Deep Learning Tutorial for Beginners

Starting a deep learning career can feel daunting due to the complex math, vast frameworks, and the sheer volume of information. Many beginners struggle with where to begin, what tools to learn, and how to bridge the gap between theory and practical application. 

This deep learning tutorial for beginners will cut through the noise, providing a clear path to understanding core concepts and building your first models. Ready to transform your career? Check out our in-depth Deep Learning course syllabus today and begin your AI adventure!

Introduction to Deep Learning: The AI Revolution

Deep learning is a revolutionary subset of machine learning and deep learning that’s powering today’s AI revolution. It’s responsible for most of the amazing breakthroughs we’re witnessing in artificial intelligence deep learning, from autonomous vehicles and face recognition to smart voice assistants and complex recommendation systems. 

Deep learning applies artificial neural networks (ANNs), which are based on the structure and function of the human brain, to learn complex patterns from large volumes of data.

In contrast to older machine learning algorithms that tend to need manual feature engineering (i.e., instructing the model on what in the data is significant), deep learning models can learn to discover and extract features on their own from raw data. 

Machine Learning vs. Deep Learning: What’s the Difference?

Although sometimes used interchangeably, machine learning and deep learning are not the same. Consider it this way: deep learning is a subset of machine learning.

• Machine Learning (ML): A larger category of algorithms that learn from data to predict or decide. It encompasses a vast array of algorithms such as linear regression, decision trees, support vector machines (SVMs), and conventional clustering algorithms. ML tends to need human assistance to choose and engineer corresponding features from the data.
• Deep Learning (DL): A branch of ML that employs multi-layered artificial neural networks to learn representations from data. The “deep” is used to denote the many hidden layers within these networks. DL is good at handling complicated, unstructured data such as images, sound, and text since it is able to extract features automatically without being programmed explicitly.

Feature	Machine Learning (ML)	Deep Learning (DL)
Feature Engineering	Manual, human-driven	Automatic, learned by the network
Data Requirements	Can work with smaller datasets	Typically requires large datasets for optimal performance
Computational Power	Less intensive	Highly intensive, often requires GPUs/TPUs
Interpretation	More interpretable (often “white box”)	Less interpretable (often “black box”)
Applications	Broader range, structured data	Excels with unstructured data (images, text, audio)

Recommended: Artificial Intelligence Tutorial for Beginners.

The Building Blocks: Artificial Neural Networks

The artificial neural network (ANN) is the basic idea behind deep learning. An ANN is a computer model based on the biological neural networks of the human brain. It is composed of interconnected nodes, or “neurons,” grouped into layers.

Structure of a Neural Network

A standard feedforward neural network contains three principal types of layers:

1. Input Layer: This is where data comes into the network. Every node in the input layer corresponds to a feature from your data set. So, for instance, if you’re classifying images, each input node could be a pixel value.
2. Hidden Layers: These are the “deep” ones where magic occurs. Every hidden layer has several neurons that take inputs from the preceding layer, compute (weighted sum and activation), and pass the output to the subsequent layer. The higher the number of hidden layers, the more complicated patterns the network is capable of learning.
3. Output Layer: This layer creates the network’s final prediction or output. The output layer’s number of neurons varies with the task:

• For binary classification (eg, spam/not spam), you can have one output neuron.
• For multi-class classification (for instance, classifying images into 10 classes), you’d have 10 output neurons.
• For regression tasks (for instance, estimating house prices), you’d have one output neuron.

How a Neuron Works: The Basic Unit

Every artificial neuron computes a basic set of operations:

1. Weighted Sum: It takes inputs from the neurons of the prior layer. Every input is weighted with a corresponding weight, which is the strength of that connection. The weighted inputs are added together and a bias term is added.

z = i = 1n(wi.xi) + b

Where:

• z is the weighted sum.
• xi are the inputs.
• wi are the weights.
• b is the bias.

2. Activation Function: The sum z is then fed into an activation function. This adds non-linearity to the network so it can learn complex, non-linear relationships between the data. If activation functions were not used, a neural network would just be a linear model, even if it’s a very complex one with lots of layers. Some popular activation functions are:

• Sigmoid: Squashes values to between 0 and 1. Good for output layers in binary classification.
• ReLU (Rectified Linear Unit): Outputs x if x0, else outputs 0. Used most often in hidden layers because it is computationally efficient and can prevent vanishing gradients.
• Softmax: Maps a vector of numbers to a distribution of probabilities. Best used in output layers for multi-class classification.

Suggested: Machine Learning Online Course.

