Deep Learning Tutorial Last Updated : 02 Jul, 2025 Summarize Comments Improve Suggest changes Share Like Article Like Report Deep Learning is a subset of Artificial Intelligence (AI) that helps machines to learn from large datasets using multi-layered neural networks. It automatically finds patterns and makes predictions and eliminates the need for manual feature extraction. Deep Learning tutorial covers the basics to advanced topics making it perfect for beginners and those with experience. Introduction to Neural Networks Neural Networks are fundamentals of deep learning inspired by human brain. It consists of layers of interconnected nodes or "neurons" each designed to perform specific calculations. These nodes receive input data, process it through various mathematical functions and pass the output to subsequent layers.Neural NetworksBiological Neurons vs Artificial NeuronsSingle Layer Perceptron Multi-Layer PerceptronArtificial Neural Networks (ANNs)Types of Neural NetworksArchitecture and Learning process in neural networkBasic Components of Neural NetworksThe basic components of neural network are: Layers in Neural NetworksWeights and Biases Forward Propagation Activation Functions Loss Functions Backpropagation Learning Rate Optimization Algorithm in Deep LearningOptimization algorithms in deep learning are used to minimize the loss function by adjusting the weights and biases of the model. The most common ones are: Optimization algorithms in deep learningGradient Descent Stochastic Gradient Descent (SGD)Batch Normalization Mini-batch Gradient DescentAdam (Adaptive Moment Estimation)Momentum-based Gradient OptimizerAdagrad OptimizerRMSProp OptimizerA deep learning framework provides tools and APIs for building and training models. Popular frameworks like TensorFlow, PyTorch and Keras simplify model creation and deployment.For more details you can refer to: What is a Deep Learning Framework?Types of Deep Learning ModelsLets see various types of Deep Learning Models:1. Convolutional Neural Networks (CNNs)Convolutional Neural Networks (CNNs) are a class of deep neural networks that are designed for processing grid-like data such as images. They use convolutional layers to automatically detect patterns like edges, textures and shapes in the data.Deep Learning AlgorithmsConvolutional Neural Networks (CNNs)Basics of Digital Image Processing Importance for CNN PaddingConvolutional LayersPooling Layers Fully Connected LayersBackpropagation in CNNs CNN based Image Classification using PyTorch CNN based Images Classification using TensorFlow CNN Based Architectures: There are various architectures in CNNs that have been developed for specific kinds of problems such as: Convolutional Neural Network (CNN) ArchitecturesLeNet-5AlexNet VGGnetVGG-16 Network GoogLeNet/Inception ResNet (Residual Network)MobileNet 2. Recurrent Neural Networks (RNNs)Recurrent Neural Networks (RNNs) are a class of neural networks that are used for modeling sequence data such as time series or natural language.Recurrent Neural Networks (RNNs)How RNN Differs from Feedforward Neural NetworksBackpropagation Through Time (BPTT)Vanishing Gradient and Exploding Gradient Problem Training of RNN in TensorFlowSentiment Analysis with RNNTypes of Recurrent Neural Networks: There are various types of RNN which are as follows:Types of Recurrent Neural NetworksBidirectional RNNsLong Short-Term Memory (LSTM)Bidirectional Long Short-Term Memory (Bi-LSTM)Gated Recurrent Units (GRU)3. Generative Models in Deep Learning Generative models generate new data that resembles the training data. The key types of generative models include: Generative Adversarial Networks (GANs) Autoencoders GAN vs. Transformer ModelsTypes of Generative Adversarial Networks (GANs): GANs consist of two neural networks, the generator and the discriminator that compete with each other. Variants of GANs include:Deep Convolutional GAN (DCGAN)Conditional GAN (cGAN)Cycle-Consistent GAN (CycleGAN)Super-Resolution GAN (SRGAN)StyleGAN Types of Autoencoders: Autoencoders are neural networks used for unsupervised learning that learns to compress and reconstruct data. Various types of Autoencoders include:Types of AutoencodersSparse AutoencoderDenoising AutoencoderConvolutional AutoencoderVariational Autoencoder4. Deep Reinforcement Learning (DRL)Deep Reinforcement Learning combines the representation learning power of deep learning with the decision-making ability of reinforcement learning. It helps agents to learn optimal behaviors in complex environments through trial and error using high-dimensional sensory inputs.Deep Reinforcement LearningReinforcement Learning Markov