Deep Learning Tutorial

Deep learning is a branch of machine learning which is completely based on artificial neural networks, as neural networks are going to mimic the human brain so deep learning is also a kind of mimic of the human brain.

This Deep Learning tutorial is your one-stop guide for learning everything about Deep Learning. It covers both basic and advanced concepts, providing a comprehensive understanding of the technology for both beginners and professionals. Whether you’re new to Deep Learning or have some experience with it, this tutorial will help you learn about different technologies of Deep Learning with ease.

Deep Learning 

What is Deep Learning?

Deep Learning is a part of Machine Learning that uses artificial neural networks to learn from lots of data without needing explicit programming. These networks are inspired by the human brain and can be used for things like recognizing images, understanding speech, and processing language. There are different types of deep learning networks, like feedforward neural networks, convolutional neural networks, and recurrent neural networks. Deep Learning needs lots of labeled data and powerful computers to work well, but it can achieve very good results in many applications.

Table of Content

• Basic Neural Network
• Artificial Neural Network
• Convolution Neural Network
• Recurrent Neural Network
• Generative Learning
• Reinforcement Learning

• Basic Neural Network
  • Biological Neurons Vs Artificial Neurons
  • Single Layer Perceptron 
  • Multi-Layer Perceptron
  • Forward and backward propagation
  • Feed-forward neural networks
  • Neural Network layers
  • Introduction to Activation Function
  • Types Of Activation Function
    • Activation Functions in Pytorch
    • Activation Functions in TensorFlow
  • Understanding Activation Functions in Depth
• Artificial Neural Network
  • Cost function in neural networks
  • How does Gradient Descent work
  • Vanishing or Exploding Gradients Problems
  • Choose the optimal number of epochs
  • Batch Normalization in Deep Learning
  • Difference between Sequential and functional API
  • Classification
    • Classify Handwritten Digits with TensorFlow
    • Classify Handwritten Digits with PyTorch
  • Regression
    • Linear Regression using PyTorch
    • Linear Regression Using Tensorflow
  • Fine-Tuning & Hyperparameters
• Convolution Neural Network
  • Digital Image Processing Basics
  • Image Processing
  • Pooling layer
  • Convolution Neural Networks or convents
  • CNN for image classification
  • Different CNN architecture
  • Pre-trained model for image classification
  • Difference between Object Detection and Image Segmentation
  • YOLO v2 – Object Detection
• Recurrent Neural Network
  • What is time Series Data?
  • Natural Language Processing
  • Tokenization, Stemming, and Lemmatisation
  • Word Embeddings
  • Recurrent Neural Network
    • Recurrent Neural Network architecture
    • Sentiment Analysis using RNN
    • Time Series forecasting using RNN
    • Short-Term Memory problem in RNN
    • Bi-directional RNN architecture
  • Long Short Term Memory (LSTM) 
    • Introduction to Long Short-Term Memory
    • Long Short-Term Memory architecture
    • LSTM – Derivation of Backpropagation through time
    • Text Generation using LSTM
  • Gated Recurrent Unit
    • Text Generation using Gated Recurrent Unit Networks
• Generative Learning
  • AutoEncoder
  • How Autoencoders works
  • Types of AutoEncoder
    • Linear Autoencoder
    • Stacked Autoencoder
    • Convolutional Autoencoder
    • Recurrent Autoencoder
    • Denoising Autoencoder
    • Sparse Autoencoder
  • Variational AutoEncoder
  • Contractive Autoencoder (CAE)
  • AutoEncoder with TensorFlow 2.0
  • Implementing an Autoencoder in PyTorch
  • Generative adversarial networks
    • Basics of Generative Adversarial Networks (GANs)
    • Generative Adversarial Network (GAN)
    • Use Cases of Generative Adversarial Networks
    • Building a Generative Adversarial Network using Keras
    • Cycle Generative Adversarial Network (CycleGAN)
    • StyleGAN – Style Generative Adversarial Networks
• Reinforcement Learning
  • Reinforcement Learning Introduction
  • Optimizing Rewards in Reinforcement Learning
  • Thompson Sampling Reinforcement Learning
  • Reinforcement Learning framework 
  • Markov Decision Process
  • Bellman Equation
  • Meta-Learning
  • Policy-Based Reinforcement Learning
  • Reinforcement Learning with Neural Networks
  • Q-Learning
    • Q-learning Implementation
  • Deep Q Learning
    • Deep Q Learning
    • Implementing Deep Q-Learning using TensorFlow
    • AI-Driven Snake Game using Deep Q Learning

Application of Deep Learning

• Virtual Assistants, Chatbots and robotics
• Self Driving Cars
• Natural Language Processing
• Automatic Image Caption Generation
• Automatic Machine Translation

FAQS on Deep Learning

Q1. Which language is used for deep Learning?

Deep learning can be implemented using various programming languages, but some of the most commonly used ones are Python, C++, Java, and MATLAB.

Q2. What is the First Layer of Deep Learning?

The input layer is the first layer in any deep Learning Model.

Q3. How can I start learning deep learning?

You can easily start deep learning by following the given Steps:

1. First, Learn machine learning basics.
2. Start Learning Python.
3. Choose a deep learning framework.
4. Learn neural network basics.
5. Practice with toy datasets.
6. At Last, Work on real-world projects.
    

Q4. Is CNN deep learning?

Yes, Convolutional Neural Networks (CNNs) are a type of deep learning model commonly used in image recognition and computer vision tasks.

Q5. What is the difference between AI and deep learning?

Deep learning is a type of Artificial Intelligence and Machine learning that imitates the way humans gain certain types of knowledge.

Q6. What are the four pillars of Machine Learning?

The four pillars of deep learning are artificial neural networks, backpropagation, activation functions, and gradient descent.

Q7. Where can I practice Deep Learning interview questions?

You can prepare interview with our recommended Deep Learning Interview Question and answer

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