TensorFlow 2.0 - TensorFlow Strikes Back
Explore TensorFlow 2.0's new features, including Keras API, eager execution, and improved production pipeline, enhancing usability while maintaining performance and scalability.
Audio Classification with Machine Learning
Learn to build practical machine learning models for audio classification using spectrograms, CNNs, and techniques like transfer learning and data augmentation with Keras and TensorFlow.
Machine Learning for Everyone
Discover the essentials of machine learning, neural networks, and reinforcement learning in this engaging talk. Gain practical insights and witness a mind-blowing demo to kickstart your ML journey.
Automated Machine Learning With Keras
Explore automated machine learning with Keras Tuner and AutoKeras, enabling efficient development of high-performance deep learning models while requiring less expertise and effort.
Python and Flutter Application for Colouring and Enhancing Old Photos
Learn to colorize and enhance old photos using Python and Flutter, exploring deep learning models, API development, and mobile app creation for efficient image processing.
Code Like a Snake Charmer Version 2.0 - Introduction to Python
Comprehensive introduction to Python for data science, covering basics and machine learning using real-world datasets. Ideal for beginners looking to integrate Python into Microsoft tools.
Coding Deep Learning
Explore deep learning fundamentals, including matrix algebra and calculus, and get practical tips for using frameworks like Keras, PyTorch, and TensorFlow in this informative session.
Kick-Start Your Understanding of Machine Learning with Python
Practical guide to building ML applications in Python, focusing on text classification using unsupervised and supervised learning techniques with visualization and deep learning examples.
Deep Learning in the World of Little Ponies
Explore computer vision and image classification using deep learning techniques to identify "My Little Pony" characters, with hands-on examples in Keras and Python.
Data Augmentation Using Keras
Explore image augmentation techniques using Keras, including reshaping and multiclass augmentation, to enhance your deep learning models.
Machine Learning Software Ecosystems: Frameworks, Compilers, and Models - Part 1
Explore the landscape of ML frameworks, compilers, and models while understanding performance considerations for various hardware platforms and edge computing requirements.
3D U-Net for Semantic Segmentation
Learn to implement 3D U-Net for semantic segmentation on volumetric data like FIB-SEM, CT, and MRI. Covers data preparation, model architecture, training, and multichannel image processing using Python libraries.
Fixing the Image Problems of the Web Using Machine Learning
Explore how machine learning can address web image issues, enhancing user experience and accessibility. Learn innovative techniques for optimizing visual content on the internet.
Faceswap - Developing a Platform for Practical Deepfakes
Explore AI-powered Deepfakes: technology, applications, ethics, and future. Learn about face detection, alignment, recognition, and image reconstruction using Python, Keras, and TensorFlow.
Comparing Elixir and Python for Simple Neural Networks
Compare Elixir's Nx library with Python's machine learning ecosystem for training neural networks. Explore development experiences and performance differences using MNIST and CIFAR-10 datasets.
Deep Learning Like a Viking - Building Convolutional Neural Networks with Keras
Build a Convolutional Neural Network to recognize hand-written Viking runes using Keras, Python, and Flask. Learn CNN fundamentals and create a web application for real-time rune recognition.
Parallel Quantum Algorithms Innovation: PyTorch, Keras, and QiML
Explore parallel quantum algorithms for machine learning using PyTorch and Keras. Enhance model accuracy and efficiency by incorporating quantum layers in neural networks.
Quantum Machine Learning Parameters for Breakthrough Parallel Algorithms
Explore breakthrough parallel quantum algorithms for machine learning, improving performance and efficiency. Learn key benefits and optimal parameter settings for quantum-classical hybrid models.
Explainability in the MLOps Cycle - MLOps Podcast Episode 138
Explore explainability in MLOps with Dattaraj Rao. Gain insights on rule-based systems, ML model deployment, and emerging trends in AI. Learn about responsible AI and practical applications in various industries.
Reinforcement Learning - Pac-Man
Explore reinforcement learning through building a Pac-Man-beating agent. Gain insights into Q-learning, deep Q-learning with neural networks, and practical implementation using libraries like Keras.
Deep AutoViML for TensorFlow Models and MLOps Workflows
Explore Deep AutoViML for building powerful TensorFlow models and MLOps workflows. Learn to fast prototype data pipelines and models using TF 2.4+ and Keras preprocessing layers.