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Welcome to the "Deep Learning CNN - Convolutional Neural Networks with Python" tutorial. In this video, we will be discussing how to build and optimize CNN models using the Python programming language. You will learn about the basics of deep learning, convolutional neural networks, and how to implement and optimize CNN models for image recognition.
This tutorial is suitable for those who have a basic understanding of Python programming and want to learn more about deep learning and CNNs. We will provide examples of CNN implementation using Python libraries such as TensorFlow and Keras, as well as explain various techniques and strategies that can improve the performance of CNN models.
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Deep Learning CNN - Convolutional Neural Networks with Python

Chapter 4 Deep Neural Network Overview
001. Neuron and Perceptron
002. DNN Architecture
003. DNN Architecture Quiz
004. DNN Architecture Solution
005. FeedForward FullyConnected MLP
006. Calculating Number of Weights of DNN
007. Calculating Number of Weights of DNN Quiz
008. Calculating Number of Weights of DNN Solution
009. Number of Neurons Versus Number of Layers
010. Discriminative Versus Generative Learning
011. Universal Approximation Theorem
012. Why Depth
013. Decision Boundary in DNN
014. Decision Boundary in DNN Quiz
015. Decision Boundary in DNN Solution
016. BiasTerm
017. BiasTerm Quiz
018. BiasTerm Solution
019. Activation Function
020. Activation Function Quiz
021. Activation Function Solution
022. DNN Training Parameters
023. DNN Training Parameters Quiz
024. DNN Training Parameters Solution
025. Gradient Descent
026. Backpropagation
027. Training DNN Animation
028. Weight Initialization
029. Weight Initialization Quiz
030. Weight Initialization Solution
031. Batch MiniBatch Stochastic Gradient Descent
032. Batch Normalization
033. Rprop and Momentum
034. Rprop and Momentum Quiz
035. Rprop and Momentum Solution
036. Convergence Animation
037. DropOut, Early Stopping and Hyperparameters
038. DropOut, Early Stopping and Hyperparameters Quiz
039. DropOut, Early Stopping and Hyperparameters Solution

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