11 Best Udemy Deep Learning Courses, Tutorials and Trainings in 20247 min read

• • How Neural Networks Work
  • Build your own Neural Network from Scratch with Python
  • TensorFlow for Classification and Regression Tasks
  • Use TensorFlow for Image Classification with Convolutional Neural Networks
  • Time Series Analysis with Recurrent Neural Networks
  • Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders
  • Learn how to conduct Reinforcement Learning with OpenAI Gym
  • Create Generative Adversarial Networks with TensorFlow

Students Enrolled: 96.1K+

Instructor: Jose Portilla

Ratings: 4.6 out of 5.0

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5. Zero to Deep Learning™ with Python and Keras (Udemy)

BEST SELLER

Understand and build Deep Learning models for images, text, sound, and more using Python and Keras

You will be able to learn with this Deep Learning Course:

• • • How deep learning can be used to build predictive models
    • Practical applications in deep learning
    • To install and use Python and Keras to build deep-learning models
    • Applying deep learning to solve supervised and unsupervised learning problems
    • To build, train, and usefully connected, convolutional and recurrent neural networks
    • Train and run models in the cloud using a GPU
    • To estimate training costs for large models

Students Enrolled: 24.0K+

Instructor: Data Weekends, joss Portilla, Francesco Mosconi

Ratings: 4.6 out of 5.0

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6. Natural Language Processing with Deep Learning in Python (Udmey)

BEST SELLER

Complete guide on deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets.

You will be able to learn with this Deep Learning Course:

• • • Understand and implement word2vec
    • CBOW method in word2vec
    • The skip-gram method in word2vec
    • Negative sampling optimization in word2vec
    • Implement GloVe using gradient descent and alternating least squares
    • Recurrent neural networks for parts-of-speech tagging
    • Recurrent neural networks for named entity recognition
    • Recursive neural networks for sentiment analysis
    • Implement recursive neural tensor networks for sentiment analysis
    • To obtain pre-trained word vectors and compute similarities and analogies

Enrollment: 46.8K+

Instructor: Lazy Programmer Inc.

Ratings: 4.7 out of 5.0

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7. Modern Deep Learning in Python (Udemy)

BEST SELLER

Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, and MXNet. Train faster with GPU on AWS.

You will be able to learn with this Deep Learning Course:

• • • Backpropagation to train neural networks
    • Adaptive learning rate procedures like AdaGrad, RMSprop, and Adam to backpropagation to train neural networks
    • The basic building blocks of Theano
    • Neural network in Theano
    • The basic building blocks of TensorFlow
    • Neural network in TensorFlow
    • A neural network that performs well on the MNIST dataset
    • The difference between full gradient descent, batch gradient descent, and stochastic gradient descent
    • Dropout regularization in Theano and TensorFlow
    • Batch normalization in Theano and Tensorflow

Students Enrolled: 37.1K+

Instructor: Lazy Programmer Inc.

Ratings: 4.6 out of 5.0

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8. Deep Learning: Recurrent Neural Networks in Python (Udemy)

GRU, LSTM, Time Series Forecasting, Stock Predictions, and Natural Language Processing (NLP) using Artificial Intelligence.

You will be able to learn with this Deep Learning Course:

• • • Apply RNNs to Time Series Forecasting (tackle the ubiquitous “Stock Prediction” problem)
    • Natural Language Processing (NLP) and Text Classification (Spam Detection)
    • Apply RNNs to Image Classification
    • The simple recurrent unit (Elman unit), GRU, and LSTM (long short-term memory unit)
    • Various recurrent networks in Tensorflow 2
    • How to mitigate the vanishing gradient problem

Students Enrolled: 37.1K+

Instructor: Lazy Programmer Inc.

Ratings: 4.7 out of 5.0

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9. Convolutional Neural Networks in Python (Udemy)

Use CNN for Image Recognition, Natural Language Processing (NLP) +More! For Data Science, Machine Learning, and AI

You will be able to learn with this Deep Learning Course:

• • • Convolution and why it’s useful for Deep Learning
    • Explain the architecture of a convolutional neural network (CNN)
    • Implement a CNN in TensorFlow 2
    • Apply CNNs to challenging Image Recognition tasks
    • Natural Language Processing (NLP) for Text Classification

Students Enrolled: 39.2K+

Instructor: Lazy Programmer Inc.

Ratings: 4.6  out of 5.0

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10. Deep Learning Certification: Unsupervised Deep Learning in Python (Udemy)

Theano / Tensorflow: Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE, and PCA.

You will be able to learn with this Deep Learning Course:

• • • The theory behind principal components analysis (PCA)
    • Derive the PCA algorithm by hand
    • The code for PCA
    • The theory behind t-SNE
    • Use t-SNE in code
    • Limitations of PCA and t-SNE
    • The theory behind autoencoders
    • Autoencoder in Theano and Tensorflow
    • How stacked autoencoders are used in deep learning
    • Stacked denoising autoencoder in Theano and Tensorflow
    • The theory behind restricted Boltzmann machines (RBMs)
    • Why RBMs are hard to train
    • Understand the contrastive divergence algorithm to train RBMs
    • Visualize and interpret the features learned by autoencoders and RBMs

Students Enrolled: 22.4K+

Instructor: Lazy Programmer Inc.

Ratings: 4.7 out of 5.0

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11. Data Science: Deep Learning in Python (Udemy)

The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow.

You will learn:

• • • How Deep Learning REALLY works (not just some diagrams and magical black box code)
    • A neural network is built from basic building blocks (the neuron)
    • Neural network from scratch in Python and NumPy
    • Code a neural network using Google’s TensorFlow
    • Different types of neural networks
    • Backpropagation rule from first principles
    • A neural network with an output that has K > 2 classes using softmax
    • The various terms related to neural networks, such as “activation”, “backpropagation” and “feedforward”
    • Install TensorFlow

Students Enrolled: 56.1K+

Instructor: Lazy Programmer Inc.

Ratings: 4.7 out of 5.0

Enroll Now

Here are the Best Deep Learning Courses. Grab the list of Best Deep Learning Tutorials, Classes, Certifications, and Training online. Happy Learning

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