Foundations of Deep Learning and Neural Networks from Coursera

Embark on a journey through the intricate world of deep learning and neural networks. This course starts with a foundation in the history and basic concepts of neural networks, including perceptrons and multi-layer structures. As you progress, you'll explore the mechanics of training neural networks, covering activation functions and the backpropagation algorithm.

The course then advances to artificial neural networks and their real-world applications, drawing inspiration from the human brain's architecture. You'll gain practical insights into input and output layers, the Sigmoid function, and key datasets like MNIST. Specialized topics such as feed-forward networks, backpropagation, and regularization techniques, including dropout strategies and batch normalization, are thoroughly covered.

You'll also be introduced to powerful frameworks like TensorFlow and Keras. The course concludes with an in-depth study of convolutional neural networks (CNNs), focusing on their applications and principles for image and video analysis.

This course is ideal for tech professionals and students with a basic understanding of programming and mathematics, particularly linear algebra, calculus, and basic probability.

What's inside

Syllabus

Course Introduction

In this module, we will introduce the basic concepts of deep learning and neural networks. We will explore the history, fundamental structures like perceptrons, and the process of training neural networks. Additionally, we'll cover important concepts such as activation functions and representations.

Artificial Neural Networks-Introduction

In this module, we will delve into the intricacies of artificial neural networks. We'll explore how the human brain inspires these networks, the detailed workings of perceptrons, and the layers that constitute neural networks. Additionally, we'll cover the sigmoid function and understanding MNIST data.

ANN - Feed Forward Network

In this module, we will focus on feed-forward networks, their operation modes, and the dimensions involved. We'll break down the pseudocode required for batch processing and introduce vectorized methods to optimize neural network training.

Backpropagation

In this module, we will dive deep into backpropagation, a crucial method for training neural networks. We'll introduce the loss function, break down the backpropagation process into multiple parts, and cover associated concepts such as the sigmoid function and stochastic gradient descent (SGD).

Regularization

In this module, we will cover regularization techniques to enhance neural network performance. We'll explore dropout methods, batch normalization in multiple parts, and introduce tools like TensorFlow and Keras that facilitate these processes.

Convolution Neural Networks

In this module, we will explore Convolutional Neural Networks (CNNs) and their applications. We'll discuss the ideas behind CNNs, analyze how they process image and video data, and implement essential operations like convolution, stride, padding, and pooling. We'll also cover combining networks for complex tasks.

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Provides a solid foundation in neural networks, covering history, basic concepts, and training processes, which is essential for those entering the field

Explores the mechanics of training neural networks, including activation functions and backpropagation, which are core skills for machine learning engineers

Introduces powerful frameworks like TensorFlow and Keras, which are widely used in industry for building and deploying deep learning models

Requires a basic understanding of programming and mathematics, particularly linear algebra, calculus, and basic probability, which may be a barrier for some learners

Covers regularization techniques like dropout strategies and batch normalization, which are crucial for improving the generalization and performance of neural networks

Examines convolutional neural networks (CNNs) and their applications for image and video analysis, which is highly relevant to computer vision and related fields

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Foundations of Deep Learning and Neural Networks with these activities:

Review Linear Algebra Fundamentals

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Strengthen your understanding of linear algebra concepts, which are foundational for understanding neural network operations and data representations.

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Review matrix operations such as addition, multiplication, and transposition.
Study vector spaces, linear independence, and basis vectors.
Practice solving systems of linear equations.

Brush Up on Calculus Concepts

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Revisit key calculus concepts like derivatives and the chain rule, essential for understanding backpropagation and optimization algorithms in neural networks.

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Review differentiation rules and techniques.
Study the concept of gradient and its role in optimization.
Practice applying the chain rule to composite functions.

Read 'Deep Learning' by Goodfellow, Bengio, and Courville

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Supplement your learning with a comprehensive textbook that covers the theoretical underpinnings and practical applications of deep learning.

View Deep Learning on Amazon

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Read the introductory chapters on neural networks and deep learning concepts.
Focus on chapters related to backpropagation, regularization, and convolutional neural networks.
Work through the examples and exercises provided in the book.

Four other activities

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Follow TensorFlow and Keras Tutorials

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Gain hands-on experience with TensorFlow and Keras by working through official tutorials and examples, solidifying your understanding of these frameworks.

