Overview
The global deep learning market is set to grow 23% annually to 2030 (Grand View Research). This IBM Deep Learning with PyTorch, Keras and TensorFlow Professional Certificate builds the job-ready skills and practical experience AI techies need to catch the eye of employers.
Deep learning is a branch of machine learning powering the generative AI revolution. It uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain.
During the program, you’ll learn to build, train, and deploy deep learning models. You’ll master fundamental concepts of machine learning and deep learning, including supervisedlearning, using Python. You’ll learn to develop transformer models for sequential data and time series predictions and apply unsupervised learning and reinforcement learning. Plus, you’ll apply popular libraries such as Keras, PyTorch, and TensorFlow to industry problems using object recognition,image and natural language processing. You’ll also gain valuable hands-on experience in labs and projects using PyTorch with deep learning models, creating custom layers and models using Keras, integrating Keras with TensorFlow 2, and developing advanced convolutional neural networks (CNNs). If you’re looking to take the next step in your AI or data science career, this IBM Professional Certificate will give you job-ready skills and practical experience employers are looking for, so ENROLL TODAY!
Syllabus
Course 1: Introduction to Deep Learning & Neural Networks with Keras
- Offered by IBM. Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning ... Enroll for free.
Course 2: Deep Learning with Keras and Tensorflow
- Offered by IBM. Deep learning is revolutionizing many fields, including computer vision, natural language processing, and robotics. In ... Enroll for free.
Course 3: Introduction to Neural Networks and PyTorch
- Offered by IBM. PyTorch is one of the top 10 highest paid skills in tech (Indeed). As the use of PyTorch for neural networks rockets, ... Enroll for free.
Course 4: Deep Learning with PyTorch
- Offered by IBM. This course advances from fundamental machine learning concepts to more complex models and techniques in deep learning using ... Enroll for free.
Course 5: AI Capstone Project with Deep Learning
- Offered by IBM. In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use ... Enroll for free.
- Offered by IBM. Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning ... Enroll for free.
Course 2: Deep Learning with Keras and Tensorflow
- Offered by IBM. Deep learning is revolutionizing many fields, including computer vision, natural language processing, and robotics. In ... Enroll for free.
Course 3: Introduction to Neural Networks and PyTorch
- Offered by IBM. PyTorch is one of the top 10 highest paid skills in tech (Indeed). As the use of PyTorch for neural networks rockets, ... Enroll for free.
Course 4: Deep Learning with PyTorch
- Offered by IBM. This course advances from fundamental machine learning concepts to more complex models and techniques in deep learning using ... Enroll for free.
Course 5: AI Capstone Project with Deep Learning
- Offered by IBM. In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use ... Enroll for free.
Courses
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PyTorch is one of the top 10 highest paid skills in tech (Indeed). As the use of PyTorch for neural networks rockets, professionals with PyTorch skills are in high demand. This course is ideal for AI engineers looking to gain job-ready skills in PyTorch that will catch the eye of an employer. AI developers use PyTorch to design, train, and optimize neural networks to enable computers to perform tasks such as image recognition, natural language processing, and predictive analytics. During this course, you’ll learn about 2-D Tensors and derivatives in PyTorch. You’ll look at linear regression prediction and training and calculate loss using PyTorch. You’ll explore batch processing techniques for efficient model training, model parameters, calculating cost, and performing gradient descent in PyTorch. Plus, you’ll look at linear classifiers and logistic regression. Throughout, you’ll apply your new skills in hands-on labs, and at the end, you’ll complete a project you can talk about in interviews. If you’re an aspiring AI engineer with basic knowledge of Python and mathematical concepts, who wants to get hands-on with PyTorch, enroll today and get set to power your AI career forward!
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Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library. After completing this course, learners will be able to: • Describe what a neural network is, what a deep learning model is, and the difference between them. • Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines. • Demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks. • Build deep learning models and networks using the Keras library.
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In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.
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Deep learning is revolutionizing many fields, including computer vision, natural language processing, and robotics. In addition, Keras, a high-level neural networks API written in Python, has become an essential part of TensorFlow, making deep learning accessible and straightforward. Mastering these techniques will open many opportunities in research and industry. You will learn to create custom layers and models in Keras and integrate Keras with TensorFlow 2.x for enhanced functionality. You will develop advanced convolutional neural networks (CNNs) using Keras. You will also build transformer models for sequential data and time series using TensorFlow with Keras. The course also covers the principles of unsupervised learning in Keras and TensorFlow for model optimization and custom training loops. Finally, you will develop and train deep Q-networks (DQNs) with Keras for reinforcement learning tasks (an overview of Generative Modeling and Reinforcement Learning is provided). You will be able to practice the concepts learned using hands-on labs in each lesson. A culminating final project in the last module will provide you an opportunity to apply your knowledge to build a Classification Model using transfer learning. This course is suitable for all aspiring AI engineers who want to learn TensorFlow and Keras. It requires a working knowledge of Python programming and basic mathematical concepts such as gradients and matrices, as well as fundamentals of Deep Learning using Keras.
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This course advances from fundamental machine learning concepts to more complex models and techniques in deep learning using PyTorch. This comprehensive course covers techniques such as Softmax regression, shallow and deep neural networks, and specialized architectures, such as convolutional neural networks. In this course, you will explore Softmax regression and understand its application in multi-class classification problems. You will learn to train a neural network model and explore Overfitting and Underfitting, multi-class neural networks, backpropagation, and vanishing gradient. You will implement Sigmoid, Tanh, and Relu activation functions in Pytorch. In addition, you will explore deep neural networks in Pytorch using nn Module list and convolution neural networks with multiple input and output channels. You will engage in hands-on exercises to understand and implement these advanced techniques effectively. In addition, at the end of the course, you will gain valuable experience in a final project on a convolutional neural network (CNN) using PyTorch. This course is suitable for all aspiring AI engineers who want to gain advanced knowledge on deep learning using PyTorch. It requires some basic knowledge of Python programming and basic mathematical concepts such as gradients and matrices.
Taught by
Alex Aklson, JEREMY NILMEIER, Joseph Santarcangelo, Ricky Shi, Romeo Kienzler, Samaya Madhavan and Wojciech 'Victor' Fulmyk