Machine Learning and AI In
Deep Learning Fundamentals

This Deep Learning Fundamentals course is designed to introduce students to the fundamental concepts and techniques of deep learning, a subset of machine learning that involves the use of artificial neural networks with multiple layers to analyze and learn from complex data. The course will cover the basics of deep learning, including neural network architectures, training methods, and applications in computer vision, natural language processing, and other areas.

REQUEST FREE INFORMATION

Overview

Deep Learning Fundamentals

Course Learning Outcomes (CLOs) and SLOs

Course Learning Outcomes (CLOs) typically include:


  • Understanding Deep Learning: Develop a thorough understanding of deep learning principles, including neural network architectures (e.g., convolutional neural networks, recurrent neural networks) and their applications.
  • Deep Learning Frameworks: Familiarize with popular deep learning frameworks such as TensorFlow, PyTorch, or Keras, and gain hands-on experience in implementing deep learning models.
  • Advanced Topics: Explore advanced topics in deep learning, such as transfer learning, generative adversarial networks (GANs), and reinforcement learning with neural networks.
  • Application Development: Learn to apply deep learning techniques to solve complex problems in image recognition, natural language processing, and other domains.

    Student Learning Outcomes (SLOs) are specific goals for students, such as:


  • Model Implementation: Implement deep learning models using appropriate frameworks and libraries, demonstrating proficiency in model architecture design and optimization.
  • Experimentation and Evaluation: Conduct experiments to evaluate and improve deep learning models, including hyperparameter tuning and performance evaluation.
  • Data Handling: Prepare and preprocess large-scale datasets for deep learning tasks, including data augmentation and normalization techniques.
  • Project Execution: Execute deep learning projects from inception to deployment, showcasing practical skills in solving real-world problems using deep learning methodologies.

    • 100% International

    Study at your own pace from anywhere in the world

    Recommended by 96% of our graduates

    According to our latest alumni survey

    50,000+ students

    enrolled in Germany’s largest university

    Study contents

    Contents

    Module 1: Introduction to Deep Learning


  • Definition of Deep Learning
  • Brief History of Deep Learning
  • Applications of Deep Learning
  • Types of Deep Learning Models

    Key Concepts:

  • Neural Networks
  • Backpropagation
  • Activation Functions

    Module 2: Mathematical Foundations


    Linear Algebra Review:
  • Vectors and Matrices
  • Vector Operations (Addition, Scalar Multiplication, Matrix Multiplication)
  • Eigenvalues and Eigenvectors
    Calculus Review:
  • Limits
  • Derivatives
  • Optimization
    Probability Theory:
  • Random Variables
  • Probability Distributions (Bernoulli, Gaussian, etc.) Bayes' Theorem

    Module 3: Neural Networks


    Multilayer Perceptrons (MLPs):
  • Feedforward Networks
  • Backpropagation Algorithm
  • Activation Functions (Sigmoid, ReLU, etc.)
    Convolutional Neural Networks (CNNs):
  • Convolutional Layers
  • Pooling Layers
  • Activation Functions (ReLU, etc.)
    Recurrent Neural Networks (RNNs):
  • Basic RNNs
  • LSTM and GRU Cells
  • Bidirectional RNNs

    Module 4: Deep Learning Architectures


    Autoencoders:
  • Vanilla Autoencoders
  • Variational Autoencoders (VAEs)
    Generative Adversarial Networks (GANs):
  • Generative Models
  • Adversarial Training
    Transfer Learning:
  • Pre-trained Models
  • Fine-tuning

    Module 5: Deep Learning Techniques


    Regularization Techniques:
  • L1 and L2 Regularization
  • Dropout and Early Stopping
    Optimization Algorithms:
  • Stochastic Gradient Descent (SGD)
  • Adam and RMSProp
    Hyperparameter Tuning:
  • Grid Search and Random Search
  • Bayesian Optimization

    Module 6: Deep Learning Applications


    Image Classification:
  • CNN Architectures (AlexNet, VGG, etc.)
  • Transfer Learning for Image Classification
    Natural Language Processing (NLP):
  • Word Embeddings (Word2Vec, GloVe, etc.)
  • Recurrent Neural Networks (RNNs) for NLP Tasks
    Speech Recognition:
  • Mel-Frequency Cepstral Coefficients (MFCCs)
  • Recurrent Neural Networks (RNNs) for Speech Recognition

