Канал: Dr. Data Science

Statistical Machine Learning,Week 14: Recurrent Neural Network (LSTM & Backpropagation through time)

Statistical Machine Learning,Week 14: Recurrent Neural Network (LSTM & Backpropagation through time)

Generative AI and LLMs for Absolute Beginners (tokenization simplified!!!)

Generative AI and LLMs for Absolute Beginners (tokenization simplified!!!)

Apple M2 Max vs NVIDIA T4 GPU: Which is Best for Deep Learning?

Apple M2 Max vs NVIDIA T4 GPU: Which is Best for Deep Learning?

Statistical Machine Learning, Week 12: Hyperparameter Optimization, KerasTuner, Hyperband Example

Statistical Machine Learning, Week 12: Hyperparameter Optimization, KerasTuner, Hyperband Example

Statistical Machine Learning, Week 11: Implementing Convolutional Neural Networks (CNNs) using Keras

Statistical Machine Learning, Week 11: Implementing Convolutional Neural Networks (CNNs) using Keras

Statistical Machine Learning,Week 10: Neural Networks in Action, Functional API, Activation Function

Statistical Machine Learning,Week 10: Neural Networks in Action, Functional API, Activation Function

Statistical Machine Learning, Week 9: Implementing Feedforward Neural Networks with Keras

Statistical Machine Learning, Week 9: Implementing Feedforward Neural Networks with Keras

Build an Image Classifier FROM SCRATCH in PYTORCH

Build an Image Classifier FROM SCRATCH in PYTORCH

Statistical Machine Learning, Week 8: Mathematical Building Blocks of Neural Networks w/ XOR Example

Statistical Machine Learning, Week 8: Mathematical Building Blocks of Neural Networks w/ XOR Example

Statistical Machine Learning, Week 7, Part a: Evaluating Classification Models (Decision Boundary)

Statistical Machine Learning, Week 7, Part a: Evaluating Classification Models (Decision Boundary)

Statistical Machine Learning, Week 7, Part b: Evaluating Classification Models (ROC/PR curves)

Statistical Machine Learning, Week 7, Part b: Evaluating Classification Models (ROC/PR curves)

Statistical Machine Learning, Week 6, Part b: Loss Function Derivation for Classification Problems

Statistical Machine Learning, Week 6, Part b: Loss Function Derivation for Classification Problems

Statistical Machine Learning, Week 6, Part a: Logistic Regression for Classification

Statistical Machine Learning, Week 6, Part a: Logistic Regression for Classification

Statistical Machine Learning, Week 5: Loss Function Optimization using Gradient Descent

Statistical Machine Learning, Week 5: Loss Function Optimization using Gradient Descent

Statistical Machine Learning, Week 4: Loss Function Derivation for Regression Problems (NLL loss)

Statistical Machine Learning, Week 4: Loss Function Derivation for Regression Problems (NLL loss)

Statistical Machine Learning, Week 3: Tensor, Gradient, and Automatic Differentiation (GradientTape)

Statistical Machine Learning, Week 3: Tensor, Gradient, and Automatic Differentiation (GradientTape)

Statistical Machine Learning, Week2: Object-Oriented Programming for Machine Learning

Statistical Machine Learning, Week2: Object-Oriented Programming for Machine Learning

Statistical Machine Learning, Week 1: Mathematical Building Blocks of Machine Learning

Statistical Machine Learning, Week 1: Mathematical Building Blocks of Machine Learning

Install PyTorch on Apple Silicon Macs (M1, M2, M3) and Check for MPS Availability in 2024

Install PyTorch on Apple Silicon Macs (M1, M2, M3) and Check for MPS Availability in 2024

Install TensorFlow/Keras on Apple Silicon Macs (M1, M2, M3) with 4 Terminal Commands in 2024

Install TensorFlow/Keras on Apple Silicon Macs (M1, M2, M3) with 4 Terminal Commands in 2024

