Deep Learning with TensorFlow

Packt via Coursera Specialization

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Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Duration & workload

4 weeks, 10 hours a week

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This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. In this specialization, you will master deep learning using TensorFlow. It’s divided into three key sections: starting with the basics of artificial neural networks (ANNs), progressing through Recurrent Neural Networks (RNNs), and ending with Convolutional Neural Networks (CNNs). You will learn key concepts such as forward propagation, activation functions, and multiclass classification, with hands-on coding using real-world datasets like MNIST, CIFAR-10, and Fashion MNIST. You will explore advanced architectures such as RNNs, GRUs, LSTMs, and CNNs. Practical sessions cover model optimization with techniques like gradient descent and Adam, and you’ll learn how to use TensorFlow and Keras for model evaluation. Additionally, you’ll apply deep learning models to time series prediction, natural language processing, and image classification tasks. This specialization is suitable for learners with basic machine learning knowledge, and some Python experience is required. It’s an intermediate-level specialization. By the end, you will be able to design and deploy deep learning models, optimizing them for tasks like image classification and time series forecasting.

Syllabus

Course 1: Deep Learning - Artificial Neural Networks with TensorFlow
Course 2: Deep Learning - Recurrent Neural Networks with TensorFlow
Course 3: Deep Learning: Convolutional Neural Networks with TensorFlow

Courses

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7 hours 21 minutes
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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course delves into deep learning and artificial neural networks using TensorFlow. - It begins with foundational machine learning concepts, covering linear classification and regression, before exploring neurons, model learning, and predictions. - Core modules focus on forward propagation, activation functions, and multiclass classification, with practical examples like the MNIST dataset for image classification and regression tasks. - It also covers model saving, Keras usage, and hyperparameter selection. - The final sections provide an in-depth look at loss functions and gradient descent optimization techniques, including Adam. - Key outcomes include understanding machine learning concepts, implementing ANN models, and optimizing deep learning models using TensorFlow. This course suits those interested in deep learning, TensorFlow 2, and foundational concepts for advanced neural networks like CNNs, RNNs, LSTMs, and transformers. Proficiency in Python and familiarity with NumPy and Matplotlib are required.
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5 hours 41 minutes
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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Recurrent Neural Networks (RNNs) are a powerful class of neural networks designed for sequence data, making them ideal for time series prediction and natural language processing tasks. This course begins with an introduction to the fundamental concepts of RNNs and explores their application in forecasting and time series prediction. You will delve into coding with TensorFlow, learning how to implement autoregressive models and simple RNNs for various predictive tasks. As the course progresses, you will encounter more sophisticated RNN architectures such as GRUs and LSTMs. These units are essential for handling complex sequences and long-distance dependencies in data. Practical sessions will guide you through using these models for challenging tasks, including stock return prediction and image classification on the MNIST dataset. The course also covers the critical aspect of managing data shapes and ensuring your models are well-structured and efficient. Towards the end, the course shifts focus to natural language processing (NLP), where you will explore embeddings, text preprocessing, and text classification using LSTMs. By combining theoretical knowledge with hands-on coding exercises, you will develop a robust understanding of how to leverage RNNs for various applications. Whether you are predicting stock prices or classifying text, this course equips you with the skills needed to succeed in the field of deep learning. This course is ideal for data scientists, machine learning engineers, and AI enthusiasts who want to learn and implement recurrent neural networks for time series analysis and natural language processing. Basic knowledge of Python and TensorFlow is recommended.
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5 hours 14 minutes
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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Unlock the potential of deep learning by mastering Convolutional Neural Networks (CNNs) and Transfer Learning with hands-on experience using TensorFlow and Keras. This course offers a comprehensive introduction to CNNs, guiding you through their theoretical foundations, practical implementations, and applications in both image and text classification. With hands-on coding in TensorFlow, you'll build, optimize, and experiment with real-world datasets like CIFAR-10 and Fashion MNIST. Dive deep into Convolutional Neural Networks (CNNs) with TensorFlow. Starting with the basics of convolution, you'll explore advanced topics like data augmentation, batch normalization, and transfer learning. You'll not only work on image datasets but also gain insights into applying CNNs for natural language processing (NLP). Whether you are building from scratch or using pre-trained models, this course equips you with the skills to deploy CNNs in real-world applications. The course begins by establishing a strong theoretical understanding of CNNs, breaking down convolutions, filters, and layers. After this, you'll implement CNNs for popular datasets like Fashion MNIST and CIFAR-10, diving into hands-on coding sessions with TensorFlow and Keras. Practical exercises such as data augmentation and batch normalization will enhance your ability to improve model performance. Later, you'll explore CNNs in the context of natural language processing, understanding how CNNs can be applied to text classification. The final section focuses on transfer learning, where you'll work with pre-trained models like VGG and ResNet and apply them to new datasets. This course is ideal for data scientists, machine learning engineers, and developers familiar with Python, TensorFlow, and basic deep learning concepts. You should have a solid understanding of neural networks, and experience with coding in Python is necessary to follow the practical aspects of the course. Familiarity with TensorFlow is recommended but not mandatory.