If you are desiring to become a Deep Learning Expert then you have fallen in the right place, in these articles we will widely talk about all those courses that will help you to enhance your skills and will make you a Deep Learning Developer.
Let start this series of 7 Legit Deep Learning Courses To Become DL Expert. All the courses below are arranged in a sequential order that will take you from a vacuum head to a Deep Learning fellow.
7 Legit Deep Learning Courses To Become DL Expert (Free)
1. Deep Learning Fundamentals
As the name suggests in this deep learning course you will explore all the core and fundamentals about deep learning and it works internally. This course will also talk about DL models.
Module 1 will Introduction to Deep Learning and in it, you will learn, you will learn: -Why Deep Learning?, What is a neural network?, Three reasons to go Deep. Your choice of Deep Net, An old problem: The Vanishing Gradient.
Module 2 will Deep Learning Models and in it, you will learn, Restricted Boltzmann Machines, Deep Belief Nets, Convolutional Networks, Recurrent Nets
Module 3 will be Additional Deep Learning Models and in it, you will learn Autoencoders, Recursive Neural Tensor Nets, Deep Learning Use Cases
Module 4 will be Deep Learning Platforms and Software Libraries and in it, you will learn What is a Deep Learning Platform?, H2O.ai, Dato GraphLab, What is a Deep Learning Library?, Theano, Caffe, TensorFlow.
Though this course does not have any hands-on lab, still you can try PowerAI to understand different deep learning libraries better. PowerAI speeds up deep learning and AI. Built on IBM’s Power Systems.
PowerAI is a scalable software platform that accelerates deep learning and AI with a blazing performance for individual users or enterprises. PowerAI platform supports popular machine learning libraries and dependencies including Tensorflow, Caffe, Torch, and Theano3.
2. Intro to TensorFlow for Deep Learning
Intro to TensorFlow for Deep Learning is a practically oriented course that focuses to first build the basic concepts of deep learning and on top of that introduce new and advanced concepts, software developers.
Udacity is teaching this course, so there is no issue with the quality of content. In this deep learning course, you will learn how to build deep learning applications with TensorFlow and deploy it.
This course was developed by the TensorFlow team and Udacity as a practical approach to deep learning for software developers. You’ll get hands-on experience building your own state-of-the-art image classifiers and other deep learning models.
You’ll also use your TensorFlow models in the real world on mobile devices, in the cloud, and in browsers. Finally, you’ll use advanced techniques and algorithms to work with large datasets.
By the end of this course, you’ll have all the skills necessary to start creating your own AI applications.
The instructor of this course will be Magnus Hyttsten, he is a Developer Advocate, Google, Juan Delgado she is a Content Developer at Udacity and Paige Bailey is also a Developer Advocate at Google.
3. Deep Learning with TensorFlow
All the knowledge that you have gained in the above two deep learning courses will help you in these courses to learn more major concepts.
TensorFlow is one of the most useful tools and frameworks for deep learning and in this course, you will take your skills with this tool to a whole new level where you can not only build an application but also solve complex problems.
Traditional neural networks rely on shallow nets, composed of one input, one hidden layer, and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth.
These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs.
Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations, and the execution pipeline.
Starting with a simple “Hello World” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.
This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.
Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks, and Autoencoders.
Your instructor will be Saeed Aghabozorgi, he is a Data Scientist in IBM with a track record of developing enterprise-level applications that substantially increase clients’ ability to turn data into actionable knowledge.
He is a researcher in the data mining field and expert in developing advanced analytic methods like deep learning, machine learning and statistical modeling on large datasets.
4. Hugo Larochelle Course on Neural Networks
This one of my favorite courses and it offers not only good teaching but also a lost assignment to do also. Neural Networks is one of the important parts of Deep Learning and you must mater it.
This is a graduate-level course, which covers basic neural networks as well as more advanced topics, including:
- Deep learning.
- Conditional random fields.
- Restricted Boltzmann machines.
- Sparse coding.
- Convolutional networks.
- Vector word representations.
- and many more…
This course is offered by Université de Sherbrooke and taught by Hugo Larochelle.
In the Content section, you’ll find links to video clips describing these different concepts, as well as recommended readings. The content is laid out into sections that should correspond to about one week’s worth of work.
In the Evaluations section, you’ll find 3 programming assignments, in Python, that I use in my class. They are good opportunities to put into practice some of the concepts covered by the course.
5. Convolutional Neural Networks for Visual Recognition
The focus of the course is the use of convolutional neural networks (CNNs) for computer vision problems and how the CNNs work. This course will also teach you image classification and recognition tasks, and introduction to advanced applications such as generative models and deep reinforcement learning.
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars.
Core to many of these applications is visual recognition tasks such as image classification, localization, and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems.
This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification.
During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.
The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet).
We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project.
Much of the background and materials of this course will be drawn from the ImageNet Challenge.
The breakdown of the lectures in the course including the three lectures to focus on if you are already familiar with deep learning.
The course is taught by Fei-Fei Li, a famous computer vision researcher at the Stanford Vision Lab and more recently as a Chief Scientist at Google.
Through 2015-16, the course was co-taught by Andrej Karpathy, now at Tesla. Justin Johnson has also been involved since the beginning and has co-taught with Serena Yeung through 2017 to 2018.
6. Natural Language Processing with Deep Learning
Natural Language Processing one of the most important applications of Deep Learning and if you want to crush DL you must need to know how to use and apply this field to solve real-life problems.
Natural Language Processing with Deep Learning, is a course offered by STANFORD SCHOOL OF ENGINEERING, and your instructor of this course will be Christopher Manning, I’m a great fan of him,
Christopher Manning is a Professor in the Computer Science and Linguistics departments. Manning works on systems that can intelligently process and produce human languages.
This course will help to investigate the fundamental concepts and ideas in natural language processing (NLP) and get up to speed with current research.
Students will develop an in-depth understanding of both the algorithms available for processing linguistic information and the underlying computational properties of natural languages.
The focus is on deep learning approaches: implementing, training, debugging, and extending neural network models for a variety of language understanding tasks. The course progresses from word-level and syntactic processing to question answering and machine translation.
For their final project, students will apply a complex neural network model to a large-scale NLP problem. The prerequisites for this course will be Calculus and linear algebra & CS124, or CS121/CS221.
The topics that you will learn in this course will be:
- Computational properties of natural languages
- Coreference, question answering, and machine translation
- Processing linguistic information
- Syntactic and semantic processing
- Modern quantitative techniques in NLP
- Neural network models for language understanding task
7. Accelerating Deep Learning with GPU
You have mastered the TensorFlow and Deep Learning, and now its time to train complex deep learning models with huge data. You should expect hours, days or weeks sometimes to train a complex model with a large dataset. So, what is the solution?
Well, you should use accelerated hardware, for example, you can use Google’s Tensor Processing Unit (TPU) or Nvidia GPU to accelerate your convolutional neural network computations time on the could.
These chips are particularly designed to support the training of neural networks, as well as the use of trained networks (inference). These accelerating hardware have recently succeeded to reduce the training time several times.
But the problem is that your data might be sensitive and you may not feel comfortable to upload it into the public cloud, and you need to analyze it on-premise. In this case, you need to use an in-house system with GPU support. One solution is using IBM’s Power Systems with Nvidia GPU, and PowerAI.
Source: TechgrabyteRelated posts: