked List (7:56)
04-01 Inorder Traversal (17:35)
04-02 Preorder Traversal (12:26)
04-03 Postorder Traversal (9:38)
05-01 Clone An Undirected Graph - Build A Graph (11:15)
05-02 Clone An Undirected Graph - Build A Queue (3:44)
05-03 Clone An Undirected Graph - Breadth First Traversal (7:31)
05-04 Clone An Undirected Graph - Depth First Traversal (5:58)
06-01 Types Of Time Complexity (13:27)
06-02 Types Of Better Time Complexity (18:15)
06-03 Bubble Sort Algorithm (6:41)
06-04 Selection Sort Algorithm (6:15)
06-05 Insertion Sort Algorithm (6:51)
06-06 Quicksort Algorithm (9:18)
06-07 Merge Sort Algorithm (8:43)
06-08 Time Complexity Of Different Sorting Algorithms (2:55)
07-01 Coin Change (8:41)
07-02 Edit Distance (15:15)
07-03 Distinct Subsequences (7:26)
08-01 Bitwise And Shift Operators (7:19)
08-02 Single Number (4:51)
08-03 Number Of 1 Bits (13:26)
08-04A Sum Of Two Integers (9:26)
08-04B Maximum Sum Subarray (6:52)
08-05 Reverse Bits (5:54)
08-06 Bitwise And Of A Range (4:28)
09-01 Permutations (9:37)
09-02 Distinct Permutations Of A String (9:15)
09-03 Letter Combinations Of A Phone Number (14:36)
09-04 Factor Combinations (9:08)
10-01 Reverse Integer (10:07)
10-02 Palindrome Number (10:10)
10-03 Excel Sheet Column Number (6:21)
Source Code
Math Interview Questions with JavaScript
00 Introduction (4:08)
01-01 Happy Number (15:33)
01-02 Trailing Zeros In Factorial (11:10)
01-03 Count Primes (6:57)
01-04 Sorted Permutation Rank With Repeats (10:23)
01-05 Sqrt(x) (18:19)
01-06 Greatest Common Divisor (7:27)
02-01 Integer To Roman (17:16)
02-02 Roman To Integer (12:04)
02-03 Rearrange Array (9:11)
03-01 N-th Tribonacci Number (14:00)
03-02 Maximum Product Of Three Numbers (10:58)
03-03 Grid Unique Paths (17:22)
03-04 City Tour (5:51)
04-01 Next Greater Element (15:28)
04-02 Ugly Number (9:02)
04-03 Power Of Two Integers (11:28)
04-04 Prime Sum (12:23)
04-05 Sum Of Bit Differences Among All Pairs (20:53)
Source Files
02 Preprocess original image for translation with Python
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