Decision Processes Key Algorithms in Deep Reinforcement LearningDeep Q-Networks (DQN)REINFORCE Actor-Critic Methods Proximal Policy Optimization (PPO) Advantages and Disadvantages of Deep LearningAdvantages:High accuracy and automation in complex tasks.Automatic feature extraction from data.Disadvantages:Needs large datasets and computational power.Complex architecture and training process.For more details you can refer to: Advantages and disadvantages of Deep LearningChallenges in Deep LearningData Requirements: Requires large datasets for training.Computational Resources: Needs powerful hardware.Interpretability: Models are hard to interpret.Overfitting: Risk of poor generalization to new data.For more details you can refer to: Challenges in Deep LearningPractical Applications of Deep Learning Self-Driving Cars: Recognize objects and navigate roads.Medical Diagnostics: Analyze medical images for disease detection.Speech Recognition: Power virtual assistants like Siri and Alexa.Facial Recognition: Identify individuals in images/videos.Recommendation Systems: Suggest personalized content (Netflix, Amazon).For more details you can refer to: Practical ApplicationsThis Deep Learning tutorial is for both beginners and experienced learners. Whether you're just starting out or want to expand your knowledge, this tutorial will help you understand the key concepts and techniques in Deep Learning. Comment More infoAdvertise with us Next Article Introduction to Deep Learning A abhishek1 Follow Improve Article Tags : Deep Learning AI-ML-DS Tutorials AI-ML-DS With Python Similar Reads Deep Learning Tutorial Deep Learning is a subset of Artificial Intelligence (AI) that helps machines to learn from large datasets using multi-layered neural networks. It automatically finds patterns and makes predictions and eliminates the need for manual feature extraction. Deep Learning tutorial covers the basics to adv 5 min read Deep Learning BasicsIntroduction to Deep LearningDeep Learning is transforming the way machines understand, learn and interact with complex data. Deep learning mimics neural networks of the human brain, it enables computers to autonomously uncover patterns and make informed decisions from vast amounts of unstructured data. How Deep Learning Works? 7 min read Artificial intelligence vs Machine Learning vs Deep LearningNowadays many misconceptions are there related to the words machine learning, deep learning, and artificial intelligence (AI), most people think all these things are the same whenever they hear the word AI, they directly relate that word to machine learning or vice versa, well yes, these things are 4 min read Deep Learning Examples: Practical Applications in Real LifeDeep learning is a branch of artificial intelligence (AI) that uses algorithms inspired by how the human brain works. It helps computers learn from large amounts of data and make smart decisions. Deep learning is behind many technologies we use every day like voice assistants and medical tools.This 3 min read Challenges in Deep LearningDeep learning, a branch of artificial intelligence, uses neural networks to analyze and learn from large datasets. It powers advancements in image recognition, natural language processing, and autonomous systems. Despite its impressive capabilities, deep learning is not without its challenges. It in 7 min read Why Deep Learning is ImportantDeep learning has emerged as one of the most transformative technologies of our time, revolutionizing numerous fields from computer vision to natural language processing. Its significance extends far beyond just improving predictive accuracy; it has reshaped entire industries and opened up new possi 5 min read Neural Networks BasicsWhat is a Neural Network?Neural networks are machine learning models that mimic the complex functions of the human brain. These models consist of interconnected nodes or neurons that process data, learn patterns and enable tasks such as pattern recognition and decision-making.In this article, we will explore the fundamental 12 min read Types of Neural NetworksNeural networks are computational models that mimic the way biological neural networks in the human brain process information. They consist of layers of neurons that transform the input data into meaningful outputs through a series of mathematical operations. In this article, we are going to explore 7 min read Layers in Artificial Neural Networks (ANN)In Artificial Neural Networks (ANNs), data flows from the input layer to the output layer through one or more hidden layers. Each layer consists of neurons that receive input, process it, and pass the output to the next