NVIDIA System Management Interface (nvidia-smi) to monitor NVIDIA GPU devices

NVIDIA System Management Interface (nvidia-smi) to monitor NVIDIA GPU devices

Full Python Programming Course for Data Science & Machine Learning w/ Jupyter Notebooks & Exercises

Full Python Programming Course for Data Science & Machine Learning w/ Jupyter Notebooks & Exercises

Programming for Data Science, Lec 15: Neural Networks, TensorFlow AutoDiff, and Keras

Programming for Data Science, Lec 15: Neural Networks, TensorFlow AutoDiff, and Keras

Programming for Data Science,Lec 14: Classification in Machine Learning using scikit-learn (sklearn)

Programming for Data Science,Lec 14: Classification in Machine Learning using scikit-learn (sklearn)

Programming for Data Science, Lec 13: Regression in Machine Learning using scikit-learn (sklearn)

Programming for Data Science, Lec 13: Regression in Machine Learning using scikit-learn (sklearn)

Programming for Data Science, Lec 12: Machine Learning in Python using scikit-learn (sklearn)

Programming for Data Science, Lec 12: Machine Learning in Python using scikit-learn (sklearn)

Programming for Data Science, Lec 11: Pandas DataFrame (data import, access, filtering, plotting)

Programming for Data Science, Lec 11: Pandas DataFrame (data import, access, filtering, plotting)

Programming for Data Science, Lec 10: Data Visualization and Plotting in Python Using Matplotlib

Programming for Data Science, Lec 10: Data Visualization and Plotting in Python Using Matplotlib

Programming for Data Science, Lec 9: Modules and Packages in Python (math, time, numpy, sklearn)

Programming for Data Science, Lec 9: Modules and Packages in Python (math, time, numpy, sklearn)

Programming for Data Science, Lec 8: Object-Oriented Programming, Inheritance and super() function

Programming for Data Science, Lec 8: Object-Oriented Programming, Inheritance and super() function

Programming for Data Science, Lec 7: Object-Oriented Programming, Class and Constructor in Python

Programming for Data Science, Lec 7: Object-Oriented Programming, Class and Constructor in Python

Programming for Data Science, Lec 6: Iteration (for/while loops, list comprehension, break/continue)

Programming for Data Science, Lec 6: Iteration (for/while loops, list comprehension, break/continue)

Programming for Data Science: Lec 5, Branching Statements in Python ('if', 'elif', and 'else')

Programming for Data Science: Lec 5, Branching Statements in Python ('if', 'elif', and 'else')

Programming for Data Science: Lec 4, Built-in/Custom Functions in Python, and Lambda Functions

Programming for Data Science: Lec 4, Built-in/Custom Functions in Python, and Lambda Functions

Programming for Data Science, Lec 3: NumPy ndarray, np.arange, np.random, np.linalg, np.concatenate

Programming for Data Science, Lec 3: NumPy ndarray, np.arange, np.random, np.linalg, np.concatenate

Programming for Data Science, Lec 2: Python Data Structures, List, Tuple, Set, and Dictionary

Programming for Data Science, Lec 2: Python Data Structures, List, Tuple, Set, and Dictionary

Programming for Data Science, Lec 1: Python Basics (Arithmetic/Comparison/Logical Operators)

Programming for Data Science, Lec 1: Python Basics (Arithmetic/Comparison/Logical Operators)

Understand & Implement Normalized Mutual Information (NMI) in Python (normalized_mutual_info_score)

Understand & Implement Normalized Mutual Information (NMI) in Python (normalized_mutual_info_score)

Automatic Differentiation for ABSOLUTE beginners: "with tf.GradientTape() as tape"

Automatic Differentiation for ABSOLUTE beginners: "with tf.GradientTape() as tape"

Three Easy Steps to Understand Conformal Prediction (CP), Conformity Score, Python Implementation

Three Easy Steps to Understand Conformal Prediction (CP), Conformity Score, Python Implementation

Functional API and hidden layers with different activation functions in Keras Deep Learning

