What is deep learning? A tutorial for beginners

Home/

Blog/

Machine Learning/

14 mins read

Oct 31, 2025

Content

What is deep learning?

Core architectures at a glance

How Does Deep Learning Work?

Deep Learning vs. Machine Learning

Deep learning tools

Deep Learning Languages

Deep Learning Libraries

Deep Learning Frameworks

Keep learning about deep learning.

Training workflow: from dataset to model you can trust

From perceptron to modern baselines

Deep learning practice: Perceptron

1. Boundary line

2. Discrete Prediction with Forward Propagation

3. Logistic Regression

4. Error Function: Cross-entropy

A tiny end-to-end upgrade you can try next

What deployment-minded beginners should know

What to learn next

Continue reading about deep learning

Deep learning (DL) is a machine learning method that allows computers to mimic the human brain, usually to complete classification tasks on images or non-visual data sets. Deep learning has recently become an industry-defining tool for its to advances in GPU technology.

Deep learning is now used in self-driving cars, fraud detection, artificial intelligence programs, and beyond. These technologies are in high demand, so deep learning data scientists and ML engineers being hired every day.

Today, we’ll help you take the first step toward those exciting careers. You’ll learn how deep learning works, why it’s become so popular, and teach you to implement your first deep learning model.

Let’s get started!

What is deep learning?#

Deep Learning (sometimes called Deep Structured Learning) is a type of machine learning algorithm based on Artificial Neural Network technology (ANN).

Deep learning and other ANN methods allow computers to learn by example in a similar way to the human brain. This is accomplished through passing input data through multiple levels of Neural Net processing to transform data and narrow the possible predictions each step along the way.

Deep learning algorithms have powerful advantages over other models like:

Unstructured data handling: Once trained with structured data, deep learning models can automatically make sense of unstructured data. This means businesses can plug all available data they have without formatting or standardizing it first.
Recognize unexpected patterns: Most models require engineers to select what pattern the ML algorithm will look for. Any correlations beyond those directly selected go undetected. Deep learning algorithms can track all correlations, even those not requested by engineers.
Unmatched accuracy: Deep learning delivers more accurate results and scales better with large data pools than other methods.

Deep learning is best suited to classification patterns that match input data to a learned type. DL methods are therefore often used for image recognition, speech recognition software, Natural Language Processing (NLP).

More recently, it’s been used to allow self-driving cars to detect signs and obstacles.

Core architectures at a glance#

A solid deep learning tutorial should briefly map today’s three pillar families. Convolutional neural networks process images with shared kernels that learn edges, textures, and shapes; they power tasks like classification, detection, and segmentation. Recurrent models (vanilla RNNs, LSTMs, GRUs) handle sequences such as time series and speech by carrying state across timesteps. Transformers replace recurrence with attention, letting models focus on the most relevant tokens or patches in parallel; they now dominate text, and increasingly vision and audio. Knowing when to use each makes it easier to translate problems into the right model family and helps you read research with purpose.

How Does Deep Learning Work?#

Deep learning learns to recognize what features all members of a type have through the analysis of structured training data.

The algorithm then analyzes each data point and recognizes similarities between all data points of the same label. This process is called feature extraction.

The algorithm then selects which of these features form the most accurate criteria for each label. This criterion is called the decision boundary.

Once the program has perfected these criteria using all available training data, it uses these learned criteria to classify unstructured input data into the previous labels.

For example, an engineer may pass in 10,000 photos, with 5,000 labeled elephant and another 5,000 labeled not elephant. The model will go through all 1000 pictures and pull out features shared by elephant pictures like “four-legged” or “trunk”.

It would learn that many creatures have 4 legs, therefore if a creature has four legs it may be an elephant. Conversely, only elephants have a trunk. The model can then predict that if a pictured animal has a trunk, it’s very likely an elephant.

The algorithm could then use these “trunk”, “four-legged” and other features to form a model that can assign elephant or not elephant labels to a different, unlabeled set of animal pictures.

Once this model is formed, we can even reuse it as a starting point for another similar deep learning algorithm. The process of reusing models is called transfer learning.

Comprehensive Training Data:

Our DL model can only be accurate if it is passed a variety of training data. Incorrect outcomes of a DL model are often caused by the training set rather than the model itself.

