a_i | m_l | py | PyTorch
Convexity Optimization in Machine Learning Models
next-gen Quantum Algorithms: Adaptive Quantum Circuits (AQCs)…an improvement upon VQCs??
Custom Array Containers with Dask and CuPy: Pythonic Machine Learning
Exploding and Vanishing Gradients in Machine Learning: Causes, Solutions, and the Role of Optimization
Overfitting in Machine Learning: Causes, Countermeasures, and the Role of Convexity
Transformers: The Powerhouse Behind Modern NLP in ML
TensorFlow and Keras: Driving and Simplifying Machine Learning Innovation
torch machine learning
The Future of AI: Exploring Transfer Learning, Domain Adaptation, Quantization, and Beyond
what are pandas python machine learning?
Neural Networks and Their Importance in AGI: Introducing The Future of Intelligence
Understanding Deep Learning Architectures: CNNs, DNNs, and Transformers
Deep Learning: Unraveling Its Uses and Future Potential
Quantization of Deep Models
Unveiling AutoML: The Future of Automated Machine Learning in Python
A Primer On OpenCV
Emotional AI: Revolutionizing Human-Machine Interaction with Python Libraries
Stemming and Lemmatization Defined
Deep Learning and Neural Networks: Transforming Industries and Shaping the Future
DeepFace: Unraveling the Power of Facial Recognition Technology
Apache Spark: Powering Big Data Processing and Analytics
ETL: Powering Data-Driven Decisions in the Age of AI
Resilient Distributed Datasets (RDDs) in Apache Spark: A Comprehensive Guide
Key Characteristics of Transformations: Lazy Evaluation, Immutability, and Lineage
MLlib: Apache Spark’s Machine Learning Library
MLlib’s rich Algorithms Apache Spark’s Machine Learning Library
PageRank Algorithm: Google’s Pioneering Web Ranking System
RankBrain: Google’s Machine Learning-Based Search Algorithm
MUM (Multitask Unified Model): Google’s Advanced AI for Search
Neural Matching: Enhancing Google’s Search with Semantic Understanding
Understanding GNNs: Graph Neural Networks
Machine Learning Algorithms
TensorFlow and Keras
TRAX: the Future of Deep Learning
TRAX: The Cutting-Edge Framework for Deep Learning in 2024
Using Sonnet in TensorFlow
Deep Learning
Cirq, IonQ, and TensorFlow in Quantum Computing
Modular Layer Perceptron (MLP) in Sonnet: Advancing Neural Network Architectures
Adam Optimizer: The Best All Around Optimization Algorithm?
Contextual Bandits: Dynamic Pricing and Real-Time Prediction
cutting-edge multi-armed bandit (MAB) algorithms for deep learning, meta-learning, and contextual adaptation
Meta-Learning Bandits: Modern Algos for Rapid Adaptation
Neural Bandits in Dynamic Pricing: Advanced Cutting-Edge Applications and Algorithms
Bandit Algos: The Apex of Real-Time Ad Auctions and Financial Derivatives Trading
Grokking in Deep Learning: Advanced Concepts, Code Implementations, and Future Directions
Xavier/Glorot Initialization: Advanced Techniques and Implementation in Deep Learning
Dissertation on Cutting-Edge Meta-Reinforcement Learning Algorithms: Concepts, Code, and Implementations
TinyML: Machine Learning Python
Edge-AI-Tiny: Low-Power Machine Learning for the Edge in 2024
Sparse Neural Networks in Edge-AI-Tiny and Machine Learning
Sparse-GEMM (General Matrix Multiplication) Algorithms in 2025
vRAN: Expert Insights and Real Code 2025 Concepts
Quantum Parallelism: The Future of Computing in the Quantum Era
Qiskit Algorithms
Quantum Interference: A Cornerstone of Quantum Computing
Quantum Machine Learning in 2025: The Power of QML, VQCs, and QSVMs with Qiskit
Expounding on Qiskit Algorithms: A Deep Dive
Quantum Advantage
Exponential Speedups in Computational Tasks Like Search Algorithms, Factorization, and Machine Learning
Exponential Speedups in Quantum Computing
even more Advanced Quantum Algorithms in 2025: Pushing the Boundaries with Qiskit
SLURM: The Ultimate Guide to High-Performance Computing Workload Management
