Deep Learning Coursera Specialization
| Learning Experience | 9.6 |
|---|---|
| Content Rating | 9.7 |
Deep Learning Specialization is a foundational program that will prepare you to participate in the development of leading-edge AI technology.
Deep Learning Coursera Specialization
If you want to break into AI, the Deep Learning Coursera specialization will help you do so. Deep Learning is one of the most highly sought-after skills in tech. This course will help you become good at Deep Learning.
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Where, a computer model learns to perform classification tasks directly from images, text, or sound. The learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. These models are trained by using a large set of labeled data and neural network architectures that contain many layers.
In a word, accuracy. Deep learning achieves recognition accuracy at higher levels than ever before. This helps businesses to meet user expectations, and it is crucial for safety-critical applications like driverless cars. Recent advances in deep learning have improved to the point where deep learning outperforms humans in some tasks like classifying objects in images (more on Deep learning).
Moreover, you would have heard about the Google technology Google Brain developed by Google team, it is based on deep learning artificial intelligence research. Formed in the early 2010s, Google Brain combines open-ended machine learning research with information systems and large-scale computing resources. The founder of Google brain is Andrew Ng, who is the tutor of this specialization on Coursera and Chief Scientist at Baidu Research that eventually resulted in the productization of deep learning technologies across a large number of Google services.
Andrew Ng, has spoken and written a lot about what deep learning is and is a good place to start. I am sure you would get more than you expect if enrolled in this course taught by Andrew Ng.
About Specialization
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. Also, you will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory but also see how it is applied in industry. Get a chance to practice all these ideas in Python and in TensorFlow, which we will teach.
You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work. The deep learning Coursera course will help you master Deep Learning, understand how to apply it, and build a career in AI.
Applied Learning Project for the Deep Learning Coursera Specialization
- Build and train deep neural networks, carry out vectorized neural networks, recognize architecture specifications, and use DL to your applications
- Use finest practices to train and establish test sets and evaluate bias/variance for building DL applications, utilize standard NN strategies, use optimization algorithms, and carry out a neural network analysis in TensorFlow
- Use techniques for decreasing mistakes in ML systems, comprehend intricate ML settings, and use end-to-end, transfer, and multi-task learning
- Build a Convolutional Neural Network, use it for visual detection and acknowledgment jobs, utilize neural design transfer to produce art, and apply these algorithms on an image, video, and other 2D/3D data
- Build and train Recurrent Neural Networks and their versions (GRUs, LSTMs), use RNNs to character-level language modeling, deal with NLP and Word Embeddings, and utilize HuggingFace tokenizers and transformers to carry out Named Entity Recognition and Question Answering.
Prerequisites
- Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
- A basic grasp of linear algebra & ML
Syllabus of Deep Learning Coursera Specialization
This specialization has been designed by Deeplearning.ai, there are 5 Courses in this specialization:
1. Neural Networks and Deep Learning (Content ratings 97%)
This course teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description.
- In this course, you will learn the foundations of deep learning.
- Build, train, and apply fully connected deep neural networks.
- Implement efficient (vectorized) neural networks.
- Understand the key parameters in a neural network’s architecture.
- After completing the course, you will be able to apply deep learning to your own applications.
2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization (98%)
The Deep Learning Coursera course will teach you the “magic” of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. After 3 weeks, you will:
- Understand industry best-practices for building deep learning applications
- Be able to effectively use the common neural network “tricks”, including initialization, L2 and dropout regularization, Batch normalization, gradient checking.
- Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop, and Adam, and check for their convergence.
- Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance.
- Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization.
3. Structuring Machine Learning Projects (96%)
You will learn how to build a successful machine learning project. The tutor of this course ‘Andrew Ng’ is a highly experienced person in this field. Precisely speaking, this is a content-driven course designed through his vast experience based on building and shipping many deep learning products, as he says in his course explanation. After 2 weeks, you will:
- Understand how to diagnose errors in a machine learning system, and be able to prioritize the most promising directions for reducing error.
- Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance.
- Know how to apply end-to-end learning, transfer learning, and multi-task learning.
4. Convolutional Neural Networks (98%)
- The Deep Learning Coursera course will teach you how to build convolutional neural networks and apply it to image data.
- Understand how to build a convolutional neural network, including recent variations such as residual networks.
- Know how to apply convolutional networks to visual detection and recognition tasks.
- Know to use neural style transfer to generate art.
- Be able to apply these algorithms to a variety of images, videos, and other 2D or 3D data.
5. Sequence Models (97%)
- The Deep Learning Coursera course will teach you how to build models for natural language, audio, and other sequence data.
- Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
Similar courses
- Learn Deep Learning (A-Z): Hands-on Artificial Neural Networks
- Deep Learning with Python: Guide to Tensor Flow
- Machine Learning Course, Data Science and Deep Learning with Python
- Deep Learning Course by Udacity
- Deep Learning with Keras and TensorFlow
Note: Your review matters
If you have already done the Deep Learning Coursera course, then kindly post your review in our reviews section. It would help others to get useful information and better insight of the course offered.
