BESTSELLING AI REFERENCE
Deep Learning Interviews is a comprehensive resource for AI professionals. The PDF version is available for free download on arXiv. The book spans nearly 400 pages, offering hundreds of fully solved problems from key AI topics, including information theory, Bayesian statistics, algorithmic differentiation, logistic regression, perceptrons, and convolutional neural networks.
Designed to sharpen skills and deepen understanding, it provides step-by-step solutions, clear diagrams, and detailed reasoning to make complex concepts intuitive. Unlike typical technical references, it frames challenging problems within thought-provoking questions and engaging stories, enabling readers to confidently tackle interview questions and articulate answers with clarity.
LIVE DEMO - HuggingFace LLM
QuantumLLMInstruct (QLMMI) is a pioneering dataset with over 500,000 curated problem-solution pairs for quantum computing, the largest of its kind. Built from over 90 seed domains and LLM-generated subdomains, it enhances instruction fine-tuning to improve LLM performance in quantum physics challenges.
Using a four-stage methodology, QLMMI covers areas like synthetic Hamiltonians, QASM code generation, and Trotter-Suzuki decompositions. It employs advanced reasoning techniques like Chain-of-Thought and Task-Oriented Reasoning, validated by a zero-shot Judge LLM for quality and reliability.
400+
Book Pages
5+
Research Areas
Numerous
Open Source Projects
This comprehensive 400-page resource addresses the growing demand for AI expertise by providing hundreds of fully solved interview questions across key artificial intelligence topics. The work spans fundamental concepts including information theory, Bayesian statistics, algorithmic differentiation, logistic regression, perceptrons, and convolutional neural networks. Each solution is presented with step-by-step reasoning, clear diagrams, and detailed explanations that make complex concepts intuitive. The book uniquely frames challenging technical problems within thought-provoking questions and engaging narratives, enabling readers to confidently articulate answers during technical interviews. Available as both a free PDF and published book, it serves as an essential reference for AI professionals, students, and researchers seeking to deepen their understanding of machine learning fundamentals.
Deep Learning
Machine Learning
Interview Preparation
AI Education
Neural Networks
Bayesian Statistics
This groundbreaking work introduces QuantumLLMInstruct, a comprehensive dataset containing 500,000 quantum computing training examples specifically designed for machine learning applications. The research explores the intersection of quantum computing principles with modern large language model architectures, proposing novel approaches to instruction tuning and model optimization. The dataset encompasses a wide range of quantum computing concepts, algorithms, and practical implementations, making it an invaluable resource for training AI systems to understand and work with quantum technologies. The work includes live demonstrations through HuggingFace implementations and provides open-source access to both the dataset and training methodologies, significantly advancing the field of quantum-inspired machine learning.
Quantum Computing
Large Language Models
Instruction Tuning
Machine Learning Dataset
Quantum Machine Learning
Open Source
QonFusion presents a novel strategy for generating Gaussian random variables using non-parametric quantum circuits, offering an alternative to traditional pseudorandom number generators. The research integrates Quantum Random Number Generators (QRNGs) into classical diffusion models, with specific applications to Stable Diffusion and Brownian Motion simulations. The methodology is implemented in QonFusion, a comprehensive Python library that bridges classical and quantum computing by being compatible with both PyTorch and PennyLane frameworks. This work represents a significant advancement in quantum-classical hybrid computing, demonstrating practical applications of quantum randomness in generative AI and stochastic processes. The library provides researchers and practitioners with tools to explore quantum-enhanced randomness in machine learning applications.
Quantum Random Numbers
Stable Diffusion
Brownian Motion
Gaussian Random Variables
PyTorch
PennyLane
Quantum Circuits
This educational research explores the use of the Yao.jl quantum computing framework, written in Julia, for teaching quantum entanglement concepts to graduate students. The work provides a comprehensive pedagogical approach to understanding quantum entanglement and its quantification in quantum information processing experiments. The paper covers fundamental concepts including quantum superposition, Bell-state generation, and GHZ state generation, emphasizing the use of clear circuit diagrams and practical code fragments. This research contributes to quantum computing education by demonstrating how modern programming frameworks can make complex quantum concepts more accessible to students. The Julia-based approach offers computational efficiency and mathematical elegance, making it an ideal tool for quantum computing education and research.
Quantum Education
Julia Programming
Quantum Entanglement
Yao.jl
Bell States
GHZ States
Pedagogy
This innovative research applies quantum computing principles to music processing by developing a note detection algorithm based on the Quantum Fourier Transform (QFT). The work explores the fundamental role of QFT in quantum information processing, particularly its applications in famous algorithms like Shor's factoring algorithm and quantum phase estimation. The paper focuses on developing and implementing a quantum-based music note detection system that can identify musical tones through period detection on both simulated and real quantum computers. This interdisciplinary approach demonstrates the potential of quantum algorithms in signal processing applications beyond traditional quantum computing domains, opening new avenues for quantum-enhanced audio processing and digital signal analysis.
Quantum Fourier Transform
Music Processing
Signal Processing
Period Detection
Quantum Algorithms
Audio Analysis
This medical AI research presents a sophisticated ensemble approach for detecting pulmonary nodules in medical imaging, combining the strengths of 3D SE-ResNet18 and DPN68 deep learning architectures. The work addresses the critical challenge of early lung cancer detection by developing automated systems that can accurately identify suspicious nodules in chest imaging scans. The ensemble methodology leverages the complementary features learned by different neural network architectures to improve overall detection accuracy and reduce false positive rates. Published in the proceedings of ICIAR 2020, this research contributes significantly to computer-aided diagnosis in medical imaging, demonstrating how advanced deep learning techniques can assist radiologists in identifying potentially cancerous lesions with greater precision and reliability.
Medical AI
Deep Learning
Ensemble Methods
3D CNNs
ResNet
Medical Imaging
Lung Cancer Detection
