Deep learning is a branch of machine learning that involves training neural networks with multiple layers to detect patterns in data without the need for human-engineered features. The definition of a deep neural network is one which has at least two hidden layers between its input and output layers. Each successive layer extracts an increasingly abstract interpretation of the inputs, ranging from simple pixels, to lines, to shapes, to entire objects, by means of a set of weights that the network learns automatically via backpropagation.
The present tutorial is arranged in directories: 12 in all, each one covering 60+ topics in total. If you’re completely new to the topic, start with Neural Network Fundamentals; otherwise, dive right into the relevant category.
What Will I Learn?
How Are Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI Related?
Deep learning is a part of machine learning, which in turn is a subset of artificial intelligence. Generative AI is a part of deep learning that is specially designed for content creation.
| Term | Defining Attribute | Example Systems |
|---|---|---|
| Artificial Intelligence | Any system performing a task associated with human reasoning | Rule-based systems, search algorithms, deep learning models |
| Machine Learning | Learns statistical patterns from data instead of explicit rules | Linear regression, decision trees, support vector machines |
| Deep Learning | Uses neural networks with multiple hidden layers to learn complex patterns | CNNs, RNNs, Transformers |
| Generative AI | AI models that generate new text, images, audio, video, or code | GPT models, DALL-E, Stable Diffusion |
Recommended Learning Path
Follow these six stages in order for a structured first pass through deep learning. Each stage links to the category section below it.
- Basics of Deep Learning — definitions, terminology, and how deep learning differs from machine learning
- Neural Network Fundamentals — the mathematical building blocks every architecture uses
- Optimization Algorithms and Regularization & Hyperparameter Tuning — how networks are trained correctly
- Convolutional Neural Networks and CNN Architectures — image data
- Recurrent Neural Networks and Transformers & Attention — sequential and language data
- Generative Deep Learning, Deep Reinforcement Learning, and Deep Learning Frameworks — advanced topics and implementation tools
1. Basics of Deep Learning
Deep learning removes the manual feature-engineering step that traditional machine learning requires — a network learns which features matter directly from training data. The field moved from academic research to industry standard in 2012, when the AlexNet convolutional neural network reduced the ImageNet image-classification error rate by more than 10 percentage points against the next-best method.
- What Is Deep Learning?
- Deep Learning vs Machine Learning vs AI vs Generative AI
- How Deep Learning Works
- History and Timeline of Deep Learning
- Types of Deep Learning Models
2. Neural Network Fundamentals
Neural networks comprise artificial neurons, which solve a single mathematical equation each: multiplying inputs with weights learned for them, adding bias terms learned during training, and then applying an activation function to the output. The backpropagation algorithm, invented by Rumelhart, Hinton, and Williams in 1986, learns all the weights needed in a neural network.
- Artificial Neuron Explained
- Weights and Biases in Neural Networks
- Activation Functions
- Forward Propagation
- Loss Functions in Deep Learning
- Backpropagation Explained With Example
- Learning Rate in Deep Learning
- Perceptron Model
3. Optimization Algorithms
The optimizer changes the weights of the network each time backpropagation is done, based on the calculated gradient to determine the amount and direction the weight must be changed. The Adam optimizer was developed in 2014 by Kingma and Ba, and it utilizes momentum along with adaptive learning rates.
- Gradient Descent
- Stochastic Gradient Descent (SGD)
- Mini-Batch Gradient Descent
- Momentum Optimizer
- Adagrad Optimizer
- RMSProp Optimizer
- Adam Optimizer
4. Regularization & Hyperparameter Tuning
Regularization approaches prevent overfitting, which is a condition where a neural network becomes too familiar with the training data set rather than developing the pattern generalizing abilities. The method known as “dropout” was proposed by Srivastava et al. in 2014 and is a very popular regularization approach.
- Dropout
- Batch Normalization
- Early Stopping
- L1 and L2 Regularization
- Epoch and Batch Size Explained
- Hyperparameter Tuning
5. Convolutional Neural Networks (CNN)
A convolutional neural network uses learnable filters to scan through an image for detecting spatial features like edges and textures, and further stacking of filters in deeper layers allows the detection of more complex shapes. The convolutional neural network is the most widely used architecture for image recognition and processing.
- Introduction to CNN
- Padding in CNN
- Convolutional Layers
- Pooling Layers
- Fully Connected Layers
- Backpropagation in CNN
- Building a CNN Using PyTorch
- Building a CNN Using TensorFlow
- Image Classification With CNN
6. CNN Architectures
Each of the architectures mentioned above addressed one specific flaw present in its previous iteration. While LeNet-5 (LeCun et al., 1998) required around 60,000 parameters for classification of digits, AlexNet (Krizhevsky et al., 2012) increased the number of parameters up to 60 million and the number of layers to 8; ResNet (He et al., 2015) added skip connections to create networks with over 100 layers.