For example, the model would likely classify a wooly mammoth as an elephant if our training data didn’t include any pictures of wooly mammoths labeled not elephant.

The key to deep learning is the many hidden layers of processing the input data must go through.

Each layer contains multiple neurons or “nodes” with mathematical functions that collect and classify data. The first and final layers are the input and output layers.

Between them, there are hidden layers with nodes that take the results of previous classifications as input. These nodes run the previous findings through their own classification functions and adjust the weighting of the findings accordingly.

Traditional neural nets before deep learning would only pass data through 2-3 hidden layers before completion. Deep learning increases that number to up to 150 hidden layers to increase result accuracy.

Deep learning tools#

Deep Learning Languages#

Python: Python is the most commonly used language for all types of machine learning, not just deep learning. Over 55% of data scientists use Python as their primary language. This is because of Python’s many ML focused libraries and its easy-to-learn syntax.
Java: Java is the second most popular language for machine learning, primarily for ML-powered security protocols like classification-based fraud detection. Java is getting more machine learning tools with each version, such as new string and file methods added in Java 11.
R: R is a graphics-based language used for statistical analysis and visualization in machine learning. R is a great language to present and explore the results of ML algorithms in a graphical way. It’s especially popular for healthcare technology and biological study presentation.

Deep Learning Libraries#

TensorFlow: TensorFlow is an open-source library that focuses on training deep neural networks. It provides options to deploy ML models to the local device, on-prem database, or via the cloud. TensorFlow is essential to the modern Python data scientist because it allows tools to build and train ML models using the latest techniques.
Scikit-learn: Sklearn adds support for a variety of supervised or unsupervised learning algorithms, including deep learning. It is the most popular ML library for Python and allows various other libraries such as SciPy and Pandas to work well together.
Keras: Keras is an ML API that provides a Python interface for artificial neural networks (ANNs) and acts as an interface for TensorFlow. It enables fast experimentation with deep neural networks and provides commonly-used neural-network building blocks to speed up development.
NumPy: NumPy adds support for multidimensional arrays and matrices as well as complex statistical operations. These are essential for a variety of machine learning models.
Theano: Theano is an optimization tool used to manipulate and evaluate matrix-based computations. Theano is great for deep learning models as it automatically optimizes computations to run efficiently on GPUs.
PyTorch: PyTorch is an ML library developed by Facebook AI and based on the popular Torch library. PyTorch is primarily used for natural language processing and computer vision in companies like Tesla, Uber, and HuggingFace.

Training workflow: from dataset to model you can trust#

A practical deep learning tutorial also teaches the standard training loop:

Split and batch: Partition data into train/validation/test; use mini-batches for stable gradients and efficient hardware utilization.
Loss + optimizer: Choose a loss that fits the task (cross-entropy for classification, MSE for regression). Start with Adam or SGD+momentum; schedule the learning rate (step, cosine, or warmup + decay).
Regularize: Apply data augmentation, weight decay, dropout, and batch norm to reduce overfitting. Early stopping on validation metrics often saves time and cost.
Evaluate: Track not only accuracy but also precision/recall and F1 for imbalanced data; inspect a confusion matrix to spot systematic errors.
Reproducibility: Fix random seeds, log hyperparameters, save checkpoints, and record the exact data snapshot you trained on.
Transfer learning: Start from a pre-trained backbone and fine-tune your head layers—this typically beats training from scratch on small to medium datasets.

These habits mirror how professionals structure experiments in PyTorch and TensorFlow notebooks and will make every future project smoother.

From perceptron to modern baselines#

The perceptron is a great teaching tool, but most real tasks need deeper models. After the perceptron warm-up, build a small convolutional baseline (e.g., two conv blocks → global pooling → linear head) for images, or a tiny transformer encoder for text. Keep the first pass intentionally simple; an honest baseline plus clear metrics will teach you more than a complex stack you can’t train reliably.