Training a Neural Network: Learning from Data

Training a neural network is a process of iteration wherein the network adapts its weights and biases to reduce the gap between its predictions and the target values. This process includes:

• Forward Propagation: Input data travels through the network, from the input layer, through the hidden layers, to the output layer, creating a prediction.
• Loss Function (Cost Function): This calculates the difference between the prediction made by the network and the actual label. Training aims to minimize this loss. Some examples are Mean Squared Error (MSE) for regression and Cross-Entropy for classification.
• Backpropagation: The main algorithm used to train ANNs is this one. It calculates the gradients of the loss function with respect to each weight and bias in the network, working backward from the output layer to the input layer. These gradients show the relative contributions of each bias and weight to the total error.
• Optimization Algorithm (e.g., Gradient Descent): With the gradients found by backpropagation, an optimizer (such as Gradient Descent, Adam, RMSprop) updates the weights and the biases towards the direction that minimizes the loss. “Learning rate” is a key hyperparameter specifying the magnitude of these updates.

Example: Simple Neural Network in PyTorch

Let’s implement a simple neural network using PyTorch. PyTorch is an open-source machine learning library that is well-liked for deep learning development and research owing to its flexibility and Pythonic interface.

import torch

import torch.nn as nn

import torch.optim as optim

import numpy as np

# 1. Prepare some dummy data

# Let’s say we want to predict y from x

# y = 2*x + 1, with some noise

X = torch.randn(100, 1) * 10 # 100 samples, 1 feature

y = 2 * X + 1 + torch.randn(100, 1) * 2 # Target with noise

# 2. Define the Neural Network

# We’ll create a simple one-hidden-layer neural network

class SimpleNN(nn.Module):

    def __init__(self):

        super(SimpleNN, self).__init__()

        # Input layer (1 feature) to hidden layer (5 neurons)

        self.hidden = nn.Linear(1, 5)

        # Hidden layer (5 neurons) to output layer (1 output)

        self.output = nn.Linear(5, 1)

        # Activation function

        self.relu = nn.ReLU()

    def forward(self, x):

        x = self.hidden(x)

        x = self.relu(x)

        x = self.output(x)

        return x

# Instantiate the model

model = SimpleNN()

# 3. Define Loss Function and Optimizer

criterion = nn.MSELoss() # Mean Squared Error for regression

optimizer = optim.SGD(model.parameters(), lr=0.01) # Stochastic Gradient Descent

# 4. Training Loop

num_epochs = 1000

for epoch in range(num_epochs):

    # Forward pass

    outputs = model(X)

    loss = criterion(outputs, y)

    # Backward and optimize

    optimizer.zero_grad() # Clear gradients from previous step

    loss.backward()       # Compute gradients

    optimizer.step()      # Update weights

    if (epoch+1) % 100 == 0:

        print(f’Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}’)

# 5. Make a prediction

test_x = torch.tensor([[5.0]])

predicted_y = model(test_x)

print(f’Predicted y for x=5: {predicted_y.item():.4f}’)

print(f’Actual y for x=5 (approx): {2*5 + 1}’)

Explore: AI Scientist Salary for Freshers.

Types of Deep Learning Architectures

Even if the basic ANN is the foundation, deep learning uses advanced architectures for specific types of data and tasks.

Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are the powerhouses behind computer vision, with capabilities like image classification, object detection, and face recognition. Their design borrows from the animal visual cortex’s anatomy.

Key Components of a CNN:

Convolutional Layer: The most basic building block. It convolves the input image with a collection of learnable filters (also referred to as kernels). Each filter sweeps through the image, calculating a dot product with the pixels below it, and producing a feature map. These filters learn to detect specific features like edges, textures, or shapes.

• Filters/Kernels: Small matrices that travel over the input.
• Stride: The amount of distance the filter moves at each step.
• Padding: Adding zeros to the boundary of the input image for controlling the size of the output and preserving information on the boundary.

Activation Function (ReLU): Applied element-wise to the convolution layer’s output to introduce non-linearity.

Pooling Layer (Downsampling): Reduces the spatial dimensions (width and height) of the feature maps, which is done to:

• Reduce computational complexity.
• Make the features robust to small position changes (translation invariance).
• Common ones are Max Pooling (gives the maximum value within a window) and Average Pooling.

Fully Connected (Dense) Layers: The high-level features extracted through several convolutional and pooling layers are converted to a 1D vector and fed into one or more fully connected layers. The fully connected layers are similar to the ordinary neural network layers and perform the final classification or regression on the learned features.

Why CNNs Work Well for Images:

• Sharing of Parameters: A filter applied to one part of an image is also applied to other parts. This has a significant impact on how many parameters the network has to learn, making it efficient.
• Local Receptive Fields: Each neuron in a convolutional layer only receives a small patch of input, which mimics the way human vision perceives local patterns prior to combining them.
• Translation Invariance: Because filters are scanning the entire image, a CNN can detect a feature regardless of its precise position in the image.