Functional API and hidden layers with different activation functions in Keras Deep Learning

Sequential Model, Dense Layer, and Model Compile in Keras Deep Learning

Sequential Model, Dense Layer, and Model Compile in Keras Deep Learning

Understanding Vector Norms in Machine Learning (L1 and L2 norms, unit balls, and NumPy)

Understanding Vector Norms in Machine Learning (L1 and L2 norms, unit balls, and NumPy)

A Gentle Introduction To Math Behind Neural Networks and Deep Learning (nested composite function)

A Gentle Introduction To Math Behind Neural Networks and Deep Learning (nested composite function)

Why the area under the ROC curve for a random classifier is 0.5? (Mathematical Proof/Python Example)

Why the area under the ROC curve for a random classifier is 0.5? (Mathematical Proof/Python Example)

Easy Steps to Understand Maximum Likelihood Estimation (MLE) for Deriving Mean Squared Error Loss

Easy Steps to Understand Maximum Likelihood Estimation (MLE) for Deriving Mean Squared Error Loss

Using sklearn's GridSearchCV with Pipeline for Hyperparameter Tuning in Machine Learning

Using sklearn's GridSearchCV with Pipeline for Hyperparameter Tuning in Machine Learning

Dive into Deep Learning Lec7: Regularization in PyTorch from Scratch (Custom Loss Function Autograd)

Dive into Deep Learning Lec7: Regularization in PyTorch from Scratch (Custom Loss Function Autograd)

Dive into Deep Learning – Lec 6: Basics of Object-Oriented Programming in PyTorch (torch.nn.Module)

Dive into Deep Learning – Lec 6: Basics of Object-Oriented Programming in PyTorch (torch.nn.Module)

Logistic (Sigmoid) function in Statistical and Machine Learning (torch.nn.Sigmoid, tf.math.sigmoid)

Logistic (Sigmoid) function in Statistical and Machine Learning (torch.nn.Sigmoid, tf.math.sigmoid)

Regularization in Machine and Deep Learning (Ridge, Lasso, and Layer weight regularizers)

Regularization in Machine and Deep Learning (Ridge, Lasso, and Layer weight regularizers)

Cross-Validation in Machine Learning: Do It Right from Scratch in Python (sklearn KFold)

Cross-Validation in Machine Learning: Do It Right from Scratch in Python (sklearn KFold)

Machine Learning/Data Science Exam/Interview Preparation With Practice Tests, Examples, and Solution

Machine Learning/Data Science Exam/Interview Preparation With Practice Tests, Examples, and Solution

Dive Into Deep Learning - Lecture 5: Parameter Access, Initialization, and storage in PyTorch

Dive Into Deep Learning - Lecture 5: Parameter Access, Initialization, and storage in PyTorch

Easiest Way to Understand Gradient Descent Step by Step (downhill to a minimum with derivatives)

Easiest Way to Understand Gradient Descent Step by Step (downhill to a minimum with derivatives)

Dive into Deep Learning - Lecture 4: Logistic/Softmax regression and Cross Entropy Loss with PyTorch

Dive into Deep Learning - Lecture 4: Logistic/Softmax regression and Cross Entropy Loss with PyTorch

Dive Into Deep Learning - Lecture 3: Build a Simple Neural Network from Scratch with PyTorch

Dive Into Deep Learning - Lecture 3: Build a Simple Neural Network from Scratch with PyTorch

Dive Into Deep Learning, Lecture 2: PyTorch Automatic Differentiation (torch.autograd and backward)

Dive Into Deep Learning, Lecture 2: PyTorch Automatic Differentiation (torch.autograd and backward)

Dive into Deep Learning - Lecture 1: PyTorch Tensor Basics, Operations, Functions, and Broadcasting

Dive into Deep Learning - Lecture 1: PyTorch Tensor Basics, Operations, Functions, and Broadcasting

The Absolute Beginner’s Guide to Google Colaboratory or Colab by Dr. Data Science

