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\n\tWhat will you learn?<\/h2><\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n\n\t\t\n\t\t\t
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\n\tAfter the training, you will be able to:<\/p>\t
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\n\t <\/svg><\/i>\n\tBuild and train your own neural networks with TensorFlow\/Keras.<\/p><\/div>\n\n\n\n
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\n\t <\/svg><\/i>\n\tUnderstand Recurrent Neural Networks: From simple RNNs to modern Transformers.<\/p><\/div>\n\n\n\n
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\n\t <\/svg><\/i>\n\tIntuitively understand the theory behind Deep Learning: What are neurons, how do neural networks learn, and what other components do I need?<\/p><\/div>\n\n\n\n
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\n\t <\/svg><\/i>\n\tApply best practices to ensure your model trains optimally like automatic data augmentations, memory-efficient training and optimized model architectures.<\/p><\/div>\n\n\n\n
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\n\t <\/svg><\/i>\n\tBuild Convolutional Neural Networks and apply them to images.<\/p><\/div>\n\n\n\n
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\n\t <\/svg><\/i>\n\tUnderstand how you can use Transfer Learning to boost your model performance.<\/p><\/div>\n\n\t<\/div>\n<\/div>\n\t\t<\/div>\n\t<\/div>\n\n<\/div>\n\n<\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n
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\n\t\t\n\tDay 1<\/h2>\t\t \n\tDeep Learning Fundamentals<\/p>\n\t\t\n\t\t\t\n\t <\/svg><\/i>\t\t<\/span>\n\t<\/button>\n\n\t\n\t\t
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\n\tHistory: The rise of Deep Learning and its current-day applications<\/li> How do neural networks learn: (Stochastic) Gradient Descent<\/li> Deconstruct a neural network: What are neurons and how do they build a network?<\/li> Fundamental components of neural networks: From loss functions to dropout layers<\/li> Hands-on: Build your first Deep Learning model\u00a0in Tensorflow<\/li><\/ul><\/div>\n\t\t<\/div>\n\t<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n\n\n\t
\n\t\t\n\tDay 2 <\/h2>\t\t \n\tAdvanced Tensorflow and Computer Vision<\/p>\n\t\t\n\t\t\t\n\t <\/svg><\/i>\t\t<\/span>\n\t<\/button>\n\n\t\n\t\t
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\n\tAdvanced techniques in Tensorflow: Model APIs, Data Augmentations, Generators and Pipelines<\/li> Introduction to Deep Learning for computer vision\u00a0<\/li> Understand the function and implementation of convolutions in Deep Learning<\/li> Hands-on: Build your first Convolutional Neural Network (CNN) and apply transfer learning to boost model performance<\/li><\/ul><\/div>\n\t\t<\/div>\n\t<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n\n\n\t
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\n\tIntroduction to Deep Learning for sequential data and natural language<\/li> Deconstruct a Recurrent Neural Network (RNN)<\/li> The solution to vanishing and exploding gradients: GRUs and LSTMs<\/li> Word embeddings: \u00a0How to go from words to numbers<\/li> Attention is all you need: Transformers and generative language models<\/li> Hands-on: Build your own RNN, LSTM, or transformer!<\/li><\/ul><\/div>\n\t\t<\/div>\n\t<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div>\n\n<\/div>\n\t\t<\/div>\n\t<\/div>\n\n<\/div>\n\n<\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n\n\t\t\n\t\t\t
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\n\tThis training is for you, if:<\/h2><\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n\n\t\t\n\t\t\t
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\n\t <\/svg><\/i>\n\tYou want to understand Deep Learning from the ground up.<\/p><\/div>\n\n\n\n
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\n\t <\/svg><\/i>\n\tYou are looking for in-depth explanations rather than a top-level view.<\/p><\/div>\n\n\n\n
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\n\t <\/svg><\/i>\n\tYou work with unstructured data, such as text, images, video, audio, or complex time-series data and your Machine Learning models are hitting a wall.<\/p><\/div>\n\n\n\n
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\n\t <\/svg><\/i>\n\tYou already have a basic understanding of Machine Learning concepts and want to push your skills to the next level.<\/p><\/div>\n\n\t<\/div>\n<\/div>\n\t\t<\/div>\n\t<\/div>\n\n<\/div>\n\n\n\n