Example: Simple CNN for Image Classification (Conceptual)

import torch

import torch.nn as nn

import torch.nn.functional as F

import torchvision

import torchvision.transforms as transforms

# Assuming you have a dataset like CIFAR-10

# transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

# trainset = torchvision.datasets.CIFAR10(root=’./data’, train=True, download=True, transform=transform)

# trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)

class SimpleCNN(nn.Module):

    def __init__(self):

        super(SimpleCNN, self).__init__()

        # Input: 3 channels (RGB), Output: 6 channels, Kernel size: 5×5

        self.conv1 = nn.Conv2d(3, 6, 5)

        # Max pooling over a 2×2 window

        self.pool = nn.MaxPool2d(2, 2)

        # Input: 6 channels, Output: 16 channels, Kernel size: 5×5

        self.conv2 = nn.Conv2d(6, 16, 5)

        # Fully connected layers

        # The input size to fc1 depends on the output size of conv2 and pool

        # Assuming input image size is 32×32, after two conv+pool layers:

        # 32 -> (32-5+1)/1 + 1 = 28 -> 28/2 = 14

        # 14 -> (14-5+1)/1 + 1 = 10 -> 10/2 = 5

        # So, 16 channels * 5 * 5 = 400

        self.fc1 = nn.Linear(16 * 5 * 5, 120)

        self.fc2 = nn.Linear(120, 84)

        self.fc3 = nn.Linear(84, 10) # 10 classes for CIFAR-10

    def forward(self, x):

        x = self.pool(F.relu(self.conv1(x)))

        x = self.pool(F.relu(self.conv2(x)))

        x = torch.flatten(x, 1) # Flatten all dimensions except batch

        x = F.relu(self.fc1(x))

        x = F.relu(self.fc2(x))

        x = self.fc3(x)

        return x

# model = SimpleCNN()

# # Training would proceed similarly to the simple NN example, but with image data

Explore: Deep Learning Online Course.

Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) are used to handle sequential data, where the sequence of elements is important. Examples of such tasks are natural language processing (NLP), speech recognition, and time series forecasting. Unlike feedforward networks, RNNs have “memory” – they can use past inputs in the sequence while processing the current input. 

How RNNs Work

• Hidden State: An RNN has a hidden state, which is modified in every step of the sequence. The hidden state, in effect, stores information from past inputs.
• Recurrent Connection: The hidden layer’s output at one time step is used as an input to the hidden layer at the next time step. It is this recurrent connection that provides RNNs with their memory.

Challenges with Simple RNNs:

• Vanishing Gradient Problem: Gradients become very small when passed through several time steps in backpropagation, preventing the network from learning long-range relationships.
• Exploding Gradient Problem: Gradients become very large, causing the training to become unstable.

Solutions: LSTMs and GRUs

To solve the vanishing/exploding gradient problems, more sophisticated RNN architectures were introduced:

• Long Short-Term Memory (LSTM) Networks: LSTMs add “gates” (input, forget, and output gates) that control the information flow into and out of the cell state, so they can selectively remember or forget something over long sequences.
• Gated Recurrent Units (GRUs): GRUs are a less complex variant of LSTMs with fewer gates (update and reset gates) and provide a good trade-off between performance and computational cost.

Example: Simple RNN (LSTM) for Text Classification (Conceptual)

import torch

import torch.nn as nn

# Example: simple sentiment analysis

# Input: sequence of word embeddings, Output: positive/negative sentiment

class LSTMTextClassifier(nn.Module):

    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, num_layers):

        super(LSTMTextClassifier, self).__init__()

        self.embedding = nn.Embedding(vocab_size, embedding_dim)

        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=num_layers, batch_first=True)

        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, text):

        # text = [batch size, sequence len]

        embedded = self.embedding(text)

        # embedded = [batch size, sequence len, embedding dim]

        output, (hidden, cell) = self.lstm(embedded)

        # output = [batch size, sequence len, hidden dim * num directions]

        # hidden = [num layers * num directions, batch size, hidden dim]

        # cell = [num layers * num directions, batch size, hidden dim]

        # Use the last hidden state for classification

        hidden_last = hidden[-1,:,:] # For single-direction LSTM

        return self.fc(hidden_last)

# # Example usage:

# vocab_size = 10000 # Number of unique words

# embedding_dim = 100

# hidden_dim = 256

# output_dim = 1 # For binary sentiment (positive/negative)

# num_layers = 2

# model_lstm = LSTMTextClassifier(vocab_size, embedding_dim, hidden_dim, output_dim, num_layers)

# # Training would involve processing text sequences and their labels

Recommended: Artificial Intelligence Course Online.