The Absolute Beginner’s Guide to Google Colaboratory or Colab by Dr. Data Science

How to Evaluate the Performance of Clustering Algorithms in Python? (Evaluation of Clustering)

How to Evaluate the Performance of Clustering Algorithms in Python? (Evaluation of Clustering)

Understanding Backpropagation In Neural Networks with Basic Calculus

Understanding Backpropagation In Neural Networks with Basic Calculus

Uncovering High Confidence Errors or “Unknown Unknowns” in Machine Learning

Uncovering High Confidence Errors or “Unknown Unknowns” in Machine Learning

3 Easy Steps to Understand and Implement Spectral Clustering in Python

3 Easy Steps to Understand and Implement Spectral Clustering in Python

Three Clustering Algorithms You Should Know: k-means clustering, Spectral Clustering, and DBSCAN

Three Clustering Algorithms You Should Know: k-means clustering, Spectral Clustering, and DBSCAN

Easiest Way to Understanding Singular Value Decomposition (SVD) with Python: numpy.linalg.svd

Easiest Way to Understanding Singular Value Decomposition (SVD) with Python: numpy.linalg.svd

Understanding Pipeline in Machine Learning with Scikit-learn (sklearn pipeline)

Understanding Pipeline in Machine Learning with Scikit-learn (sklearn pipeline)

Step-by-Step Python Implementation with Dr. Data Science, Cost-sensitive Learning and Google Colab

Step-by-Step Python Implementation with Dr. Data Science, Cost-sensitive Learning and Google Colab

Machine Learning with Imbalanced Data - Part 5 (Ensemble learning, Bagging classifier)

Machine Learning with Imbalanced Data - Part 5 (Ensemble learning, Bagging classifier)

Machine Learning with Imbalanced Data -Part 4 (Undersampling, Clustering-Based Prototype Generation)

Machine Learning with Imbalanced Data -Part 4 (Undersampling, Clustering-Based Prototype Generation)

Machine Learning with Imbalanced Data - Part 3 (Over-sampling, SMOTE, and Imbalanced-learn)

Machine Learning with Imbalanced Data - Part 3 (Over-sampling, SMOTE, and Imbalanced-learn)

Machine Learning with Imbalanced Data - Part 2 (Cost-sensitive Learning)

Machine Learning with Imbalanced Data - Part 2 (Cost-sensitive Learning)

Machine Learning with Imbalanced Data - Part 1 (Confusion matrix, precision, and recall)

Machine Learning with Imbalanced Data - Part 1 (Confusion matrix, precision, and recall)

Mathematics for Machine Learning: Matrix Multiplication and (Pseudo) Inverse With NumPy

Mathematics for Machine Learning: Matrix Multiplication and (Pseudo) Inverse With NumPy

Mathematics for Machine Learning: Array, Norm, and Dot Product with NumPy

Mathematics for Machine Learning: Array, Norm, and Dot Product with NumPy

3 Easy Steps to Understand Gradient Descent in Machine Learning with Dr. Data Science

3 Easy Steps to Understand Gradient Descent in Machine Learning with Dr. Data Science

Is A Data Science Degree Worth It? 5 Tips for Choosing The Right Program

Is A Data Science Degree Worth It? 5 Tips for Choosing The Right Program

Mathematics for Machine Learning: What is Gradient?

Mathematics for Machine Learning: What is Gradient?

Simple Way to Understand Probability Density Function using Python

Simple Way to Understand Probability Density Function using Python

The Truth About Using Data Science Methods

The Truth About Using Data Science Methods

Visualizing Regression Models in Less Than Ten Seconds Using Seaborn

Visualizing Regression Models in Less Than Ten Seconds Using Seaborn

Simple Steps to Understand Linear Regression in Scikit-learn (sklearn.linear_model.LinearRegression)

Simple Steps to Understand Linear Regression in Scikit-learn (sklearn.linear_model.LinearRegression)