Python

import numpy as np
def step(weighted_sum): # step activation function
    # The step activation is applied to the perceptron output that
    # returns 0 if the weighted sum is less than 0 and 1 otherwise
    return (weighted_sum > 0) * 1
 
def forward_propagation(input_data, weights, bias):
    #Computes the forward propagation operation of a perceptron 
    # and returns the output after applying the step activation function
  
   # takes the dot product of input and the weights and adds the bias
    return step(np.dot(input_data, weights) + bias) 
# Initialize parameters
X = np.array([2, 3]) # declaring two data points
Y = np.array([0]) # label
weights = np.array([2.0, 3.0]) # weights of perceptron
bias = 0.1 # bias value
Y_predicted = forward_propagation(X, weights.T, bias) # predicted label
print("Predicted label:", Y_predicted)

Code Explanation:

Call the forward_propagation function:

After the parameters are initialized, the forward propagation function is called.

forward_propagation function

Takes in the input variable X and weights, then it calculates the dot product using np.dot and adds the bias to compute the weighted sum.

Applies the step function to the computed weighted sum.

step function:

Takes the weighted sum and returns 1 if the value is greater than 0 and 0 otherwise.

Variables	Definition
`X`	An input NumPY array with feature values 2 and 3
`Y`	An output label with value 0

`weights`	The weights of the perceptron with initial values of 2 and 3, respectively.

`bias`	The bias value initialized with 0

Python

import numpy as np
def sigmoid(x):
  # The sigmoid activation function
  return 1 / (1 + np.exp(-x)) # applying the sigmoid function
def forward_propagation(input_data, weights, bias):
  
  #Computes the forward propagation operation of a perceptron and 
  #returns the output after applying the sigmoid activation function
  
  # take the dot product of input and weight and add the bias
  return sigmoid(np.dot(input_data, weights) + bias) # the perceptron equation
# Initializing parameters
X = np.array([2, 3]) # declaring two data points
Y = np.array([0]) # label
weights = np.array([2.0, 3.0]) # weights of perceptron
bias = 0.1 # bias value
output = forward_propagation(X, weights.T, bias) # predicted label
print("Forward propagation output:", output)
Y_predicted = (output > 0.5) * 1 ## apply sigmoid activation
print("Label:", Y_predicted)

4. Error Function: Cross-entropy#

Finally, we’ll implement an error function that compares the actual value and the predicted value of each point in our example.

Error functions are used to quantify the certainty of a prediction. For example, instead of simply having the logistically determined “yes” or “no”, we’ll be able to see how certain the program is in its prediction.

Cross-entropy is the error function used for classification models.

$E= -(y log(y') + (1-y)log(1-y'))$

Minimized cross-entropy indicates a maximum likelihood that a class belongs to the predicted type.

Python

import numpy as np
def sigmoid(x): 
    # The sigmoid activation function"""
    return 1 / (1 + np.exp(-x))
def forward_propagation(input_data, weights, bias):
   
    # Computes the forward propagation operation of a perceptron and 
    # returns the output after applying the sigmoid activation function
   
   # take the dot product of input and weight and add the bias
   return sigmoid(np.dot(input_data, weights) + bias) 
 
def calculate_error(y, y_predicted):
   #Computes the binary cross entropy error"""
   return - y * np.log(y_predicted) - (1 - y) * np.log(1 - y_predicted)
def ce_two_different_weights(X, Y, weights_0, weights_1, bias):
    #Computes sum of error using two different weights and the same bias"""
    sum_error1 = 0.0
    sum_error2 = 0.0
    for j in range(len(X)):
        Y_predicted_1 = forward_propagation(X[j], weights_0.T, bias) # predicted label
        sum_error1 = sum_error1 + calculate_error (Y[j], Y_predicted_1) # sum of error with weights_0
        Y_predicted_2 = forward_propagation(X[j], weights_1.T, bias) # predicted label
        sum_error2 = sum_error2 + calculate_error (Y[j], Y_predicted_2) # sum of error with weights_1
    return sum_error1, sum_error2
 
# Initialize parameters
X = np.array([[2, 3], [1, 4], [-1, -3], [-4, -5]]) # declaring two data points
Y = np.array([1.0, 1.0, 0.0, 0.0]) # actual label
weights_0 = np.array([0.0, 0.0]) # weights of perceptron
weights_1 = np.array([1.0, -1.0]) # weights of perceptron
bias = 0.0 # bias value
sum_error1, sum_error2 = ce_two_different_weights(X, Y, weights_0, weights_1, bias)
print("sum_error1:", sum_error1, "sum_error2:", sum_error2)

