Deep Learning Python - Learn Deep Learning with examples - Deep Learning Tutorial

Deep Learning Full Course video will help you understand and learn Deep Learning & Tensorflow in detail. This Deep Learning Tutorial is ideal for both beginners as well as professionals who want to master Deep Learning Algorithms. Below are the topics covered in this Deep Learning tutorial video:

• What is Deep Learning
• Why Artificial Intelligence?
• What is AI?
• Applications of AI
• Machine Learning
• Types of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Limitations of Machine Learning
• Deep Learning to the Rescue
• What is Deep Learning?
• Deep Learning Example
• Deep Learning Applications
• Deep Learning Tutorial
• Understanding Deep Learning With an Analogy
• How Deep Learning works?
• Why We need Artificial Neuron?
• Perceptron Learning Algorithm
• Types of Activation Functions
• Single Layer Perceptron Use-case
• What is TensorFlow?
• Tensorflow Code Basics
• TensorFlow Example
• What is a Computational Graph?
• Limitations of Single Layer Perceptron
• Multilayer Perceptron
• How it works?
• What is Backpropagation?
• Backpropagation Learning Algorithm
• Multilayer Perceptron Use-case
• Top 8 Deep Learning Frameworks
• Chainer
• CNTK
• Caffe
• MXNet
• Deeplearning4j
• Keras
• PyTorch
• TensorFlow
• TensorFlow Tutorial
• Rock or Mine Prediction Use-case
• :53 How to Create This Model?
• What are Tensors?
• Tensor Rank
• What is TensorFlow?
• Graph Visualization
• Constant, Placeholder & Variables
• Creating A Model
• Reducing The Loss
• Batch Gradient Descent
• Implementing Rock or Mine Prediction Use-case
• Artificial Neural Network Tutorial
• Why Neural Network?
• Problems Before Neural Network
• What is Artificial Neural Network?
• How It Works?
• Perceptron Learning Algorithm - Beer Analogy
• Multilayer Perceptron
• Artificial Neutral Network
• Training A Neural Network
• Applications of Network Networks
• Backpropagation & Gradient Descent Tutorial
• Perceptron
• How does the Network Learn?
• MNIST Dataset
• Cost Function
• Finding Local Minima
• Gradient Descent Learning
• Back Propagation
• Recurrent Neural Networks
• Why not Feedforward Network?
• What is Recurrent Neural Networks?
• Training A Recurrent Neural Network
• Vanishing & Exploding Gradient Problem
• Long Short Term Memory Networks
• Convolutional Neural Network
• How A Computer Reads An Image?
• Why Not Fully Connected Network?
• What Convolutional Neural Network?
• How CNN Works?
• Convolution Layer
• ReLU Layer
• Fully Connected Layer
• Autoencoders Tutorial
• PCA vs Autoencoders
• Introduction to Autoencoders
• Properties of Autoencoders
• Training Autoencoders
• Architecture of Autoencoders
• Types of Autoencoders
• Convolutional Autoencoders
• Sparse Autoencoders
• Deep Autoencoders
• Contractive Autoencoders
• Demo
• Restricted Boltzmann Machine
• Working of RBMs
• RBM: Energy-Based Model
• RBM: Probabilistic Model
• RBM Training
• RBM: Training to Prediction
• RBM: Example
• TensorFlow Object Detection
• What is Object Detection?
• Object Detection Applications
• Workflow of Object Detection
• Object Detection in TensorFlow
• Object Detection Demo
• Creating Chatbots Using Tensorflow
• What is Chatbots?
• How Does ChatBot Works?
• Applications of Chatbot
• Layers of Chatbot
• Natural Language Processing
• Demo
• Layers of Chatbot
• Deep Learning Interview Questions

#deep-learning #machine-learning #tensorflow