The Complete Self-Driving Car Course - Applied Deep Learning (Updated)

WEBRip | English | MP4 + Project files | 1280 x 720 | AVC ~1237 Kbps | 30 fpsAAC | 128 Kbps | 44.1 KHz | 2 channels | 18:04:51 | 9.49 GBGenre: Video TutorialLearn to use Deep Learning, Computer Vision and Machine Learning techniques to Build an Autonomous Car with Python



What you'll learnLearn to apply Computer Vision and Deep Learning techniques to build automotive-related algorithmsUnderstand, build and train Convolutional Neural Networks with KerasSimulate a fully functional Self-Driving Car with Convolutional Neural Networks and Computer VisionTrain a Deep Learning Model that can identify between 43 different Traffic SignsLearn to use essential Computer Vision techniques to identify lane lines on a roadLearn to build and train powerful Neural Networks with KerasUnderstand Neural Networks at the most fundamental perceptron-based levelSelf-driving cars, have rapidly become one of the most transformative technologies to emerge. Fuelled by Deep Learning algorithms, they are continuously driving our society forward, and creating new opportunities in the mobility sector. Deep Learning jobs command some of the highest salaries in the development world. This is the first, and only course which makes practical use of Deep Learning, and applies it to building a self-driving car, one of the most disruptive technologies in the world today.Learn & Master Deep Learning in this fun and exciting course with top instructor Rayan Slim. With over 28000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course.You'll go from beginner to Deep Learning expert and your instructor will complete each task with you step by step on screen.By the end of the course, you will have built a fully functional self-driving car fuelled entirely by Deep Learning. This powerful simulation will impress even the most senior developers and ensure you have hands on skills in neural networks that you can bring to any project or company.This course will show you how to:Use Computer Vision techniques via OpenCV to identify lane lines for a self-driving car.Learn to train a Perceptron-based Neural Network to classify between binary classes.Learn to train Convolutional Neural Networks to identify between various traffic signs.Train Deep Neural Networks to fit complex datasets.Master Keras, a power Neural Network library written in Python.Build and train a fully functional self driving car to drive on its own!No experience required. This course is designed to take students with no programming/mathematics experience to accomplished Deep Learning developers.This course also comes with all the source code and friendly support in the Q&A area.Who this course is for:Anyone with an interest in Deep Learning and Self Driving CarsAnyone (no matter the skill level) who wants to transition into the field of Artificial IntelligenceEntrepreneurs with an interest in working on some of the most cutting edge technologiesAll skill levels are welcome! GeneralComplete name : 4. Convolutions II.mp4Format : MPEG-4Format profile : Base MediaCodec ID : isom (isom/iso2/avc1/mp41)File size : 79.8 MiBDuration : 8 min 7 sOverall bit rate : 1 373 kb/sWriting application : Lavf58.12.100VideoID : 1Format : AVCFormat/Info : Advanced Video CodecFormat profile : [email protected]Format settings : CABAC / 4 Ref FramesFormat settings, CABAC : YesFormat settings, RefFrames : 4 framesFormat settings, GOP : M=4, N=60Codec ID : avc1Codec ID/Info : Advanced Video CodingDuration : 8 min 7 sBit rate : 1 237 kb/sNominal bit rate : 3 000 kb/sWidth : 1 280 pixelsHeight : 720 pixelsDisplay aspect ratio : 16:9Frame rate mode : ConstantFrame rate : 30.000 FPSColor space : YUVChroma subsampling : 4:2:0Bit depth : 8 bitsScan type : ProgressiveBits/(Pixel*Frame) : 0.045Stream size : 71.8 MiB (90%)Writing library : x264 core 148Encoding settings : cabac=1 / ref=3 / deblock=1:-1:-1 / analyse=0x1:0x111 / me=umh / subme=6 / psy=1 / psy_rd=1.00:0.15 / mixed_ref=1 / me_range=16 / chroma_me=1 / trellis=1 / 8x8dct=0 / cqm=0 / deadzone=21,11 / fast_pskip=1 / chroma_qp_offset=-3 / threads=22 / lookahead_threads=3 / sliced_threads=0 / nr=0 / decimate=1 / interlaced=0 / bluray_compat=0 / constrained_intra=0 / bframes=3 / b_pyramid=2 / b_adapt=1 / b_bias=0 / direct=1 / weightb=1 / open_gop=0 / weightp=2 / keyint=60 / keyint_min=6 / scenecut=0 / intra_refresh=0 / rc_lookahead=60 / rc=cbr / mbtree=1 / bitrate=3000 / ratetol=1.0 / qcomp=0.60 / qpmin=0 / qpmax=69 / qpstep=4 / vbv_maxrate=3000 / vbv_bufsize=6000 / nal_hrd=none / filler=0 / ip_ratio=1.40 / aq=1:1.00AudioID : 2Format : AACFormat/Info : Advanced Audio CodecFormat profile : LCCodec ID : mp4a-40-2Duration : 8 min 7 sBit rate mode : ConstantBit rate : 128 kb/sChannel(s) : 2 channelsChannel positions : Front: L RSampling rate : 44.1 kHzFrame rate : 43.066 FPS (1024 SPF)Compression mode : LossyStream size : 7.44 MiB (9%)Default : YesAlternate group : 1 Screenshots