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Complete the official TensorFlow tutorials for beginners.
Explore Keras examples for building different types of neural networks.
Adapt the tutorial code to solve your own simple problems.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow' by Aurélien Géron

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Expand your understanding of machine learning frameworks and techniques with a practical guide that covers Scikit-Learn, Keras, and TensorFlow.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters on neural networks and deep learning.
Work through the examples and exercises provided in the book.
Experiment with different neural network architectures and hyperparameters.

Implement Backpropagation from Scratch

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Deepen your understanding of backpropagation by implementing it from scratch using only basic Python libraries.

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Implement the forward pass of a simple neural network.
Calculate the gradients of the loss function with respect to the weights and biases.
Implement the backward pass to update the weights and biases.
Test your implementation on a small dataset.

Build a CNN for Image Classification

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Apply your knowledge of CNNs by building an image classification model using a dataset like CIFAR-10 or a custom dataset.

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Choose an image classification dataset.
Design a CNN architecture with convolutional, pooling, and fully connected layers.
Train the model using TensorFlow or Keras.
Evaluate the model's performance on a test set.

Career center

Learners who complete Foundations of Deep Learning and Neural Networks will develop knowledge and skills that may be useful to these careers:

Deep Learning Engineer

A Deep Learning Engineer focuses on designing, implementing, and validating deep learning models. This course specifically covers the core concepts of deep learning and neural networks, making it directly relevant to this role. A deep learning engineer will apply concepts explained in this course, such as the backpropagation algorithm, regularization techniques, and the use of frameworks like TensorFlow and Keras. The exploration of convolutional neural networks, along with their application to image and video analysis, is particularly vital to this role. This course provides a theoretical and practical foundation that is extremely helpful for those aiming for this career.

See salaries and explore the career path for Deep Learning Engineer

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models. This role involves a deep understanding of neural networks, which is directly addressed in this course. The course's modules on feed-forward networks, backpropagation, and regularization techniques are extremely relevant to the daily work of a machine learning engineer. Additionally, the course's introduction to Keras and TensorFlow provides practical skills for implementing and testing these models. The exploration of convolutional neural networks will be particularly useful for those engineers focused on image and video data, as this role also entails adapting and optimizing models for real-world applications. This course can help build a foundation in the theoretical and practical aspects of neural networks, vital to this role.

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Machine Learning Researcher

A Machine Learning Researcher investigates new machine learning methodologies, often publishing scholarly papers that advance the field. This course is useful to a machine learning researcher as it covers the basic theories behind neural networks, and examines how they are built, trained, and optimized. The course explains feed-forward networks, introduces the backpropagation algorithm, and discusses regularization. The methods and techniques covered in the course are vital background information for anyone beginning a career in machine learning research. This course can help build a foundation for cutting-edge work utilizing deep learning techniques.

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Computer Vision Engineer

A Computer Vision Engineer develops algorithms that enable machines to 'see' and interpret images and videos. The course's detailed coverage of convolutional neural networks is directly applicable to this role. A Computer Vision Engineer uses networks much like those covered in the course to build visual systems, and the course provides both a theoretical and practical understanding of this process. The course provides essential knowledge on feed-forward networks, backpropagation, regularization, and frameworks like TensorFlow and Keras, all directly relevant to this role. This course is an excellent starting point for anyone who wishes to begin a career as a computer vision engineer.

See salaries and explore the career path for Computer Vision Engineer

Image Processing Specialist

An Image Processing Specialist develops and implements algorithms for processing and analyzing images. This course’s coverage of convolutional neural networks is very relevant to this role, as it teaches the principles behind image and video analysis. An Image Processing Specialist may use networks similar to those discussed in the course. This course explains the theory behind these operations, as well as provides a practical introduction to TensorFlow and Keras, which would be extremely valuable. The course’s coverage of feed-forward networks and backpropagation provides an overview of the foundations of neural network creation. This course will be helpful for those building a resume for an image processing career.

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Artificial Intelligence Specialist

An Artificial Intelligence Specialist researches and applies artificial intelligence techniques to solve real-world problems. This often involves working with neural networks, a central topic in the course. This course delves into the history and basic concepts of neural networks, moving into advanced topics such as backpropagation, regularization, and convolutional neural networks. An Artificial Intelligence Specialist can apply the skills learned in this course to more complex problems, and the practical experience with TensorFlow and Keras will prove valuable. This course may be helpful to those new to the field, as well as those who wish to upgrade their current skill set.

