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This Edureka TensorFlow Full Course video is a complete guide to Deep Learning using TensorFlow. It covers in-depth knowledge about Deep Leaning, Tensorflow & Neural Networks. Below are the topics covered in this TensorFlow tutorial: – Introduction – Artificial Intelligence – Why Artificial Intelligence? – What is Artificial Intelligence? – Artificial Intelligence Domains – Artificial Intelligence Subsets – Machine Learning – Types of Machine Learning – Machine Learning Use Case – Supervised Learning – Types of Supervised Learning – Use Case 2 – Linear Regression – Linear Regression Demo – Regression Application – Building Logistic Regression Model – Logistic Regression Use Case – Analysing Performance Of The Model – Calculating The Accuracy – Logistic Regression Demo – Clustering Use Case – How Clustering works? – Initialization – Cluster Assignment – Move Centroid – Optimization – Convergence – How to find optimal solution? – Choosing the number of cluster – Reinforcement Learning – Limitation of Machine Learning – How Deep Learning Solves the Issue? – What is Deep Learning? – Applications of Deep Learning – What is a Tensor? – Rank of Tensors – Shape of a Tensor – What is TensorFlow? – TensorFlow Code Basics – TensorFlow Basic Demo – Activation or Transformation Function – Linear – Unit Step – Sigmoid – Tanh – ReLU – Softmax – Activation Function Demo – How Neuron Works? – What is a Perceptron? – Role of Weights & Bias – Perceptron Example – Training a Perceptron – Perceptron Learning Algorithm – Training Network Weights – Reducing The Loss – Perceptron Learning Algorithm Demo Course Length: 3:11:40 hours Source: Edureka

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