Deep Learning Frameworks: PyTorch and TensorFlow

To train and use deep learning models, developers utilize robust open-source frameworks. The two most well-known are PyTorch and TensorFlow. Both of them offer a deep range of tools, libraries, and APIs to make it easier to develop, train, and deploy deep neural networks.

PyTorch

Released by: Facebook AI Research (FAIR).

Key Features:

• Dynamic Computation Graph: PyTorch has a “define-by-run” strategy where the graph is constructed dynamically when the operations are being run. This provides more flexibility when it comes to debugging and managing dynamic network structures.
• Pythonic Interface: It is reported to have an intuitive and Pythonic API, which makes it simple for Python developers to learn.
• Strong Community Support: A fast-growing community and dense documentation.
• Used for: Mostly used in research and quick prototyping owing to its versatility.

# Example of PyTorch’s dynamic graph (eager execution)

x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)

y = x * 2

z = y.mean()

z.backward() # Computes gradients on the fly

print(x.grad)

TensorFlow

Created by: Google Brain.

Major Features:

• Static Computation Graph (previously in older releases, now also eager execution): TensorFlow had long employed a “define-and-run” model whereby you define the whole computational graph upfront and then run it in a session. This provided deployment performance advantages but perhaps a less flexible model for development. In TensorFlow 2.x, eager execution (the analogue of PyTorch’s dynamic graph) is the default, balancing flexibility with performance.
• Keras API Integration: Keras is integrated as the high-level API by TensorFlow 2.x, which makes it extremely user-friendly for rapid building and training of models.
• Production Deployment: Robust ecosystem for scaling models (e.g., TensorFlow Serving, TensorFlow Lite for mobile/edge devices).
• Used for: Used in research as well as production environments, particularly in industrial applications of large scales.

import tensorflow as tf

# Example of TensorFlow’s eager execution (default in TF 2.x)

x = tf.constant([1.0, 2.0, 3.0])

with tf.GradientTape() as tape:

    tape.watch(x)

    y = x * 2

    z = tf.reduce_mean(y)

gradients = tape.gradient(z, x) # Computes gradients

print(gradients)

# Example using Keras with TensorFlow backend

# model = tf.keras.Sequential([

#     tf.keras.layers.Dense(5, activation=’relu’, input_shape=(1,)),

#     tf.keras.layers.Dense(1)

# ])

# model.compile(optimizer=’sgd’, loss=’mse’)

# # Training similar to PyTorch

# # model.fit(X_train, y_train, epochs=100)

Both PyTorch and TensorFlow are great options, and the choice really comes down to individual preference or project needs. Much of the concepts and methods can be translated between the two.

Recommended: Python Tutorial for Beginners.

Your Deep Learning Career Path: Getting Started

Beginning a career in deep learning can be rewarding and thrilling. This is your guide:

• Solid Fundamentals: A proper understanding of linear algebra, calculus, probability, and statistics goes a long way. You do not have to be a math whiz, but knowing the fundamentals will open up deeper knowledge.
• Programming Proficiency: Python is the language of choice for AI deep learning. Familiarize yourself with its libraries like NumPy, Pandas, Matplotlib, and scikit-learn.
• Understand ML First: While deep learning is powerful, traditional machine learning concepts (supervised, unsupervised learning, common algorithms) provide essential context.
• Hands-on Projects: This is essential! Practice what you’ve learned by doing real-world datasets. Begin with simple projects (e.g., image classification on MNIST) and then move on to more complicated ones. Kaggle competitions are a great way to practice and learn from others.
• Framework Mastery: Master one of the top deep learning frameworks such as PyTorch or TensorFlow.
• Stay Current: The technology of deep learning AI is changing fast. Subscribe to researchers, blog-read (e.g., Towards Data Science), and monitor new papers.

Check Out: All Trending Software Courses.

Conclusion: Your Deep Learning Journey Awaits!

Deep learning is a revolutionary technology that keeps extending the limits of what AI can do. Though the initial learning curve may appear to be steep, with persistence and a systematic approach, you can learn its principles and join this fascinating field. Don’t forget to concentrate on the basics, learn through projects, and select a framework such as PyTorch or TensorFlow to implement your ideas.

Ready to dive deep and develop real-world AI deep learning implementations? Our detailed deep learning course in Chennai covers the mathematical foundations and basic artificial neural networks right through to the cutting-edge convolutional neural networks and implementation with PyTorch and TensorFlow. Don’t learn it, build it! Sign up today to take your AI career to the next level!