    Module 7: Advanced Topics in Deep Learning


    Attention Mechanisms:
  • Soft Attention and Hard Attention
    Batch Normalization:
  • Normalization Techniques for Neural Networks
  • Dropout and Batch Normalization for Regularization

    Module 8: Case Studies and Projects


    Real-world Applications of Deep Learning:
  • Computer Vision, NLP, Speech Recognition, etc.
  • Case Study 1: Image Classification with CNNs
  • Case Study 2: Text Classification with RNNs

    Module 9: Implementations and Tools

    Programming Languages:
  • Python and TensorFlow/Keras
    Popular Deep Learning Libraries:
  • TensorFlow/Keras, PyTorch, Caffe2, etc.
    Data Preprocessing and Visualization Tools:
  • Pandas, NumPy, Matplotlib, Seaborn, etc.

    • Admission

    Admission Criteria

    Mathematics: Calculus, Linear Algebra, Probability, and Statistics. Familiarity with mathematical concepts such as derivatives, integrals, eigenvectors, and matrix operations. Programming: Basic programming skills in a language such as Python, MATLAB, or R. Familiarity with data structures, algorithms, and software development principles. Computer Science: Understanding of computer science concepts such as data structures, algorithms, and software design patterns. Recommended Background: Linear Algebra: Understand the basics of linear algebra, including vector spaces, eigenvalues, and eigenvectors. Calculus: Familiarity with calculus concepts such as derivatives and integrals. Probability and Statistics: Understanding of basic probability theory and statistical concepts such as Bayes' theorem and hypothesis testing. Machine Learning: Familiarity with machine learning basics such as supervised and unsupervised learning, regression, classification, clustering, and dimensionality reduction. Additional Recommendations: ** Familiarity with Python**: Python is a popular language used in deep learning, so having a basic understanding of Python programming is highly recommended. Familiarity with Deep Learning Libraries: Knowledge of popular deep learning libraries such as TensorFlow, PyTorch, or Keras can be helpful but is not strictly necessary. Understanding of Data Preprocessing: Understanding of data preprocessing techniques such as data cleaning, normalization, and feature scaling can be beneficial. Course Objectives: Introduction to Deep Learning: Understand the basics of deep learning, including neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and autoencoders. Mathematical Foundations: Develop an understanding of the mathematical foundations of deep learning, including calculus, linear algebra, and probability theory. Deep Learning Techniques: Learn various deep learning techniques such as forward propagation, backpropagation, optimization methods, regularization techniques, and transfer learning. Hands-on Experience: Gain hands-on experience with deep learning using popular libraries such as TensorFlow or PyTorch.

    Careers

    Career Path

    Machine Learning Engineer: Design and develop machine learning models, algorithms, and systems that enable AI-powered applications. Artificial Intelligence (AI) Researcher: Conduct research in AI and deep learning to develop new algorithms, models, and techniques for various applications. Data Scientist: Apply machine learning and deep learning techniques to analyze large datasets, identify patterns, and make predictions. Computer Vision Engineer: Develop computer vision systems that can interpret and understand visual data from images, videos, and other sources. Natural Language Processing (NLP) Engineer: Create NLP systems that can understand, generate, and process human language. Autonomous Systems Engineer: Design and develop autonomous systems that use machine learning and deep learning for self-driving cars, drones, and other applications. Robotics Engineer: Apply machine learning and deep learning to develop intelligent robots that can perceive, reason, and interact with their environment. Quantum Computing Researcher: Explore the application of machine learning and deep learning to quantum computing and its potential for solving complex problems. Image Processing Specialist: Develop image processing algorithms using deep learning techniques for applications such as medical imaging, satellite imaging, or surveillance. Speech Recognition Engineer: Create speech recognition systems that use machine learning and deep learning to recognize and transcribe spoken language. Recommendation Systems Engineer: Design and develop recommendation systems that use machine learning and deep learning to suggest products or services based on user behavior. Bioinformatics Researcher: Apply machine learning and deep learning to analyze large biological datasets, such as genomic sequences or medical imaging data. Finance Data Scientist: Use machine learning and deep learning to analyze financial data, predict market trends, and make investment decisions. Marketing Analyst: Apply machine learning and deep learning to analyze customer behavior, optimize marketing campaigns, and predict customer churn. Healthcare Data Analyst: Use machine learning and deep learning to analyze healthcare data, predict patient outcomes, and develop personalized treatment plans.

