Inception V1 and GoogLeNet: Machine Learning Made Simple скачать в хорошем качестве

Inception V1 and GoogLeNet: Machine Learning Made Simple

#MachineLearning #MachineLearningMadeSimple #Data #Images #ImageClassification #DataScience #AI #Trending Help me become rich by joining Robinhood: https://join.robinhood.com/fnud75 Article on Neural Architecture Search: Why and How Is Neural Architecture Biased.   / why-and-how-is-neural-architecture-search-...   This video explains in the Inception V1 module and GoogLeNet Neural Network. These are legendary neural networks both in the image classification and detection phase and in the creation of Neural Networks in general. These showed that completely connected networks were inefficient for the cost and it was much cheaper to use sparse but deep networks in this case (technically another paper showed the last bit mathematically, but details). This is definitely a super duper cool idea, and I thank my Twitter followers for this. I had a lot of fun learning about this. If there's any topics you want covered, let me know. Also let's get this thing #Trending Paper Details: Paper: Going Deeper with Convolutions Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich Link: https://arxiv.org/abs/1409.4842 Abstract: We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. This was achieved by a carefully crafted design that allows for increasing the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC 2014 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection. Reach out to me: Check out my other articles on Medium. :   / machine-learning-made-simple   My YouTube: https://rb.gy/88iwdd Reach out to me on LinkedIn:   / devansh-devansh-516004168   My Instagram: https://rb.gy/gmvuy9 My Twitter:   / machine01776819   My Substack: https://devanshacc.substack.com/ Live conversations at twitch here: https://rb.gy/zlhk9y Get a free stock on Robinhood: https://join.robinhood.com/fnud75