layer. The layers work together to extract features, transform data, and make pr 4 min read Activation functions in Neural NetworksWhile building a neural network, one key decision is selecting the Activation Function for both the hidden layer and the output layer. It is a mathematical function applied to the output of a neuron. It introduces non-linearity into the model, allowing the network to learn and represent complex patt 8 min read Feedforward Neural NetworkFeedforward Neural Network (FNN) is a type of artificial neural network in which information flows in a single direction—from the input layer through hidden layers to the output layer—without loops or feedback. It is mainly used for pattern recognition tasks like image and speech classification.For 6 min read Backpropagation in Neural NetworkBack Propagation is also known as "Backward Propagation of Errors" is a method used to train neural network . Its goal is to reduce the difference between the model’s predicted output and the actual output by adjusting the weights and biases in the network.It works iteratively to adjust weights and 9 min read Deep Learning ModelsConvolutional Neural Network (CNN) in Machine LearningConvolutional Neural Networks (CNNs) are deep learning models designed to process data with a grid-like topology such as images. They are the foundation for most modern computer vision applications to detect features within visual data.Key Components of a Convolutional Neural NetworkConvolutional La 6 min read Introduction to Recurrent Neural NetworksRecurrent Neural Networks (RNNs) differ from regular neural networks in how they process information. While standard neural networks pass information in one direction i.e from input to output, RNNs feed information back into the network at each step.Lets understand RNN with a example:Imagine reading 10 min read What is LSTM - Long Short Term Memory?Long Short-Term Memory (LSTM) is an enhanced version of the Recurrent Neural Network (RNN) designed by Hochreiter and Schmidhuber. LSTMs can capture long-term dependencies in sequential data making them ideal for tasks like language translation, speech recognition and time series forecasting. Unlike 5 min read Gated Recurrent Unit NetworksIn machine learning Recurrent Neural Networks (RNNs) are essential for tasks involving sequential data such as text, speech and time-series analysis. While traditional RNNs struggle with capturing long-term dependencies due to the vanishing gradient problem architectures like Long Short-Term Memory 6 min read Transformers in Machine LearningTransformer is a neural network architecture used for performing machine learning tasks particularly in natural language processing (NLP) and computer vision. In 2017 Vaswani et al. published a paper " Attention is All You Need" in which the transformers architecture was introduced. The article expl 4 min read Autoencoders in Machine LearningAutoencoders are a special type of neural networks that learn to compress data into a compact form and then reconstruct it to closely match the original input. They consist of an:Encoder that captures important features by reducing dimensionality.Decoder that rebuilds the data from this compressed r 8 min read Generative Adversarial Network (GAN)Generative Adversarial Networks (GANs) help machines to create new, realistic data by learning from existing examples. It is introduced by Ian Goodfellow and his team in 2014 and they have transformed how computers generate images, videos, music and more. Unlike traditional models that only recogniz 12 min read Deep Learning FrameworksTensorFlow TutorialTensorFlow is an open-source machine-learning framework developed by Google. It is written in Python, making it accessible and easy to understand. It is designed to build and train machine learning (ML) and deep learning models. It is highly scalable for both research and production.It supports CPUs 2 min read Keras TutorialKeras high-level neural networks APIs that provide easy and efficient design and training of deep learning models. It is built on top of powerful frameworks like TensorFlow, making it both highly flexible and accessible. Keras has a simple and user-friendly interface, making it ideal for both beginn 3 min read PyTorch TutorialPyTorch is an open-source deep learning framework designed to simplify the process of building neural networks and machine learning models. With its dynamic computation graph, PyTorch allows developers to modify the network’s behavior in real-time, making it an excellent choice for both beginners an 7 min read Caffe : Deep Learning FrameworkCaffe (Convolutional Architecture for Fast Feature