    Student reviews

    Coming Soon.

    Tuition fees

    Deep Learning Fundamentals (Duration 4 Weeks)

    The Deep Learning Specialization is for early-career software engineers or technical professionals looking to master fundamental concepts and gain practical machine learning and deep learning skills.

    350 $

    200 $ / Total Cost
    Inquiry Add to Cart

    All our study programmes include the following benefits

    • Teaching and study material
    • Marking of your end-of-module exams
    • Monthly live and recorded tutorials
    • Use of the online campus
    • Individual study coaching
    • Online exams
    • Career coaching
    • Learn English for free

    Our global recognition

    IU is recognised by WES Canada and U.S., which means your degree can be converted to points in the local system for purposes of immigration, work, or studies.

    As the first EU institution in UNESCO's Global Education Coalition, IU is committed to ensuring accessible quality education to students in crisis worldwide through free online micro-credentials.

    Our company partners

    For over 20 years, IU has established partnerships with leading global companies. This offers you the chance to gain firsthand experience through internships and projects and allow us to adapt our learning content to the ever-evolving needs of the labour market. You'll benefit from an education designed to bridge the gap between theory and real-world practice, ensuring your readiness for your future career.

    Recognition

    Recognition of previous achievements

    Have you already completed a training course, studied at a university or gained work experience? Have you completed a course or a learning path through EPIBM LinkedIn Learning, and earned a certificate? Then you have the opportunity to get your previous achievements recognised, and complete your studies at EPIBM sooner.

    Save time:

    Skip individual modules or whole semesters!
    Even before you apply for a study programme, we’ll gladly check whether we can take your previous achievements into account: 100% online, no strings attached. Simply fill in our recognition application form, which you can find under the content section of each study programme's webpage, and upload it via our upload section. You can also e-mail it to us, or send it via post.
    Send an email to [email protected] to find out which previous achievements you can get recognised. You can get your previous achievements recognised during your studies. Recognition files

    F.A.Q

    Frequently Asked Questions

    Feugiat pretium nibh ipsum consequat. Tempus iaculis urna id volutpat lacus laoreet non curabitur gravida. Venenatis lectus magna fringilla urna porttitor rhoncus dolor purus non.

    Dolor sit amet consectetur adipiscing elit pellentesque habitant morbi. Id interdum velit laoreet id donec ultrices. Fringilla phasellus faucibus scelerisque eleifend donec pretium. Est pellentesque elit ullamcorper dignissim. Mauris ultrices eros in cursus turpis massa tincidunt dui.

    Eleifend mi in nulla posuere sollicitudin aliquam ultrices sagittis orci. Faucibus pulvinar elementum integer enim. Sem nulla pharetra diam sit amet nisl suscipit. Rutrum tellus pellentesque eu tincidunt. Lectus urna duis convallis convallis tellus. Urna molestie at elementum eu facilisis sed odio morbi quis

    Dolor sit amet consectetur adipiscing elit pellentesque habitant morbi. Id interdum velit laoreet id donec ultrices. Fringilla phasellus faucibus scelerisque eleifend donec pretium. Est pellentesque elit ullamcorper dignissim. Mauris ultrices eros in cursus turpis massa tincidunt dui.

    Molestie a iaculis at erat pellentesque adipiscing commodo. Dignissim suspendisse in est ante in. Nunc vel risus commodo viverra maecenas accumsan. Sit amet nisl suscipit adipiscing bibendum est. Purus gravida quis blandit turpis cursus in

    Laoreet sit amet cursus sit amet dictum sit amet justo. Mauris vitae ultricies leo integer malesuada nunc vel. Tincidunt eget nullam non nisi est sit amet. Turpis nunc eget lorem dolor sed. Ut venenatis tellus in metus vulputate eu scelerisque. Pellentesque diam volutpat commodo sed egestas egestas fringilla phasellus faucibus. Nibh tellus molestie nunc non blandit massa enim nec.

    You might also be interested in these study programmes

    ✓

    We value and respect your privacy. By continuing, you agree to EPIBM data protection policy.

    Accredited and certified