Here is a comprehensive guide that will help you understand the difference between Deep Learning and Machine Learning, and gain practical hands-on experience:
In the previous tutorial, we learned how to perform Data Visualization, K-means Cluster Analysis, and Association Rule Mining using WEKA Explorer
Look at different career opportunities in both fields.
Every tech company will have career paths that involve machine learning and deep learning. However, Deep Learning and ML are often used together in the industry of AI but are not the same.
The most uncomplicated way to think of their relationship is to understand how they fit into the broader category of artificial intelligence.
This guide will help you clearly understand the overlapping concepts, terms, and differences. We will also look at Machine learning vs Deep learning career opportunities and frequently asked questions for better guidance.
=> Read Through The Complete Machine Learning Training Series
What You Will Learn:
Deep Learning vs Machine Learning
So, without any further pause, let us begin.
Clearing The Confusion Between AI vs Machine Learning vs Deep Learning
Artificial Intelligence, Machine Learning, and Deep Learning are the three trending terminologies that we hear every day and are usually used interchangeably in the computer science industry. However, the confusion between the three terms can be cleared by understanding the critical distinctions.
You can think of artificial intelligence, machine learning, and deep learning as a set of three interconnected circles where deep learning is a part of ML, which itself is derived from AI.
Refer to the image below:
Fascinating, right?
To break Deep learning vs Machine learning vs AI into simpler words, let us first understand the definitions of these three technologies.
Artificial intelligence is the practice of giving human intelligence to machines to learn and solve problems efficiently without human intervention. In simpler words, AI is a set of programmed rules that tells machines to behave specifically, move and manipulate objects, identify, and predict items and trends, or solve the given input problem.
#2) Machine Learning
Machine learning is a part of AI that empowers computer systems with the ability to learn, improve, and modify themselves when exposed to more data. In ML (Machine Learning), various math and computer science algorithms are used to achieve these tasks without explicitly programming.
There are different machine learning algorithms (supervised learning, unsupervised learning, transfer learning, etc.), each designed for a specific type of data or task.
Recommended Reading =>>Introduction To ML & Its Applications
#3) Deep Learning
Deep learning is a subpart of ML that mimics the functions of the human brain, and the detailed processing patterns found in the human brain inspire its algorithms. DL models can be taught to identify items and classify various patterns, like how the brain interprets the information it receives.
Deep learning is also called deep neural learning because it consists of neural networks having structures like human neural systems.
Note: Deep Learning is a diverse field of neural networks and algorithms. To develop an understanding of DL, refer “Complete Guide to Artificial Neural Network In Machine Learning.”
There are many techniques of AI, whereas ML lets the algorithm learn from the data, and deep learning uses many-layered neural networks to solve the most challenging problems for computers.
Now we know that AI is the ability of machines to mimic intelligent human behavior. ML is the application of AI that enables a machine to automatically learn and improve from its experiences. DL is an application of ML that utilizes complex algorithms and deep neural nets to train a machine to predict future values.
To summarize these three terms, we can say that Machine learning and Deep learning fuels AI systems. That means that with the use of machine learning and deep learning, we can achieve AI tasks.
Key Differences
| Deep Learning | Machine Learning | |
|---|---|---|
| Data | Deep learning requires a more significant amount of data. | Machine learning requires less data to train the model. |
| Accuracy | Deep learning provides higher accuracy. | Machine Learning gives less accuracy in comparison to deep learning. |
| Hardware Requirement | Deep learning demands machines with GPU to train adequately. | On the other hand, machine learning works well with low-end machines like CPUs. |
| Engineering Anomalies | For deep learning, you need to understand the basic functionality of the data. | For machine learning, you need to understand the features and represent the information. |
| Training Time | Deep learning models usually take a longer time to train. | In contrast, machine learning algorithms take less time to train. |
| Hyperparameter Tuning | Deep learning can be tuned in numerous ways. | Machine learning has limited tuning capabilities. |
| Processing Time | Deep learning takes a few hours or weeks for preprocessing. | Machine learning takes a few seconds or hours to preprocess. |
| Number Of Algorithms | Deep learning has fewer algorithms and formulas. | Machine learning has many algorithms. |
| Data Interpretation | Data interpretation in deep learning models is difficult. | In machine learning, some ML algorithms are easy to interpret, whereas, at the same time, some are extremely hard to decode and understand. |
Coding Differences
When it comes to Deep Learning vs Machine Learning coding differences, the only training step is different. In Machine Learning, you load your model and train the model, whereas, in Deep Learning, you build an architecture for the network to train the model. We will see this in the implementation in the next section.
Note: The primary step to load the dataset, create the data frame, process the data points, splitting the dataset is quite similar. Hence, to keep this guide short and to explain to you the significant differences, we will only see the training step of both technologies.
Machine Learning Training Process
from sklearn.linear_model import LinearRegression
Linear_Regression_Object = LinearRegression()
Linear_Regression_Object.fit(X_Train_Dataset, Y_Train_Dataset)
Y_Train_Dataset_Prediction = Linear_Regression_Object.predict(X_Train_Dataset)
Y_Test_Dataset_Prediction = Linear_Regression_Object.predict(X_Test_Dataset)
- We start with importing the required model from the sklearn module. There are different machine learning models which can be imported according to your problem statement.
- Then we create an object of the LinearRegression function and call the fit function on the LR variable. The model is trained on the input data X_train and Y_train.
- Once the model is trained, we use a predict function to make predictions on the training and the testing data.
