Flutter iOS & Android Fruit Detection Using Machine Learning TensorFlow Lite - GetX Flutter Deep Learning Image Processing App Development
Develop a Flutter app for iOS and Android that detects fruits using TensorFlow Lite, incorporating machine learning and image processing techniques.
RStudio Tutorial
Comprehensive RStudio tutorial covering statistical analysis, data visualization, bioinformatics, machine learning, and more. Ideal for beginners and advanced users seeking practical skills in R programming.
Learn TensorFlow and Deep Learning Fundamentals with Python - Code-First Introduction Part 1-2
Comprehensive introduction to TensorFlow and deep learning fundamentals, covering tensor operations, neural network regression, and classification. Hands-on coding examples and practical insights for building and improving models.
Learn TensorFlow and Deep Learning Fundamentals with Python - Code-First Introduction Part 2/2
Dive deep into TensorFlow and Python, mastering non-linear models, multi-class classification, and advanced techniques for optimizing neural networks and evaluating their performance.
PyTorch and the Journey in Open Source - Keynote by Soumith Chintala
Soumith Chintala discusses PyTorch's journey, principles, and ecosystem, exploring its development, community impact, and future directions in deep learning and open-source collaboration.
Random Walks II - Lecture 13
Explore random walks, Pascal's triangle, and probability distributions using Julia programming. Learn to implement time evolution of probabilities and understand cumulative sums in computational thinking.
Transforming Images and Image Processing Techniques - Lecture 2
Explore image transformation techniques using Julia, including downsampling, linear combinations, convolutions, and filters. Learn about complexity, GPUs, and data structures in computational thinking.
Convolutions in Image Processing - MIT 18.S191 Fall 2020 - Week 1
Explore image processing convolutions, from basic blurs to edge detection, using Julia. Learn about kernels, computational complexity, and connections to Fourier transforms.
Complete Machine Learning in 6 Hours - Krish Naik
Comprehensive machine learning tutorial covering key algorithms, techniques, and practical implementations for regression, classification, clustering, and ensemble methods.
Deep Learning Indepth Tutorials With Krish Naik
Comprehensive deep learning tutorial covering key concepts, techniques, and practical implementations, from perceptrons to CNNs, with hands-on examples and expert insights.
Saving Lives Behind the Wheel - Artificial Intelligence and Computer Vision for Road Safety
Explore AI and computer vision's role in enhancing road safety through real-time edge-based deep learning systems, addressing challenges and leveraging technological trends to reduce traffic fatalities.
Crash Course on Transfer Learning
Explore transfer learning in deep learning models, including its concept, applications, benefits, and methods. Learn how models leverage prior knowledge for improved performance and efficiency.
Introduction to Convolutional Neural Networks with TensorFlow
Learn to build and implement convolutional neural networks for image classification using TensorFlow. Gain hands-on experience in deep learning techniques for computer vision tasks.
JavaScript for Cancer Prevention Through Early Detection
Explore how JavaScript, TensorFlow.js, and React Native can enhance melanoma detection accuracy, aiding early skin cancer diagnosis and potentially saving lives through innovative technology.
Genomics, Imaging and AI - Ewan Birney
Explore the convergence of genomics, imaging, and AI in biological research and clinical practice, highlighting technological advancements, data challenges, and future opportunities in life sciences.
Artificial Intelligence Tutorial for Beginners - What Is AI?
Comprehensive introduction to AI, covering neural networks, NLP, and computer vision. Explores key concepts, applications, and real-world use cases for beginners entering the field of artificial intelligence.
Linear Systems of Differential Equations with Forcing - Convolution and the Dirac Delta Function
Explores linear differential equations with forcing, covering impulse response, Dirac delta function, and convolution integral. Essential for control theory and linear systems analysis.
Inference with Torch-TensorRT Deep Learning Prediction for Beginners - CPU vs CUDA vs TensorRT
Accelerate deep learning inference using Torch-TensorRT. Compare CPU, CUDA, and TensorRT performance with ResNet50 for image classification. Hands-on tutorial covers setup, implementation, and benchmarking.
HuggingFace Crash Course - Sentiment Analysis, Model Hub, Fine Tuning
Comprehensive introduction to HuggingFace and Transformers library, covering sentiment analysis, Model Hub exploration, and fine-tuning techniques for NLP tasks.
TensorFlow 2 Beginner Course
Comprehensive introduction to TensorFlow 2.0 and Keras, covering key concepts and practical applications in deep learning through hands-on projects and real-world examples.
Autoencoder in PyTorch - Theory & Implementation
Learn to implement Autoencoders in PyTorch, covering theory, data loading, simple and CNN architectures, training loops, and image plotting. Gain practical deep learning skills through hands-on coding.