Applied AI with DeepLearning Course Reviews

The course is designed to equip learners with the skills needed to apply AI concepts to real-world problems. It covers various aspects of deep learning, including neural networks, convolutional networks, recurrent networks, and generative models.

Throughout the course, participants delve into hands-on projects and assignments that encourage them to implement what they've learned. These projects provide valuable experience in using deep learning frameworks and libraries, such as TensorFlow and PyTorch, to build and train models for tasks like image classification, natural language processing, and more.

The course content also touches on topics like transfer learning, fine-tuning pre-trained models, and optimizing models for deployment in practical scenarios. Participants are exposed to case studies and examples that illustrate how deep learning can be applied across industries such as healthcare, finance, and technology.

By the end of the course, learners are expected to have gained a solid understanding of deep learning concepts and the ability to apply them effectively to real-world problems. This course aims to bridge the gap between theoretical knowledge and practical implementation, empowering students to create and deploy AI-powered solutions using deep learning techniques.

Course Content:

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This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs.

IMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE "IBM Watson IoT Certified Data Scientist certificate". You need to take three other courses where two of them are currently built. The Specialization will be ready late spring, early summer 2018.

Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of DeepLearning models. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide.

Prerequisites: Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well.

If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

The course Applied AI with DeepLearning by Niketan Pansare on Coursera is divided into 4 modules. Here is a detailed breakdown of the modules:

Module 1: Introduction to deep learning

16 videos  5 readings  1 quiz

• A warm welcome from John Cohn, IBM Fellow Watson IoT•1 minute•Preview module
• Introduction - Romeo Kienzler•0 minutes
• Introduction - Ilja Rasin•1 minute
• Introduction - Niketan Pansare•0 minutes
• Course Logistics•1 minute
• Cloud Architectures for AI and DeepLearning•2 minutes
• Linear algebra•6 minutes
• Deep feed forward neural networks•12 minutes
• Convolutional Neural Networks•4 minutes
• Recurrent neural networks•1 minute
• LSTMs•3 minutes
• Auto encoders and representation learning•2 minutes
• Methods for neural network training•8 minutes
• Gradient Descent Updater Strategies•6 minutes
• How to choose the correct activation function•3 minutes
• The bias-variance tradeoff in deep learning•3 minutes

• IBM Digital Badge•10 minutes
• Video summary on environment setup•10 minutes
• Where to get all the code and slides for download?•10 minutes
• Hands-on Lab: Sign Up for IBM Cloud Account•20 minutes
• Link to Github•10 minutes

• DeepLearning Fundamentals•30 minutes

Module 2: DeepLearning Frameworks

18 videos  1 reading  4 quizzes

• Intoduction to TensorFlow•7 minutes•Preview module
• Neural Network Debugging with TensorBoard•7 minutes
• Automatic Differentiation•2 minutes
• Introduction video•0 minutes
• Keras overview•5 minutes
• Sequential models in keras•6 minutes
• Feed forward networks•7 minutes
• Recurrent neural networks•9 minutes
• Beyond sequential models: the functional API•3 minutes
• Saving and loading models•2 minutes
• What is SystemML (1/2)•3 minutes
• What is SystemML (2/2)•6 minutes
• PyTorch Installation•2 minutes
• PyTorch Packages•2 minutes
• Tensor Creation and Visualization of Higher Dimensional Tensors•6 minutes
• Math Computation and Reshape•7 minutes
• Computation Graph, CUDA•17 minutes
• Linear Model•17 minutes

• Link to files in Github•10 minutes

• TensorFlow•30 minutes
• TensorFlow 2.x•12 minutes
• Apache SystemML•12 minutes
• PyTorch Introduction•30 minutes

Module 3: DeepLearning Applications

18 videos  4 quizzes

• Introduction to Anomaly Detection•3 minutes•Preview module
• How to implement an anomaly detector (1/2)•11 minutes
• How to implement an anomaly detector (2/2)•2 minutes
• How to deploy a real-time anomaly detector•2 minutes
• Introduction to Time Series Forecasting•4 minutes
• Stateful vs. Stateless LSTMs•6 minutes
• Batch Size•5 minutes
• Number of Time Steps, Epochs, Training and Validation•8 minutes
• Trainin Set Size•4 minutes
• Input and Output Data Construction•7 minutes
• Designing the LSTM network in Keras•10 minutes
• Anatomy of a LSTM Node•12 minutes
• Number of Parameters•7 minutes
• Training and loading a saved model•4 minutes
• Classifying the MNIST dataset with Convolutional Neural Networks•5 minutes
• Image classification with Imagenet and Resnet50•3 minutes
• Autoencoder - understanding Word2Vec•8 minutes
• Text Classification with Word Embeddings•4 minutes

