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3 Deep Learning Projects You Can Do for Fun , You want to build ,10 Deep Learning Projects You Can Do for Fun Then keep reading ,- helpcodes.me

 

3 Deep Learning Projects You Can Do for Fun



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You want to build ,10 Deep Learning Projects You Can Do for Fun Then keep reading , 3 Projects You Can Build Using Deep Learning?,


Build an AI Chatbot

You probably used AIM chat rooms back in high school. You know how it worked? You could type in an individual message and the computer would respond with “a right?”, “hmmm”, or “omg, yes!” If you got lucky, your friend would type in the response, like “yes!”. The computer would type back the response, and then you’d type in your response. After a few rounds of this, you’d get the answer “You are an idiot!”, and have to start over. In that first round of conversation, you would usually learn a few things about your friend’s personality and that would help you more the next time. Today, chatbots have evolved significantly. Chatbots allow us to interact with our computers with humans, not computers.


Build a Deep Learning Car

Continue reading , Deep Learning Car, Step 1: Setup 5. Deep Learning Platform as a Service Continue reading , Deep Learning Platform as a Service Try Deep Learning Click to Start Building This Deep Learning Platform, You Can Build This Deep Learning Platform, Step by Step 8. Deep Learning Software Library Continue reading , Deep Learning Software Library, Step by Step Try Deep Learning The best way to learn is to build and experiment with systems that you haven't built before. Try Deep Learning Creating the Maze 3. PASCAL: A Deep Learning Research Language Continue reading , PASCAL: A Deep Learning Research Language, Step by Step 9.


Build Image Recognition Software

We can learn to do simple things like detecting landmarks on our images with a convolutional neural network. In this example, I use the CaffeNet architecture in the standard version (detected landmark) and the convolutional variant (recognition) and tweak the network parameters. The initial training steps for the network usually look like this: Let’s start with some pre-processing code, a basic class to draw paths on a MNIST dataset in Matlab. I also modify the training data to have different classes, like stop signs and houses. I like to mix up random data points and classes in an image. That gives me the most control over what kinds of features we use in our model.

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