Do you want to learn Deep Learning? Here we listed Deep Learning tutorials which will help you learn Deep Learning from scratch, and are suitable for beginners, intermediate learners as well as experts
FREE-Basics of Deep Learning
What you’ll learn
- Basics of Deep Learning
- evolution of deep neural network and their application in areas like image recognition, natural language processing etc
Duration: 1hr 46min
Rating: 4.5 (690 ratings) out of 5
Trainer: Sunil Kumar Mishra
Deep Learning A-Z™ 2023: Neural Networks, AI & ChatGPT Bonus
What you’ll learn
- Understand the intuition behind Artificial Neural Networks
- Apply Artificial Neural Networks in practice
- Understand the intuition behind Convolutional Neural Networks
- Apply Convolutional Neural Networks in practice
- Understand the intuition behind Recurrent Neural Networks
- Apply Recurrent Neural Networks in practice
- Understand the intuition behind Self-Organizing Maps
- Apply Self-Organizing Maps in practice
- Understand the intuition behind Boltzmann Machines
- Apply Boltzmann Machines in practice
- Understand the intuition behind AutoEncoders
- Apply AutoEncoders in practice
Duration : 22.5 hours
Rating:4.6
Trainer: Kirill Eremenko
Data Science: Deep Learning and Neural Networks in Python
What you’ll learn
- Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)
- Learn how a neural network is built from basic building blocks (the neuron)
- Code a neural network from scratch in Python and numpy
- Code a neural network using Google’s TensorFlow
- Describe different types of neural networks and the different types of problems they are used for
- Derive the backpropagation rule from first principles
- Create a neural network with an output that has K > 2 classes using softmax
- Describe the various terms related to neural networks, such as “activation”, “backpropagation” and “feedforward”
- Install TensorFlow
Duration : 12 hours
Rating: 4.6
Trainer: Lazy Programmer
Deep Learning: Convolutional Neural Networks in Python
What you’ll learn
- Understand convolution and why it’s useful for Deep Learning
- Understand and explain the architecture of a convolutional neural network (CNN)
- Implement a CNN in TensorFlow 2
- Apply CNNs to challenging Image Recognition tasks
- Apply CNNs to Natural Language Processing (NLP) for Text Classification (e.g. Spam Detection, Sentiment Analysis)
Duration : 12 hours
Rating: 4.6
Trainer: Lazy Programmer
Deep Learning Prerequisites: The Numpy Stack in Python (V2+)
What you’ll learn
- Understand supervised machine learning (classification and regression) with real-world examples using Scikit-Learn
- Understand and code using the Numpy stack
- Make use of Numpy, Scipy, Matplotlib, and Pandas to implement numerical algorithms
- Understand the pros and cons of various machine learning models, including Deep Learning, Decision Trees, Random Forest, Linear Regression, Boosting, and More!
Duration : 6 hours
Rating: 4.6
Trainer: Lazy Programmer
Modern Deep Learning in Python
What you’ll learn
- Apply momentum to backpropagation to train neural networks
- Apply adaptive learning rate procedures like AdaGrad, RMSprop, and Adam to backpropagation to train neural networks
- Understand the basic building blocks of Theano
- Build a neural network in Theano
- Understand the basic building blocks of TensorFlow
- Build a neural network in TensorFlow
- Build a neural network that performs well on the MNIST dataset
- Understand the difference between full gradient descent, batch gradient descent, and stochastic gradient descent
- Understand and implement dropout regularization in Theano and TensorFlow
- Understand and implement batch normalization in Theano and Tensorflow
- Write a neural network using Keras
- Write a neural network using PyTorch
- Write a neural network using CNTK
- Write a neural network using MXNet
Duration : 11.5 hours
Rating: 4.7
Trainer: Lazy Programmer
Deep Learning Prerequisites: Logistic Regression in Python
What you’ll learn
- program logistic regression from scratch in Python
- describe how logistic regression is useful in data science
- derive the error and update rule for logistic regression
- understand how logistic regression works as an analogy for the biological neuron
- use logistic regression to solve real-world business problems like predicting user actions from e-commerce data and facial expression recognition
- understand why regularization is used in machine learning
Duration : 6.5 hours
Rating: 4.7
Trainer: Lazy Programmer
Deep Learning: GANs and Variational Autoencoders
What you’ll learn
- Learn the basic principles of generative models
- Build a variational autoencoder in Theano and Tensorflow
- Build a GAN (Generative Adversarial Network) in Theano and Tensorflow
Duration : 7.5 hours
Rating: 4.7
Trainer: Lazy Programmer
Deep Learning with PyTorch for Medical Image Analysis
What you’ll learn
- Learn how to use NumPy
- Learn classic machine learning theory principals
- Foundations of Medical Imaging
- Data Formats in Medical Imaging
- Creating Artificial Neural Networks with PyTorch
- Use PyTorch-Lightning for state of the art training
- Visualize the decision of a CNN
- 2D & 3D data handling
- Automatic Cancer Segmentation
Duration : 12 hours
Rating: 4.6
Trainer: Jose Portilla
Deep Learning Specialization
What you’ll learn
- Learn about convolutional networks, RNNs, BatchNorm, Dropout and more.
- Different techniques using which you can build models to solve real-life problems.
- Real-world case studies in fields such as healthcare, autonomous driving, sign language reading, music generation, and natural language processing are covered.
Trainer: deeplearning.ai Rating: 4.8 (203,169 ratings)
Neural Networks and Deep Learning
What you’ll learn
- Understand the major technology trends driving Deep Learning
- Be able to build, train and apply fully connected deep neural networks
- Know how to implement efficient (vectorized) neural networks
- Understand the key parameters in a neural network’s architecture
Duration: Approx. 20 hours
Rating: 4.9 (72,798 ratings) out of 5
Trainer: deeplearning.ai
Deep Learning Explained
What you’ll learn
- The components of a deep neural network and how they work together
- The basic types of deep neural networks (MLP, CNN, RNN, LSTM) and the type of data each is designed for
- A working knowledge of vocabulary, concepts, and algorithms used in deep learning
Duration: Approx. 25 hours Trainer: Steve Elston URL: https://www.edx.org/course/deep-learning-explained-2
Deep Learning Prerequisites: The Numpy Stack in Python
What you’ll learn
- Understand supervised machine learning (classification and regression) with real-world examples using Scikit-Learn
- Understand and code using the Numpy stack
- Make use of Numpy, Scipy, Matplotlib, and Pandas to implement numerical algorithms
- Understand the pros and cons of various machine learning models, including Deep
- Learning, Decision Trees, Random Forest, Linear Regression, Boosting, and More!
Duration: 3hr 31min Rating: 4.5 (17,018 ratings) out of 5 Trainer: Lazy Programmer Inc Enroll Now
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