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Deep Learning Neural Networks from Scratch- Keras Tensorflow

Link : Deep Learning Neural Networks from Scratch- Keras Tensorflow

The Sequential constructor takes an array of Keras Layers. Since we're just building a standard feedforward network, we only need the Dense layer, which is your regular fully-connected (dense) network layer. The first two layers have 64 nodes each and use the ReLU activation function. HOT & NEW

Deep Learning Neural Networks from Scratch- Keras Tensorflow
5.0 (2 ratings)
45 students enrolled
Created by Manifold AI Learning

What you'll learn
  • Deep learning using Keras to implement various problems like Binary Classification, Multi Class classification, & Regression
  • Intuition on Deep Learning Neural Networks by implementing the code in Python using Keras Library
  • Learn Python to kick start Deep Learning journey
  • Build intuition on Various Models in Deep learning and Learning algorithms in Deep learning
  • Basic coding experience in any programming language
  • Basic Mathematics Knowledge - High School Level
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Yes. AI is the new future, with the various technological advancements in hardware and software, Deep Learning algorithms are able to perform better compared to last decade. Combined with true power in hardware and research for better Deep Learning models, the field AI is growing exponentially.

With all the latest demand we have in this present world, We at ManifoldAILearning decided to create the course - DEEP LEARNING 101 - Kickstarter for Building Deep Neural networks
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