PyTorch Techniques for 2025: What You Need to Know
Metaprogramming Custom CUDA Kernels with PyCUDA and CuPy for 2025 GPU Computing
Backpropagation and Neural Network Training in PyTorch: A Beginner’s Guide
CUDA, cuDNN and PyTorch
all about CUDA
tensors in CUDA an ultra basic primer
tensor cores in action explained
cuDNN basics
cuDNN more INsights
cuDF: GPU-Accelerated Data Processing with NVIDIA’s RAPIDS AI
cuML: GPU-Accelerated Machine Learning
cuGraph: GPU-Accelerated Graph Analytics
Deep Learning Evolution
what are parameters
weights+gradients
error+loss
optimize the loss
it’s an iterative process
PyTorch training loop
mixed precision fp16
fp16 libraries
cuda libraries
JupyterHub: The Future of AI & Machine Learning Collaboration in 2025
pytorch project_1
pytorch proj2
The HISTORY of Multi-GPU Training:- A Deep Learning Discussion
distributed_training_system.py
High-Performance Distributed Computing with PyTorch: Multi-GPU Architecture
named_tensors_
TorchScript- the NVlink of PyTorch models
einsum in NumPy
PyTorch Autograd and Gradient Descent: Deep Training Neural Networks
Embeddings in PyTorch: When to Use and How They Power Machine Learning Models
Understanding Neural Network Models: Concepts and Applications
PyTorch’s Autograd: Backpropagating All Things
Model Fine-Tuning
SSH: Secure Remote Access, History, and Usage Guide with PuTTY (2025 Edition)
Gradients, Rate of Loss, Options, Delta, and Backpropagation in Machine Learning
PyTorch_3 nn.Module and SOFTMAX, nn.Sequential, and more
PyTorch_4: Unpacking nn.CrossEntropyLoss, nn.LogSoftmax, and nn.NLLLoss
PyTorch_5: Convolutional Layers in Deep Learning
Classification Models: Understanding Their Structure and Practical Applications
Deep Learning Foundations: From Logits to Training Loops
Deep Learning Concepts: From nn.Module to Tensor Masking
Deep Learning Workflow: Metrics, Losses, and Tensorboard
Using TensorBoard in PyTorch: History, Purpose, and Advantages
Understanding the F1 Score: A Harmonized Metric of Precision and Recall
Segmentation: From Basics to 2035 Concepts
CrossEntropyLoss: loss function design
Deploying PyTorch Models: A Comprehensive Dissertation for Advanced Practitioners
Advanced PyTorch Concepts for Molecular Dynamics in 2025: Libraries, Strategies, and Innovations
Machine Learning Potentials in 2025: ANI Family, Differentiable Simulations, and Advanced Applications
Mastering TorchMD: Advanced Molecular Dynamics with Deep Learning in 2025
TorchANI and the ANI Family: Advanced Neural Network Potentials for Molecular Simulations
nn.LogSoftmax: Loss Function Neural Networks
nn.NLLLoss: Log Probability Classification in PyTorch
Dice Loss in Image Segmentation
ROC/AUC Metrics in PyTorch_Model_Eval
intro to Molecular Dynamics Segmentation
The Dual Nature of PyTorch: Interface and Backend
intro to JIT and TorchScript: AI Training and Inference
PyTorch JIT: The Future of Optimized Training and Inference
Advanced Concepts in JIT and TorchScript for Quantum Physicists and Molecular Dynamics Researchers
JIT and TorchScript for Molecular Dynamics
Quantization in Deep Learning
JIT and TorchScript in Quantization
Quantization-Aware Training (QAT): Advanced Techniques with JIT, TorchScript, and PyTorch
Tracing and Scripting in ML Workflows: PyTorch, TorchScript, and JIT-ed Models
ATen: Foundational PyTorch Tensor Library
ATen: Integration with PyTorch and 2025 AI Workflows
PyTorch cheat_sheet
Hugging Face Transformers Basics
Hugging Face Transformers: AI Concepts for 2025
Numerical Stability in PyTorch: Mixed Precision Computation 2025 AI Innovations
Kernels in PyTorch: The Nexus of Efficiency with JIT, TorchScript, and Quantization
Dynamic Fusion in PyTorch: The Future of Accelerated Deep Learning with JIT, TorchScript, and Quantization
Quantization Operators in PyTorch: The Foundation of Modern AI Optimization with QAT, JIT, and TorchScript