Embedding) is an open-source deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) to assist developers in creating, training, testing, and deploying deep neural networks. It provides a valuable medium for enhancing com 8 min read Apache MXNet: The Scalable and Flexible Deep Learning FrameworkIn the ever-evolving landscape of artificial intelligence and deep learning, selecting the right framework for building and deploying models is crucial for performance, scalability, and ease of development. Apache MXNet, an open-source deep learning framework, stands out by offering flexibility, sca 6 min read Theano in PythonTheano is a Python library that allows us to evaluate mathematical operations including multi-dimensional arrays efficiently. It is mostly used in building Deep Learning Projects. Theano works way faster on the Graphics Processing Unit (GPU) rather than on the CPU. This article will help you to unde 4 min read Model EvaluationGradient Descent Algorithm in Machine LearningGradient descent is the backbone of the learning process for various algorithms, including linear regression, logistic regression, support vector machines, and neural networks which serves as a fundamental optimization technique to minimize the cost function of a model by iteratively adjusting the m 15+ min read Momentum-based Gradient Optimizer - MLMomentum-based gradient optimizers are used to optimize the training of machine learning models. They are more advanced than the classic gradient descent method and helps to accelerate the training process especially for large-scale datasets and deep neural networks.By incorporating a "momentum" ter 4 min read Adagrad Optimizer in Deep LearningAdagrad is an abbreviation for Adaptive Gradient Algorithm. It is an adaptive learning rate optimization algorithm used for training deep learning models. It is particularly effective for sparse data or scenarios where features exhibit a large variation in magnitude.Adagrad adjusts the learning rate 6 min read RMSProp Optimizer in Deep LearningRMSProp (Root Mean Square Propagation) is an adaptive learning rate optimization algorithm designed to improve the performance and speed of training deep learning models.It is a variant of the gradient descent algorithm which adapts the learning rate for each parameter individually by considering th 5 min read What is Adam Optimizer?Adam (Adaptive Moment Estimation) optimizer combines the advantages of Momentum and RMSprop techniques to adjust learning rates during training. It works well with large datasets and complex models because it uses memory efficiently and adapts the learning rate for each parameter automatically.How D 4 min read Deep Learning ProjectsLung Cancer Detection using Convolutional Neural Network (CNN)Computer Vision is one of the applications of deep neural networks and one such use case is in predicting the presence of cancerous cells. In this article, we will learn how to build a classifier using Convolution Neural Network which can classify normal lung tissues from cancerous tissues.The follo 7 min read Cat & Dog Classification using Convolutional Neural Network in PythonConvolutional Neural Networks (CNNs) are a type of deep learning model specifically designed for processing images. Unlike traditional neural networks CNNs uses convolutional layers to automatically and efficiently extract features such as edges, textures and patterns from images. This makes them hi 5 min read Sentiment Analysis with an Recurrent Neural Networks (RNN)Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews 5 min read Text Generation using Recurrent Long Short Term Memory NetworkLSTMs are a type of neural network that are well-suited for tasks involving sequential data such as text generation. They are particularly useful because they can remember long-term dependencies in the data which is crucial when dealing with text that often has context that spans over multiple words 4 min read Machine Translation with Transformer in PythonMachine translation means converting text from one language into another. Tools like Google Translate use this technology. Many translation systems use transformer models which are good at understanding the meaning of sentences. In this article, we will see how to fine-tune a Transformer model from 6 min read Deep Learning Interview QuestionsDeep learning is a part of machine learning that is based on the artificial neural network with multiple layers to learn from and make predictions on data. An artificial neural network is based on the structure and working of the Biological neuron which is found in the brain. Deep Learning Interview 15+ min read Like