What You Will Learn?
  • Build various deep learning agents (including DQN and A3C)
  • Apply a variety of advanced reinforcement learning algorithms to any problem
  • Q-Learning with Deep Neural Networks
  • Policy Gradient Methods with Neural Networks
  • Reinforcement Learning with RBF Networks
  • Use Convolutional Neural Networks with Deep Q-Learning
  • Build applications based on deep learning algorithms to detect and track objects using different algorithms
  • Classify text and images according to predefined categories and make use of neural networks, decision trees, random forests for classification
  • Use deep reinforcement learning to build an AI that plays arcade games
  • Learn the basics of deep learning and artificial neural networks to understand classification and probabilistic predictions with Single-hidden-layer neural networks

Follow a Structured Curriculum

  • The deep neural network architectures
  • Neurons
  • The neuron linear function
  • Neuron activation functions
  • The loss and cost functions in deep learning
  • The forward propagation process
  • The back propagation function
  • Stochastic and minibatch gradient descents
  • Optimization algorithms for deep learning
  • Using momentum with gradient descent
  • The RMSProp algorithm
  • The Adam optimizer
  • Deep learning frameworks
  • What is TensorFlow?
  • What is Keras?
  • Popular alternatives to TensorFlow
  • Building datasets for deep learning
  • The train, val, and test datasets
  • Managing bias and variance in deep neural networks

  • The Programming Model
  • Working with constants, variables and placeholders
  • Linear Regression using Tensorflow
  • Logistic Regression using Tensorflow
  • Tensorflow low level APIs
  • Data manipulation using Tensorflow
  • Data Manipulation using Keras
  • Data Model
  • Tensor Board
  • Introducing Feed Forward Neural Nets
  • Softmax Classifier
  • ReLU Classifier
  • Dropout Optimization
  • MNIST Classification using Deep Neural Network

  • CNN Architecture
  • Convolution and Relu
  • Pooling
  • Variants of the Basic Convolution Function
  • Efficient Convolution Algorithms
  • The Neuroscientific Basis for Convolutional Networks
  • MNIST Data set Analysis
  • CIFAR dataset Analysis
  • Do’s and Don’t while using CNNs
  • Evaluation of Deep Learning Model
  • Tuning Deep Learning Model
  • Dropout Optimization

Learn From the best

If you can take just one Python course, make sure it's this one.

I love the pace of the course so far. I'm new to Coding and AI and we are starting off very slow in order to get the hang of things. I can see as I go deeper in the course, the pace picking up, but by then I expect to know a lot of the smaller details of Python for AI. If you can take just one Python course, make sure it's this one.

Reynard Daffa, Software Engineer Freelancer

Artificial Intelligence Course Schedule

BootUP provides 4 entry levels for the Artificial Intelligence. Check the available levels and the schedule in the table below and choose your entry level. We provide some courses in other countries based on the available schedules and plans. We started the journey in India as Skill Speed, a well known and respected academy with thousands of graduates and students, mostly hired by our career partners. They provide BootUP Academy students with some venues in India. We also might have different venues in different countries based on our plans and availability of our courses.

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