Lectures related to Transport Equation: Numerical Analysis

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Explores transport equations, emphasizing conservation principles and scheme stability.

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Explores optimization methods in machine learning, emphasizing gradients, costs, and computational efforts for efficient model training.

Explores the dynamics of a block pulled by a spring under various conditions.

Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.

Addresses challenges in data cleaning for analysis, proposing optimizations to reduce processing time.

Explores models, learning paradigms, and applications in Mathematics of Data.

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.

Covers the Boltzmann Machine, a type of stochastic recurrent neural network.

Covers data wrangling techniques using Apache Hive for efficient big data management.

Introduces a course on Genetics & Genomics, focusing on R programming with interactive exercises.