Deep Learning Training Process
Keras_Model = keras.Sequential(
[
keras.Input(shape=Input_Shape_Array),
layers.Conv2D(64, kernel_size=(3, 3), activation=”tanh”),
layers.MaxPooling2D(pool_size=(2, 2)),
layers.Conv2D(128, kernel_size=(3, 3), activation=”tanh”),
layers.MaxPooling2D(pool_size=(2, 2)),
layers.Flatten(),
layers.Dropout(0.1),
layers.Dense(Number_Of_Output_Classes, activation=”relu”),
]
)
Keras_Model.summary()
Batch_Size_For_Training = 128
Num_Of_Epochs = 15
Keras_Model.compile(loss=”MeanSquaredError”, optimizer=”RMSprop”, metrics=[“Accuracy”])
Keras_Model.fit(X_train, y_train, batch_size=Batch_Size_For_Training, epochs=Num_Of_Epochs, validation_split=0.25)
Accuracy_Score = Keras_Model.evaluate(X_test, y_test, verbose=1)
print(“Testing Dataset Loss Value:”, Accuracy_Score[0])
print(“Testing Dataset Accuracy Value:”, Accuracy_Score[1])
- In Deep Learning, you start by designing an architecture for the network. In the above network, we have one input layer, two hidden layers followed by max-pooling layers, one flatten layer, a dropout layer and a fully connected dense layer.
- We use the tanh activation function on hidden layers and a relu function on the dense layer and then print the model description using the summary method.
- Then we define the number of epochs and the batch size for the model training and then compile the model using MeanSquaredError loss function and RMSprop optimizer.
- At last, we fit the model on the training data with the specified number of batch sizes and epochs. And then, we evaluate the accuracy of the test data using evaluate method.
Career Opportunities
You will be clear about the Machine Learning vs Deep Learning difference by now. Now let us see the diverse types of job opportunities in both fields and how they differ are:
#1) Machine Learning Engineer
A Machine Learning engineer writes programs and develops algorithms to extract meaningful insights from enormous amounts of numerical data. Most ML engineers are experienced with different Machine Learning algorithms and have knowledge of the required programming languages like Python and C++.
The average salary for Machine Learning engineers in the US ranges from USD 75K to USD 153K per year.
#2) Deep Learning Engineer
A Deep Learning engineer is a professional who carries out data engineering, modeling, deployment, and creating pipeline tasks. They are experts in defining data, requirements, collection, labeling, inspecting, cleaning, augmentation, and moving data across different architectures and channels.
Experts in this field know AI ML and DL algorithms, mathematics, computer science fundamentals, cloud computing, and programming languages like R or Python. The average pay of a Deep Learning engineer in the US is USD 135K per annum.
#3) AI Engineers
An AI engineer is a blend of machine learning and data science. Their role involves software designing, creating, and deploying machine learning algorithms. They have expert skills in data science, statistics, mathematics, CI/CD and SDLC, and CS and programming languages. The average pay of an AI engineer in the US is USD 86K per year.
#4) Computer Vision Engineer
A Computer Vision engineer develops innovative solutions for challenging evolving business problems using deep learning algorithms. They usually deal with optical character recognition, retail automation, machine inspection, 3d model building, medical imaging, motion capture, surveillance, automotive safety, and biometrics systems.
Most CV (Computer Vision) engineers can design, create, and develop deep learning frameworks for processing and visualizations. The average salary of a CV engineer in the US is USD 155K per year.
#5) Data Scientist
Data Scientist handles data sourcing, cleansing, statistical modeling, result evaluation, and result testing and deployments. They have a good understanding of processes, theories, concepts, tools, and technologies, and they are primarily employed in digital advertising, internet research, recommender systems, and image/speech recognition fields.
They must have proven experience in SaaS, Python, SAS, SQL databases, and other latest tools. The average pay of a data scientist in the US is USD 130K per annum.
Frequently Asked Questions
Q #1) What are the diverse tools used in Deep Learning vs Machine Learning?
Answer: Many tools are common to both technologies, like pandas, matplotlib, jupyter notebook, and tableau; however, people prefer Pytorch, TensorFlow, Keras, Microsoft cognitive toolkit, neural designer, H2O.ai, etc., for Deep Learning objectives.
In contrast, for Machine Learning projects, professionals prefer using scikit learn, weka, google cloud autoML, azure machine learning studio, etc.
Q #2) What type of data is involved in Machine Learning vs Deep Learning?
Answer: Machine Learning is best applied to numerical, categorical, time-series, and text data. In contrast, Deep Learning models are best suited for unstructured data like images, video, sound, or text.
Q #3) What are the most used algorithms in Machine Learning vs Deep Learning?
Answer: The most used algorithms used in Machine Learning are linear regression, logistic regression, decision trees, SVM, naive Bayes, KNN, random forest, etc.
In contrast, the most used algorithms in deep learning are convolutional neural network (CNN), recurrent neural network (RNN), Long Short-Term Memory networks (LSTM), Deep belief networks (DEB), Deep Boltzmann machine (DBM), stacked autoencoders, etc.
Conclusion
Deep Learning and Machine Learning will take over every industry in the coming days. They have massive potential in replacing the harsh ecosystem activities concerning space travel, human-intensive labor tasks, etc.
We hope this beginner’s guide on Deep Learning vs Machine Learning was able to teach you the fundamental difference between the two technologies. It will also help people from a non-technical background choose the best field for themselves.
Also Read =>> Difference Between Data Mining Vs Machine Learning Vs Artificial Intelligence Vs Deep Learning Vs Data Science
With this, the next step into your deep learning vs machine learning journey is to get hands-on practical experience with a real-time project. Get started with it and let me know what you think about these buzz words.
=> Visit Here For The Exclusive Machine Learning Series