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Algorithm Developer

An Algorithm Developer designs and implements algorithms for solving computational problems. This course covers many complex algorithms related to deep learning and neural networks, such as backpropagation. The explanation of feed-forward networks, backpropagation, regularization techniques and the implementation of convolutional neural networks can help an algorithm developer further increase their knowledge base. The course provides practical experience using TensorFlow and Keras. This course will be useful to someone who has experience in algorithm design but is looking to expand their knowledge into the area of deep learning.

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Research Scientist

A Research Scientist conducts research to advance knowledge in their specific field. This course may be useful for a Research Scientist who chooses to focus on deep learning and neural networks, as it covers essential theoretical and practical information, such as backpropagation, regularization techniques, and convolutional neural networks, as well as using frameworks such as TensorFlow and Keras. The course's in-depth exploration of these topics can help a Research Scientist who is engaging with these specific technologies. A Research Scientist is often required to perform original research and improve upon current techniques, therefore a foundation is necessary for advancement. This course can help build such a foundation.

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Data Scientist

A Data Scientist analyzes complex data to extract insights and create data-driven solutions. This course's exploration of neural networks and deep learning helps build a foundation in relevant techniques. Understanding how to build and train neural networks, as well as the details of architectures such as feed-forward networks, backpropagation and regularization methods, helps a data scientist expand their repertoire of tools and techniques. Though this course doesn't address all aspects of data science, the course's introduction to frameworks like TensorFlow and Keras will be useful. This course may be useful for a data scientist who wishes to expand their expertise to include deep learning methods.

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Robotics Engineer

A Robotics Engineer designs, builds, and tests robots, often incorporating artificial intelligence and machine learning. The course's in-depth study of neural networks provides a foundation for building intelligent robotic systems. A Robotics Engineer uses concepts such as feed-forward networks, backpropagation, and convolutional neural nets to develop robots that can interpret data, navigate, and perform complex tasks. The practical aspects of the course, including the use of TensorFlow and Keras, may be useful for building and testing such systems. This course may help those who wish to begin a career in robotics, especially if their focus is on the artificial intelligence aspects of the project.

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Software Developer

A Software Developer designs, writes, and tests code for various applications. This course may be helpful for a software developer who plans to work on programs that include machine learning or AI capabilities. The course provides an overview of the principles of deep learning, including neural network architectures such as feed-forward networks, backpropagation, and regularization techniques. The course also gives practical knowledge, with an introduction to TensorFlow and Keras. This knowledge may be useful for a software developer who works with data and wants to broaden their understanding of data processing techniques. Software developers who wish to work in the AI or machine learning space may find this course useful.

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Bioinformatics Scientist

A Bioinformatics Scientist develops computational methods to analyze biological data, often including data derived from genomic and proteomic sources. This area is becoming increasingly reliant on machine learning methods. This course may be useful to a bioinformatics scientist who wishes to work with machine learning data, as the course's explanation of deep learning and neural networks can build essential background knowledge. The course provides information on feed-forward networks, backpropagation, regularization, and convolutional neural networks, and covers the use of TensorFlow and Keras. This background could help the bioinformatics scientist expand the range of tools used in their research. This course may be useful for a bioinformatics scientist who wishes to incorporate deep learning into their work.

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Data Analyst

A Data Analyst collects, organizes, and interprets statistical information to support business decisions. While this role may not directly utilize deep learning on a day to day basis, a data analyst may benefit from learning about cutting edge techniques. This course gives a solid introduction to deep learning, covering topics such as neural network architectures, backpropagation, regularization, and image and video processing. For data analysts who wish to be on the cutting edge of technology, this course provides an overview of the latest trends and techniques. This course may be useful for a data analyst who wants to move into more advanced roles.

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Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical methods to analyze financial data, build models, and predict market trends. While this role doesn't specifically focus on deep learning, a knowledge of neural network architectures could be useful in finding patterns and correlations in data. This course's coverage of feed-forward networks, backpropagation, and regularization can help build a background in these methods so they can be implemented for quantitative analysis. An introduction to frameworks such as TensorFlow and Keras may also be useful. This course may be useful for a quantitative analyst looking to explore new techniques in the field.

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Data Engineer

A Data Engineer designs, builds, and manages data infrastructure, enabling easier access to data for analysis. This role may include a focus on building systems that work with machine learning and deep learning algorithms. Therefore, this course's focus on neural networks, backpropagation, and convolutional neural networks may be useful for a data engineer. An introduction to TensorFlow and Keras may also be relevant as these tools could be integrated into the platforms built by the data engineer. This course may be useful for a data engineer who wishes to work with systems that use deep learning.

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Foundations of Deep Learning and Neural Networks

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