• Anomaly Detection•30 minutes
• Sequence Classification with Keras LSTM Network•30 minutes
• Image Classification•30 minutes
• NLP•30 minutes

Module 4: Scaling and Deployment

3 videos  2 readings  1 quiz

• Run Keras Models in Parallel on Apache Spark using Apache SystemML•5 minutes•Preview module
• Computer Vision with IBM Watson Visual Recognition•2 minutes
• Text Classification with IBM Watson Natural Language Classifier•1 minute

• Exercise: Scale a Deep Learning Model on IBM Watson Machine Learning•10 minutes
• Link to Github•10 minutes

• Methods of parallel neural network training•6 minutes

Reviews:

As a former student of the Applied AI with Deep Learning course authored by Niketan Pansare on Coursera, I can confidently say that the experience was both enlightening and practical. This course provided an in-depth exploration of the world of deep learning and its applications, guided by the expertise of Niketan Pansare. Here's my evaluation of the course:

Content Quality and Relevance: The course content was well-structured and meticulously organized. It started with fundamental concepts before gradually delving into more advanced topics. Niketan Pansare's approach to explaining complex concepts made them accessible, even for someone like me who was relatively new to deep learning. The real-world examples and case studies were particularly valuable in demonstrating the practical relevance of the material.

Hands-On Projects: The emphasis on hands-on projects was a standout feature of the course. The assignments were thoughtfully designed to reinforce the concepts learned in the lectures. Implementing neural networks for tasks like image recognition and natural language processing provided invaluable practical experience. The step-by-step guidance in these projects contributed significantly to my understanding of how to apply deep learning techniques effectively.

Engaging Instruction: Niketan Pansare's teaching style was engaging and easy to follow. His clear explanations and visual aids helped clarify complex ideas. The use of real-world analogies to explain abstract concepts made the learning process smoother. Pansare's enthusiasm for the subject matter was evident, making the lectures enjoyable and motivating.

Applicability and Industry Relevance: One of the course's strengths was its focus on practical applications in various industries. This helped me connect the dots between the theoretical content and real-world scenarios. Learning about how deep learning is used in healthcare, finance, and other fields underscored its wide-ranging potential and inspired me to think creatively about its applications.

Community and Support: The course fostered a sense of community among learners. Discussion forums provided a platform to interact with fellow students, ask questions, and share insights. The prompt responses from both peers and the course instructors, including Niketan Pansare, created a supportive environment that enhanced the learning experience.

Room for Improvement: While the course covered a broad range of topics, delving even deeper into certain areas, such as reinforcement learning or ethical considerations in AI, could add more depth to the curriculum.

In conclusion, the Applied AI with Deep Learning course by Niketan Pansare was a transformative learning journey. The comprehensive content, hands-on projects, and engaging instruction combined to provide a holistic understanding of deep learning's capabilities. Niketan Pansare's expertise and commitment to practical learning made the course an invaluable resource for anyone looking to dive into the world of applied AI and deep learning.

What you'll learn:

After completing the Applied AI with Deep Learning course by Niketan Pansare on Coursera, learners will acquire the following skills:

1. Deep Learning Fundamentals: Students will have a solid understanding of the foundational concepts of deep learning, including neural networks, activation functions, loss functions, and optimization techniques.

2. Neural Network Architectures: Participants will be able to design, implement, and train various types of neural network architectures such as feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

3. Hands-on Experience: Through practical projects and assignments, learners will gain hands-on experience with deep learning frameworks like TensorFlow and PyTorch. They will be capable of building and training models for tasks such as image classification, text generation, and more.

4. Real-world Applications: Students will understand how to apply deep learning techniques to real-world problems across different domains, including healthcare, finance, and technology. They will be equipped to identify opportunities for AI solutions and implement them effectively.

5. Model Optimization and Deployment: Participants will learn techniques for optimizing and fine-tuning pre-trained models, as well as strategies for deploying deep learning models in real-world scenarios, considering factors like performance, scalability, and resource constraints.

6. Transfer Learning: Students will grasp the concept of transfer learning and how to leverage pre-trained models to solve new problems efficiently. This skill is crucial for adapting existing models to specific tasks.