- LeNet-5
- AlexNet
- VGG-16 and VGG-19
- GoogLeNet (Inception)
- ResNet (Residual Network)
- MobileNet
- U-Net
- Vision Transformer (ViT)
7. Recurrent Neural Networks (RNN)
The Recurrent Neural Network is able to process sequences of data because the output at every time step is fed as an input at the next time step to create memory for the sequence. Traditional RNNs fail to have information from a large number of time steps ago due to which LSTM (Hochreiter & Schmidhuber, 1997) uses three gates to control the information.
- Introduction to RNN
- How RNN Differs From Feedforward Neural Networks
- Backpropagation Through Time (BPTT)
- Vanishing Gradient Problem
- Bidirectional RNN
- Long Short-Term Memory (LSTM)
- Bidirectional LSTM (Bi-LSTM)
- Gated Recurrent Unit (GRU)
8. Transformers & Attention
The transformer is a neural network system that can work with a whole sequence at once via a process called self-attention, which determines how much each item in a sequence affects all the other items. The transformer was proposed by Vaswani et al. in the 2017 paper “Attention Is All You Need” and constitutes the backbone of all major large language models since 2018.
- Attention Mechanism Explained
- Self-Attention Explained
- Transformer Architecture
- BERT Explained
- GPT Architecture Explained
9. Generative Deep Learning
Generative architecture training focuses on generation of data as opposed to classifying existing data. In a GAN (Goodfellow et al., 2014), there is training of two neural networks against each other until a generator is able to generate data that the discriminator cannot tell apart from real data. Diffusion models (Ho et al., 2020) generate new data by reversing addition of noise.
- Autoencoders
- Types of Autoencoders
- Generative Adversarial Network (GAN)
- Diffusion Models
- Encoder-Decoder Models
- Seq2Seq Model
- Transfer Learning
10. Deep Reinforcement Learning
The concept of deep reinforcement learning is an integration of deep neural networks with reinforcement learning, whereby an agent learns the action to perform in a particular state through maximization of the numerical reward signal. Deep Q-Networks were the first deep reinforcement learning algorithm to achieve human level performance in playing Atari video games solely through pixels.
- Reinforcement Learning Basics
- Deep Reinforcement Learning
- Markov Decision Process (MDP)
- Deep Q-Networks (DQN)
- Policy Gradient Methods
- REINFORCE Algorithm
- Actor-Critic Methods
- Proximal Policy Optimization (PPO)
11. Deep Learning Frameworks
There are four deep learning frameworks that are currently being used most extensively: TensorFlow (Google, 2015), PyTorch (Meta AI, 2016), Keras (François Chollet, 2015), and JAX (Google, 2018). The framework Keras has been developed to offer a more user-friendly environment that works on top of TensorFlow, PyTorch, or JAX and serves as an entry.
- TensorFlow Tutorial
- PyTorch Tutorial
- Keras Tutorial
- JAX Tutorial
- TensorFlow vs PyTorch vs Keras vs JAX
12. Practice & Career
Practicing with real-world data using all the concepts discussed above will make your theoretical understanding useful and practical. The two pages below will be very helpful for practice and interviews. The course page will offer structured learning and mentorship in all topics of this tutorial.
- Deep Learning Projects for Beginners
- Deep Learning Interview Questions
- Gyansetu Deep Learning Course — structured training covering neural network fundamentals through CNNs, RNNs, Transformers, and deployment, with real-world projects and placement support
Frequently Asked Questions
What is deep learning in simple terms?
Deep learning is an approach that involves training multi-layer neural networks to discover the patterns within the data automatically without defining the patterns manually.
Where should a beginner start in this tutorial?
It starts with Neural Network Fundamentals – every other category relies on those: the concepts of weights, activation function, and backpropagation.
What is the difference between deep learning and machine learning?
Deep learning is a particular type of machine learning based on the use of neural networks with at least two hidden layers, while other types of machine learning do not include neural networks (e.g., decision trees).
Which deep learning architecture should I learn first?
After learning the fundamentals, convolutional neural networks are the de facto standard architecture to start with because it is easier to comprehend the image-based tasks than sequential or generative.
Do I need a GPU to follow this tutorial?
No. You can learn and implement Neural Network Fundamentals, Optimization, and Regularization categories without a GPU; it becomes necessary only in larger CNNs and Transformer architectures.
