Deep Learning: Generative Neuronale Netzwerke mit Python Entwerfe eigene künstliche Intelligenzen (AI) mit TensorFlow und Keras, die Bilder von Tieren un Objekten erzeugen kann.
Object Recognition with Deep Learning using OpenCV and C# Building Object Detection and Classification Applications using Computer Vision, Deep Learning, OpenCV and C#
Data Science: Hands-On 1 Hour Project On Deep Learning. Image Classification for Autonomous Vehicle using Keras : Learn to explore datasets and build, train and test models.
In this practical Learning Path, you will build Deep Learning applications with real-world datasets and Python. Beginning with a step by step approach, right from building your neural nets to reinforcement learning and working with different Deep Learning applications such as computer Vision and voice and image recognition, this course will be ...
Computer Vision: YOLO Custom Object Detection with Colab GPU 0.0 (0 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
Data science: Learn linear regression from scratch and build your own working program in Python for data analysis.
Computer Vision: Python OCR & Object Detection Quick Starter 4.4 (19 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
In this deep learning course I'm going to teach you to create an LSTM model for generating text using Keras with Tensorflow backend. I am using Marcus Aurelius Meditations as my source text, hence the name of the course. This course is best suited for students, who are already familiar with Python and want to build their deep learning portfolio.
The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques
TensorFlow 101: Introduction to Deep Learning 4.6 (144 ratings) Course Ratings are calculated from individual students