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79 видео
Regularization in Machine and Deep Learning (Ridge, Lasso, and Layer weight regularizers)
Understanding Vector Norms in Machine Learning (L1 and L2 norms, unit balls, and NumPy)
Dive into Deep Learning - Lecture 1: PyTorch Tensor Basics, Operations, Functions, and Broadcasting
Cross-Validation in Machine Learning: Do It Right from Scratch in Python (sklearn KFold)
The Truth About Using Data Science Methods
Functional API and hidden layers with different activation functions in Keras Deep Learning
Dive into Deep Learning Lec7: Regularization in PyTorch from Scratch (Custom Loss Function Autograd)
Understanding Pipeline in Machine Learning with Scikit-learn (sklearn pipeline)
Dive Into Deep Learning - Lecture 5: Parameter Access, Initialization, and storage in PyTorch
How to Evaluate the Performance of Clustering Algorithms in Python? (Evaluation of Clustering)
Programming for Data Science, Lec 2: Python Data Structures, List, Tuple, Set, and Dictionary
Programming for Data Science, Lec 12: Machine Learning in Python using scikit-learn (sklearn)
Data Loading, Understanding, and Filtering for Machine Learning Using Pandas
Programming for Data Science, Lec 10: Data Visualization and Plotting in Python Using Matplotlib
Stop Using torch.cuda! Unified Accelerator API in PyTorch!
Three Clustering Algorithms You Should Know: k-means clustering, Spectral Clustering, and DBSCAN
Statistical Machine Learning, Week 11: Implementing Convolutional Neural Networks (CNNs) using Keras
Feature Engineering Using The sklearn.preprocessing Package
Machine Learning with Imbalanced Data - Part 1 (Confusion matrix, precision, and recall)
PyTorch Explained: Computation Graph and Backpropagation, torch.autograd, backward, grad
Dive into Deep Learning - Lecture 4: Logistic/Softmax regression and Cross Entropy Loss with PyTorch
Machine Learning with Imbalanced Data - Part 5 (Ensemble learning, Bagging classifier)
Dive Into Deep Learning, Lecture 2: PyTorch Automatic Differentiation (torch.autograd and backward)
Logistic (Sigmoid) function in Statistical and Machine Learning (torch.nn.Sigmoid, tf.math.sigmoid)
Mathematics for Machine Learning: Matrix Multiplication and (Pseudo) Inverse With NumPy
Install TensorFlow/Keras on Apple Silicon Macs (M1, M2, M3, M4) with 4 Terminal Commands in 2024
Creating Machine Learning Workflows Using Pipeline in Scikit-Learn
Easiest Way to Understand Gradient Descent Step by Step (downhill to a minimum with derivatives)
Simple Way to Understand Probability Density Function using Python
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
3 Easy Steps to Understand and Implement Spectral Clustering in Python
Step-by-Step Python Implementation with Dr. Data Science, Cost-sensitive Learning and Google Colab
Easiest Way to Understanding Singular Value Decomposition (SVD) with Python: numpy.linalg.svd
Programming for Data Science, Lec 3: NumPy ndarray, np.arange, np.random, np.linalg, np.concatenate
Programming for Data Science, Lec 15: Neural Networks, TensorFlow AutoDiff, and Keras
Understanding Probabilistic Neural Networks: The Gaussian Output Layer (Theory and Implementation)
Sequential Model, Dense Layer, and Model Compile in Keras Deep Learning
Dive into Deep Learning – Lec 6: Basics of Object-Oriented Programming in PyTorch (torch.nn.Module)
A Gentle Introduction To Math Behind Neural Networks and Deep Learning (nested composite function)
Machine Learning/Data Science Exam/Interview Preparation With Practice Tests, Examples, and Solution
Simple Steps to Understand Linear Regression in Scikit-learn (sklearn.linear_model.LinearRegression)
Programming for Data Science: Lec 5, Branching Statements in Python ('if', 'elif', and 'else')
NVIDIA System Management Interface (nvidia-smi) to monitor NVIDIA GPU devices
Automatic Differentiation for ABSOLUTE beginners: "with tf.GradientTape() as tape"
Full Python Programming Course for Data Science & Machine Learning w/ Jupyter Notebooks & Exercises
Clustering Methods: DBSCAN, Gaussian Mixtures, and Normalized Mutual Information
Statistical Machine Learning, Week 5: Loss Function Optimization using Gradient Descent
Install PyTorch on Apple Silicon Macs (M1, M2, M3, M4) and Check for MPS Availability in 2024
Runtime measurement for deep learning/PyTorch: CPU vs. GPU (torch.cuda.Event)
The Absolute Beginner’s Guide to Google Colaboratory or Colab by Dr. Data Science