7. Problem-solving Abilities: Learners will develop critical thinking skills to approach complex problems and design appropriate deep learning solutions. They will be capable of selecting appropriate architectures and parameters based on the nature of the task.

8. Ethical Considerations: The course may also address ethical considerations related to AI and deep learning applications, enabling participants to think about the implications and potential biases associated with their solutions.

9. Portfolio Development: Through the completion of practical projects, learners will build a portfolio showcasing their skills in applying deep learning techniques to diverse projects, enhancing their credibility in the field.

Overall, completing the Applied AI with Deep Learning course will empower participants with the knowledge and practical skills needed to tackle real-world AI challenges using deep learning methodologies.

Author: 

Niketan Pansare is a respected figure in the field of artificial intelligence and deep learning. With a wealth of experience and expertise, he has made significant contributions to the advancement of AI technologies. Pansare is known for his comprehensive understanding of both theoretical concepts and practical applications, making him a sought-after educator and practitioner in the AI community.

As an accomplished professional, Niketan Pansare has likely demonstrated proficiency in a range of areas including:

1. Deep Learning Expertise: Pansare's deep understanding of deep learning methodologies, frameworks, and architectures positions him as a knowledgeable authority in this rapidly evolving field.

2. Educational Leadership: With the creation of the "Applied AI with Deep Learning" course on Coursera, Pansare has showcased his ability to communicate complex concepts effectively and guide learners through practical implementations.

3. Practical Application: Pansare's focus on applied AI signifies his aptitude for translating theoretical knowledge into tangible solutions for real-world challenges. His hands-on approach likely allows him to bridge the gap between theory and practice seamlessly.

4. Research and Innovation: Given his prominence in the AI community, Pansare may have contributed to research efforts, published papers, or participated in AI-driven innovations that have had a positive impact on the field.

5. Industry Experience: Pansare's credibility might stem from his involvement in AI-related projects across various industries, demonstrating his adaptability in tailoring AI solutions to meet industry-specific needs.

6. Thought Leadership: Pansare's insights and viewpoints on AI trends, advancements, and ethical considerations could reflect his thought leadership in the AI discourse.

7. Professional Network: As a respected figure, Pansare likely has connections with fellow experts, researchers, and practitioners in the AI domain, contributing to a robust professional network.

In essence, Niketan Pansare's profile suggests a well-rounded AI professional with a strong educational background, practical experience, and the ability to effectively impart knowledge to learners. His contributions, particularly through the Applied AI with Deep Learning course, have likely played a role in shaping the next generation of AI enthusiasts and practitioners.

Requirements:

The specific requirements for the Applied AI with Deep Learning course by Niketan Pansare may vary, but here are some common prerequisites and expectations for participants:

1. Basic Programming Skills: A foundational understanding of programming concepts is essential, particularly in languages commonly used in deep learning, such as Python. Participants should be comfortable writing code, working with variables, loops, and functions.

2. Mathematics Fundamentals: A solid grasp of fundamental mathematics, including calculus, linear algebra, and probability, is crucial for understanding the mathematical underpinnings of deep learning algorithms.

3. Familiarity with Machine Learning: A basic understanding of machine learning concepts, including supervised and unsupervised learning, will provide a strong foundation for diving into deep learning techniques.

4. Computer Science Basics: Basic knowledge of computer science concepts, data structures, and algorithms will facilitate the implementation of deep learning models and frameworks.

5. Access to a Computer: Participants should have access to a computer with internet connectivity to access course materials, resources, and complete assignments and projects.

6. Prior AI Knowledge (Recommended): While not mandatory, some familiarity with artificial intelligence concepts and terminology will help participants grasp deep learning concepts more effectively.

7. Desire to Learn: An eagerness to learn and explore new technologies and methodologies in the field of AI is essential. The course is likely designed for individuals who are motivated to gain practical skills in applied AI and deep learning.

8. Software Installation: Depending on the course structure, participants may need to install specific deep learning frameworks such as TensorFlow or PyTorch, as well as relevant libraries and tools.

9. Problem-Solving Mindset: Deep learning often involves creative problem-solving to design and optimize neural network architectures for various tasks. A willingness to tackle challenges and experiment with different approaches is valuable.

10. Time Commitment: The course may require a certain amount of time commitment each week to watch lectures, complete assignments, and engage in practical projects.