Description
Deep Learning Coursera Specialization
If you want to break into AI, the Deep Learning Coursera specialization will help you do so. Deep Learning is one of the most highly sought-after skills in tech. This course will help you become good at Deep Learning.
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Where, a computer model learns to perform classification tasks directly from images, text, or sound. The learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. These models are trained by using a large set of labeled data and neural network architectures that contain many layers.
In a word, accuracy. Deep learning achieves recognition accuracy at higher levels than ever before. This helps businesses to meet user expectations, and it is crucial for safety-critical applications like driverless cars. Recent advances in deep learning have improved to the point where deep learning outperforms humans in some tasks like classifying objects in images (more on Deep learning).
Moreover, you would have heard about the Google technology Google Brain developed by Google team, it is based on deep learning artificial intelligence research. Formed in the early 2010s, Google Brain combines open-ended machine learning research with information systems and large-scale computing resources. The founder of Google brain is Andrew Ng, who is the tutor of this specialization on Coursera and Chief Scientist at Baidu Research that eventually resulted in the productization of deep learning technologies across a large number of Google services.
Andrew Ng, has spoken and written a lot about what deep learning is and is a good place to start. I am sure you would get more than you expect if enrolled in this course taught by Andrew Ng.
About Specialization
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. Also, you will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory but also see how it is applied in industry. Get a chance to practice all these ideas in Python and in TensorFlow, which we will teach.
You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work. The deep learning Coursera course will help you master Deep Learning, understand how to apply it, and build a career in AI.
Applied Learning Project for the Deep Learning Coursera Specialization
- Build and train deep neural networks, carry out vectorized neural networks, recognize architecture specifications, and use DL to your applications
- Use finest practices to train and establish test sets and evaluate bias/variance for building DL applications, utilize standard NN strategies, use optimization algorithms, and carry out a neural network analysis in TensorFlow
- Use techniques for decreasing mistakes in ML systems, comprehend intricate ML settings, and use end-to-end, transfer, and multi-task learning
- Build a Convolutional Neural Network, use it for visual detection and acknowledgment jobs, utilize neural design transfer to produce art, and apply these algorithms on an image, video, and other 2D/3D data
- Build and train Recurrent Neural Networks and their versions (GRUs, LSTMs), use RNNs to character-level language modeling, deal with NLP and Word Embeddings, and utilize HuggingFace tokenizers and transformers to carry out Named Entity Recognition and Question Answering.
Prerequisites
- Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
- A basic grasp of linear algebra & ML
Syllabus of Deep Learning Coursera Specialization
This specialization has been designed by Deeplearning.ai, there are 5 Courses in this specialization:
1. Neural Networks and Deep Learning (Content ratings 97%)
This course teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description.
- In this course, you will learn the foundations of deep learning.
- Build, train, and apply fully connected deep neural networks.
- Implement efficient (vectorized) neural networks.
- Understand the key parameters in a neural network’s architecture.
- After completing the course, you will be able to apply deep learning to your own applications.
2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization (98%)
The Deep Learning Coursera course will teach you the “magic” of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. After 3 weeks, you will:
- Understand industry best-practices for building deep learning applications
- Be able to effectively use the common neural network “tricks”, including initialization, L2 and dropout regularization, Batch normalization, gradient checking.
- Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop, and Adam, and check for their convergence.
- Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance.
- Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization.
3. Structuring Machine Learning Projects (96%)
You will learn how to build a successful machine learning project. The tutor of this course ‘Andrew Ng’ is a highly experienced person in this field. Precisely speaking, this is a content-driven course designed through his vast experience based on building and shipping many deep learning products, as he says in his course explanation. After 2 weeks, you will:
- Understand how to diagnose errors in a machine learning system, and be able to prioritize the most promising directions for reducing error.
- Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance.
- Know how to apply end-to-end learning, transfer learning, and multi-task learning.
4. Convolutional Neural Networks (98%)
- The Deep Learning Coursera course will teach you how to build convolutional neural networks and apply it to image data.
- Understand how to build a convolutional neural network, including recent variations such as residual networks.
- Know how to apply convolutional networks to visual detection and recognition tasks.
- Know to use neural style transfer to generate art.
- Be able to apply these algorithms to a variety of images, videos, and other 2D or 3D data.
5. Sequence Models (97%)
- The Deep Learning Coursera course will teach you how to build models for natural language, audio, and other sequence data.
- Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
Similar courses
- Learn Deep Learning (A-Z): Hands-on Artificial Neural Networks
- Deep Learning with Python: Guide to Tensor Flow
- Machine Learning Course, Data Science and Deep Learning with Python
- Deep Learning Course by Udacity
- Deep Learning with Keras and TensorFlow
Note: Your review matters
If you have already done the Deep Learning Coursera course, then kindly post your review in our reviews section. It would help others to get useful information and better insight of the course offered.
Specification:
- Coursera
- deeplearning.ai
- Microdegree
- Self-paced
- Intermediate
- 3+ Months
- Free Trial (Paid Course & Certificate)
- English
- Python
- Intermediate Python Skills Linear Algebra Machine Learning Basics
- Data Science Data Science with 'Python' Deep learning Machine learning Neural Networks TensorFlow
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