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Deep Learning Specialization

Deep Learning Specialization

7 Reviews
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Course Highlights

  • Self-Paced Learning Option

  • Course Videos & Readings

  • Shareable Specialization and Course Certificates

  • Practice Quizzes | Graded Assignments with Peer Feedback

Mode of learning: Online self study

Course Overview

Who should do this course ?
  • Suitable for people with programming experience. The course is taught in Python. We assume you have basic programming skills (understanding of for loops, if/else statements, data structures such as lists and dictionaries).

  • Recommended for Mathematics: basic linear algebra (matrix vector operations and notation) will help.

  • Machine Learning: a basic knowledge of machine learning

What are the course deliverables ?
  • Learn the foundations of deep learning

  • Understand the major technology trends driving Deep Learning

  • Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture

  • 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.

  • This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader.

  • 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 I've seen teams waste months or years through not understanding the principles taught in this course.

  • 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 image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization.

  • Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.

  • Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis

More about this course
  • Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.Participants will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. Participants will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. Participants will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. Participants will master not only the theory, but also see how it is applied in industry.

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    Reviews

    Average Rating

    4

    Based on 7 Ratings

    Aniruddha Mitra

    The kind of mystery that deep learning brings with it, is hard to understand in a conventional classroom set up or through printed books. So after going through formal courses in Deep Learning in my post graduation I felt that need to demystify the subject by myself. I'm happy about the choice I made. After completing each assignment I felt happy about myself.

    Pavan Kumar Reddy Kunchala

    It was a wonderful course, It helped me improve my tuning skills for setting hyper-parameters and helped me improve. the efficiency of the model

    Rajat

    I have completed 2 courses in this specialization...they have been really awesome..the instructor Mr Andrew Ng Sir has explained all the math in as simple language as possible ...and it is truely a must do specialization in order to deepen your knowledge about Neural networks

    ANISH

    This is a introductory course for stepping your foot in the deep field of deep learning. The course is really well designed and the exercise are small but focus more on the little things that could go wrong. This is more of a theoretical approach and bottom up approach. If you want a more code intensive and top-down approach go for fast.ai Course. what I am doing is I am going for both in parallel, which is the best approach at least for me.

    Shubhajit Das

    Good getting started course for deep learning

    A good course to get started with deep learning.Taking the five courses is very instructive. The content is well structured and good to follow for everyone with at least a bit of an understanding on matrix algebra. Some experience in writing Python code is a requirement. The programming assignments are well designed in general. Apart of their instructive character, it’s mostly enjoyable to work on them, too. Finally, in my opinion, doing this specialization is a fantastic way to get you started on the various topics in Deep Learning. After taking the courses, you should know in which field of Deep Learning you wanna specialize further on.

    Yaser Sakkaf

    I already knew about neural networks but after completing the first part of Specialization I have gained knowledge about the mathematical concepts running in the background while the neural network is in action. The hands-on provided is quite helpful too.

    Lakshay Goyal

    The course was amazing . There is an amazing impact of it. The way they explains everything was amazing. At this point I am very confident about Tensorflow.
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