A tiny end-to-end upgrade you can try next#

To extend this deep learning tutorial beyond a single neuron:

Replace the single layer with a two-hidden-layer MLP and compare train vs validation curves to visualize overfitting.
Port the logic into a mainstream framework and use its autograd to compute gradients automatically; verify you can overfit a tiny subset in a few steps (a good sanity check).
Swap to a small CNN on a toy vision dataset and add simple augmentation (random crop/flip).
Try transfer learning: freeze a pre-trained backbone, train a classifier head, then unfreeze and fine-tune with a lower learning rate.
Log precision/recall and confusion matrices, save the best checkpoint on validation F1, and export a minimal inference script.

This turns the conceptual perceptron into a stepping stone toward practical, reproducible projects that reflect how deep learning is done in modern notebooks and tutorials.

What deployment-minded beginners should know#

Even in a beginner deep learning tutorial, it helps to note where you’re headed. Simple Flask or FastAPI endpoints wrap models for batch or real-time inference; quantization and pruning reduce latency and cost; and experiment tracking tools keep work auditable as datasets evolve. As you advance, try exporting to ONNX for portability and investigate hardware backends like GPUs and TPUs so you can match model size to available compute. These practices make the jump from notebook to production much less intimidating.

What to learn next#

Congratulations, you’ve now made a simple Perceptron classifier! You can now move onto other top deep learning projects like:

Letter Classification System
Face Detection System
Digit Recognition System
Music Genre Classification System

Classification is the most common use of deep learning so you’ll want to get as much practice with them as possible!

To help you along the way, Educative has created the course A Beginner’s Guide to Deep Learning. The course walks you through core concepts of deep learning at an approachable level. Then, you get the chance to practice each concept with a hands-on example.

By the end of the course, you’ll have the hands-on experience you need to start off your deep learning journey right.

Happy learning!

Continue reading about deep learning#

Written By:

Ryan Thelin

Free Resources

blog

Demystifying Fuzzy Inference Systems

blog

Introduction to convolutional neural networks (CNN)

blog

Bagging vs. Boosting in machine learning

What is deep learning? A tutorial for beginners

Learn deep learning with hands-on projects
Learn all the top machine learning techniques and tools without scrubbing through tutorial videos.

A Beginner’s Guide to Deep Learning

What is deep learning?#

Core architectures at a glance#

How Does Deep Learning Work?#

Deep Learning vs. Machine Learning#

Deep learning tools#

Deep Learning Languages#

Deep Learning Libraries#

Deep Learning Frameworks#

Keep learning about deep learning.#

Training workflow: from dataset to model you can trust#

From perceptron to modern baselines#

Deep learning practice: Perceptron#

1. Boundary line#

2. Discrete Prediction with Forward Propagation#

3. Logistic Regression#

4. Error Function: Cross-entropy#

A tiny end-to-end upgrade you can try next#

What deployment-minded beginners should know#

What to learn next#

Continue reading about deep learning#

What is deep learning? A tutorial for beginners

Learn deep learning with hands-on projects Learn all the top machine learning techniques and tools without scrubbing through tutorial videos. A Beginner’s Guide to Deep Learning

What is deep learning?#

Core architectures at a glance#

How Does Deep Learning Work?#

Deep Learning vs. Machine Learning#

Deep learning tools#

Deep Learning Languages#

Deep Learning Libraries#

Deep Learning Frameworks#

Keep learning about deep learning.#

Training workflow: from dataset to model you can trust#

From perceptron to modern baselines#

Deep learning practice: Perceptron#

1. Boundary line#

2. Discrete Prediction with Forward Propagation#

3. Logistic Regression#

4. Error Function: Cross-entropy#

A tiny end-to-end upgrade you can try next#

What deployment-minded beginners should know#

What to learn next#

Continue reading about deep learning#

Learn deep learning with hands-on projects
Learn all the top machine learning techniques and tools without scrubbing through tutorial videos.

A Beginner’s Guide to Deep Learning